Forem: John Doe

100+ YouTube Channels For Programmers (20+ Programming Languages Covered)

John Doe — Sun, 26 Mar 2023 13:32:10 +0000

In this article, I will share a huge list of top as well as new programming youtube channels you should follow in 2023. I've tried to cover various programming languages like Python, Java, JavaScript, PHP, Flutter, Kotlin, C, C++, C#, Swift, Ruby, Rust, Scala, Go, R and more. This list of youtube channel is not in top-to-bottom order. So, Surf the list, Join a youtube channel as per your preference and begin your 2023 coding journey.

According to Redditors, YCombinators, Quorans, Githubers, Mediumers, Linkedinians, Twitterians, Devians, and other web experts, the Best Programming YouTube Channels are

👉 Traversy Media : Traversy Media has videos that are mostly around full-stack development involving crash courses, projects, and more on JavaScript, CSS, HTML, Bootstrap, jQuery, Django, MERN stack, React, Vue, Python, MongoDB, NodeJs, ExpressJs, SASS, Restful API, PHP, Flux, Angular, GraphQL, Firestore, etc.

Link to join this Channel: Transversy Media

👉 Cory Schafer : Corey Schafer creates tutorials for programmers and developers, covering topics from beginner to advanced levels. Various topics such as Python, SQL, JavaScript, Git, as well as tips and tricks are covered to help you in your career.

Link to join this Channel: Cory Schafer

👉 The Coding Train by Daniel Shiffman : The channel is focused on coding for beginners. You’ll understand how to create simple games like Snake. Other videos include machine learning, simulation, JavaScript and more. His style of teaching is quite unique though, but not everybody may find it appealing. If you enjoy watching experienced developers making mistakes along the way and solving problems on the fly, then you will probably find this channel enjoyable.

Link to join this Channel: The Coding Train

👉 Joma Tech : Joma Tech, A Silicon Valley's least eligible bachelor features educational content about data science and software engineering in hopes to break the seemingly high barrier of breaking into the big tech companies. The channel also features an interesting series of narrative fiction called “Startup Series” that covers content around startups with a funny take. Particularly, if you're preparing for programming interviews then please don't forget to check a playlist named coding interview questions and answers on his channel.

Link to join this Channel: Joma Tech

👉 Computerphile : Computerphile is a great channel, but it’s mainly targeted towards junior developers or soon-to-be developers. His videos cover topics such as blockchain, bitcoin, game computer coding, automatic, code testing and more.

Link to join this Channel: ComputerPhile

👉 DevTips : In contrast to other tutorials (and maybe untrue web dev stereotypes), DevTips entertainment value is next-level. Travis brings his fun, high-energy personality to make the videos not only informative, but also stimulating. On top of that, Travis also posts some phenomenal web developer career and life advice for young web dev professionals and advocates.

Link to join this Channel: DevTips

👉 The Net Ninja : Net Ninja’s videos are some of the best videos on programming out there. You’ll find videos on topics such as JavaScript (beginner to advanced coding), Node.js, React, Vue.js, Firebase, MongoDB, HTML, and CSS. The guy who runs the channels is a skillful developer and all of his content is free. This channel is like a mini computer programming education school that can significantly improve your coding skills.

Link to join this Channel: The Net Ninja

👉 Free Code Camp : One of the best youtube channels for learning different programming languages for free. Freecodecamp offers best tutorials from different instructors for Python, CSS, React, Data Science, JavaScript and etc. Also their website is worth to visit as they provide amazing articles and different free and valuable courses like Coding Interview Preparation, JavaScript Algorithms and Data Structures, Responsive Web Design and more for free

Link to join this Channel: FreeCodeCamp

👉 Derek Banas : Derek Banas is truly the jack-of-all trades programmer. He has professional-level tutorials on almost all popular (and some less popular) programming languages. His channel is a good first place to look if you want to learn a programming language.

Link to join this Channel: Derek Banas

👉 Chris Coyier : The primary focus of Chris Coyier’s content has been CSS and HTML and both of those topics are covered in significant detail. Other topics like PHP and Sass have been covered as well both in YouTube videos as well as on the CSS-Tricks.com website. On YouTube, CSS-Tricks has more than 58,000 subscribers and there are 300+ videos available.

Link to join this Channel: Chris Coyier

A Directory Of 100+ Tools For Developers (Tools That Can Save Your 1000s Of Minutes)

John Doe — Sun, 26 Mar 2023 13:22:59 +0000

In this 2 years, the world has experienced an unpredictable change. The global pandemic required us to shift the way we live and work in daily lives, blurring the boundaries as many of us shifted to working remotely from different places. After doctors, Developers are the most valuable people in the world. So, To make their work and life easy and flexible, I've curated and created a mixed version of some new programming tools a coder need in 2023 plus few must have productivity tools that can save thousands of minutes of a programmer plus essential software development & software engineering tools and a bonus of some of the best developer tools.

100+ Developer Tools Are

👉 Git

Git is a version control system meant to track changes in computer files, and it’s mainly used in source code management.

Each member of the developer team gets a local copy of the development history, and the system documents and copies all changes across repositories. It operates locally, and one of its main draws is its speed. The Git feature that stands out the most is its branching model; you can use multiple branches for your ideas, test them, and easily backtrack if they don’t work.

Check out Git

👉 Atom

This free and open source text editor can be used with Git and GitHub directly. After suffering a steady decline since taking home the silver in 2017, this tool is finally recovering its position, up one spot from last year.

Check out Atom

👉 Chrome DevTools

Chrome Dev Tools is a lifesaver for Developers. You can use it for many things such as Performance monitoring, inspecting elements, and more.

Check out Chrome Dev Tools

👉 The Silver Searcher

The Silver Searcher (Ag) is basically a smart, recursive grep on steroids. While fzf (above) is great for searching file names, Ag is great for searching the content inside those files for specific tokens and matches.

What makes it really great and awesome is it automatically ignores files that are part of your .gitignore file. Where a grep -r would have spat out a bunch of irrelevant matches inside your node_modules , Ag has a blazing-fast search time because it’s only searching the files that matter. By default, Ag output results also give you a line number. You technically could achieve everything Ag does by supplying lots and lots of arguments to grep , but then, at the end of the day, Ag is still several times faster — and continues to get even faster.

Check out The Silver Searcher

👉 Oh My Zsh

Oh My Zsh is a wrapper for Z shell that makes your terminal a delightful tool to work with. On top of various themes, convenience functions, and built-in defaults, you enjoy various plugins for extended functionality, including Docker, Kubernetes Kubectl, and tmux, among other things.

Check out Oh My Zsh

👉 Postman

This collaboration tool helps simplify and expedite API development. Postman has skyrocketed from ranking as the 13th tool in the Top Dev Tools for 2017 to the 3rd best developer tool in 2020.

Check out Postman

👉 Visual Studio Code

VS Code is a sleek code editor that supports debugging, version control, and task running, giving devs the tools they need for a quick code-build-debug workflow.

This tool has taken the spot for four years in a row! As Microsoft continues to improve this lightweight development environment, we only expect usage to grow.

Check out VS Code

👉 Sizzy

The browser for developers. Stop wasting time and speed up your development workflow using sizzy.

Check out Sizzy

👉 Repl.it

Online code editors such as Codesandbox or Codepen are limited to the frontend. ReplIt is more than 50 languages that can be used in the browser for free. The perfect place to prototype bits of code backends quickly. With a free account you can even share your algos!

Check out Repl

👉 Vim

Vim is hardcore. Let’s be clear about that upfront. Where Sublime Text, Notepad++, and some of the other editors on this list are newbie friendly, Vim is unapologetic in its utilitarian brutalism. From the official website down to the software itself, Vim is a coder’s tool for coders by coders.

Once you get past the UI and the steep learning curve, you’ll see why the text editor is designed the way it is. It does roughly everything all the other best text editors can do. But more than maybe anything else on the list, it’s designed for efficiency and function. Watching people code in Vim is like watching a concert pianist. The UI is keystroke based, and if you thought the Sublime Text keyboard shortcuts were helpful, when and if you can get used to Vim, the experience is unreal. If you try to go back to a visual GUI for a text editor, you will feel like you’re working in slow motion.

Because Vim can be so intense to learn, they created a game for you to play to learn how it works. Not a tutorial, really, but a way not to overwhelm you with everything that makes Vim, Vim. It’s pretty fun.

Check out Vim

Recommendation Algorithms And System Designs Of Dropbox, Spotify, Airbnb, YouTube, Netflix And Uber

John Doe — Sun, 26 Mar 2023 13:15:53 +0000

In this post, you will find Recommendation Algorithms of tech giants like Spotify, YouTube, Signal Messenger, Amazon, etc. and System Designs of companies like Uber, Twitter, Netflix, Airbnb, Dropbox, Google Docs, Zoom, etc. One more thing we want to share before we jump into the article i.e. thanks for your so much love all on our previous articles.

1. Uber System Design

Uber’s technology may look straightforward but the game starts behind the scenes when A user requests a ride from the app, and a driver arrives to take them to their destination.

Uber's infrastructure consisting of thousands of services and terabytes of data supports each and every trip on the platform.

The system was predominantly written in Python and used SQLAlchemy as the ORM-layer to the database. The actual architecture was fine for running a relatively modest number of trips in a few cities.

The company's backend is now not just designed to handle taxies, instead, it can handle taxi, food delivery, cargo and a lot more.

2. YouTube Video Recommendation Algorithm

When users are spotting the videos on YouTube, a list of recommended videos are thrown below or on the right side of your device which the user might like in a certain order.

The described model in the paper focuses on the two main objectives. A Wide & Deep model architecture was used which combines the power of a wide model linear model (memorization) alongside a deep neural network (generalizations). The Wide & Deep model will generate a prediction for each of the defined (both engagement and satisfaction) objectives.

3. Spotify Music Recommendation Algorithm by Harvard Business School

How Spotify uses Machine learning to recommend the music?

The company uses three recommendation models concurrently and other techniques. Three models as described by Harvard Business School are

CF aka Collaborative Filtering: Models that analyze your behavior and compare it to other users’ behaviors
NLP aka Natural Language Processing Models - that scan the internet and analyze text about Spotify’s catalog
Audio Analysis: Models that analyze the raw audio files

4. Netflix System Design

Netflix operates in two clouds: AWS and Open Connect. Both clouds must work together without error to deliver endless hours of customer-pleasing video.

Three main component that plays a major role: CDN, Backend and Client

Anything that doesn’t involve serving video is handled in AWS. Everything that happens after you hit play is handled by Open Connect. Open Connect is Netflix’s custom global content delivery network (CDN).

5. TikTok's ML backed Recommendation Engine

The Archetype of TikTok’s Recommendation System Design is User-Centric Design. In a simple term, TikTok will only recommend the content you would love, from a cold start adjustment to an explicit recommendation for active users.

TikTok never reveals its core algorithm to the public or the tech community. But based on the fragmented information posted via the company, and trails discovered by geeks using the reverse engineer techniques this recommendation workflow is made.

Let's divide this workflow in various parts

Step 0: Duo-Audit system for the User Generated Content (UGC)

Step 1: Cold Start

Step 2: Metric based Weighing

Step 3: User Profile Amplifier

Step 4: Boutique Trending Pool

Other Step: Delayed Ignition

100+ Cheat Sheets For Machine Learning, Data Science, Deep Learning, AI And Python

John Doe — Sun, 26 Mar 2023 13:13:09 +0000

Today, I'm gonna share something very big which you might have never seen or rarely seen on the web. I have researched for more than 35 days to find out all the cheatsheets on machine learning, deep learning, data mining, neural networks, big data, artificial intelligence, python, Tensorflow, scikit-learn, etc from all over the web. To make it easy for all users, I have zipped over 100+ Cheat sheets and shared it in this post. If you find any cheat sheet missing, please share it in the comment section.

All Cheats In High Quality With PDF Downloadable Link: 100+ Cheat Sheet For Data Science And Machine Learning

Let's dive into cheat sheets very quickly...

Bias and Variance in Machine Learning Models

Machine Learning Algorithms Summary

Imbalanced data in Machine Learning

Big-O Algorithm Cheat Sheet by Big-O-Cheatsheet

Bayes Theorem

Numpy Cheat Sheet for Data Science

Basics of Convolutional Neural Network

Slackr: Send Files, Messages, R Objects, And Images To Slack Directly From R

Ensemble Methods in Machine Learning

Machine Learning Algorithms

Deep Learning Concepts Explained

Deep Learning and Big Data Concepts Explained

Python for Data Science Cheat Sheet

A Complete Chart of NEURAL NETWORKS

Data Transformation With Dplyr CheatSheet

Scikit-Learn Algorithm Cheat Sheet

What is Data Science? Types of Data, Probability and Stats Cheats

Data Science with Python Workflow

Special Topics in Data Science with Python

Python Data Science Cheat Sheet

Numpy Cheat Sheets for Data Science

Scipy - Linear Algebra Cheat Sheet

Principal Component Analysis and Dimensionality Reduction

Segmentation and Clustering Cheats (Python & R)

Other images are very large in dimensions and when I uploaded it here it becomes blur. So, its a request to all to check other core cheatsheets and DOWNLOADABLE PDF links for all cheat sheets from the below given link.

Check out other 80+ Cheat Sheets from here: Machine Learning Algorithms Python and R Codes Cheatsheet, Data Analysis in Python using NumPy, Pandas, and Matplotlib Cheatsheet, Data Exploration using Pandas in Python Cheat Sheet, Data Exploration in R & More

100+ Machine Learning Algorithms in One Pic

John Doe — Sun, 26 Mar 2023 12:41:24 +0000

In this post, I am going to share something very helpful for all machine learners or data science enthusiast. In this post, you'll find 100+ Machine Learning Algorithms in One Pic - A 2023 Tour for Machine Learners, Data Science Enthusiast and others who are interested in algorithms. This article will cover all the types of machine learning algorithms that you should know in 2023.

Download HQ PNG: 100+ Machine Learning Algorithms

Download PDF: 100+ Machine Learning Algorithms

Let's take a deep dive into types of machine learning algorithms

Machine learning Approaches and algorithms are generally classified into four broad categories: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning and Reinforcement Learning

Supervised learning: Supervised learning is a type of ML where the model is provided with labeled input data and the expected output results. The AI system is specifically told what to look for, thus the model is trained until it can detect the underlying patterns and relationships, enabling it to yield good results when presented with never-before-seen data

For example, every shoe is labelled as a shoe and the same for the socks so that the system knows the labels, and when subjected to a new type of shoes, it will identify it as ‘shoes’ without being explicitly programmed to do so.

Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. It's like learning on their own partially similar humans. In unsupervised learning, the goal is to identify meaningful patterns in the data. To accomplish this, the machine must learn from an unlabeled data set. In other words, the model has no hints how to categorize each piece of data and must infer its own rules for doing so.

Taking the same example which was discussed in supervised learning, here, the labels that are socks and shoes will be unknown to the interpreter, but the system will know that both are of different categories and places them accordingly.

Reinforcement learning: Reinforcement learning differs from other types of machine learning. In RL, A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). During training, the agent receives a reward when it performs this task, which is called a reward function. With reinforcement learning, the agent can learn very quickly how to outperform humans.

For Example, Consider the scenario of teaching new tricks to your pet (Let's consider it as your dog)

As dog doesn't understand English or any other human language, we can't tell him directly what to do. Instead, we follow a different strategy. We emulate a situation, and the dog tries to respond that situation in various ways. If the dog's response is the desired way, we will give him his tasty food. Now whenever the dog is exposed to that same situation, the dog executes a similar action with even more enthusiastically in expectation of getting more reward (food). This is one of the best example of reinforcement learning

Semi-Supervised Learning: Semi-supervised learning is a type of machine learning in which the algorithm is trained upon a combination of both labeled as well as unlabeled data during the training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data).

For example, Imagine you want to classify content available on internet like google does, then labeling each webpage is an almost an impossible process and thus you need to use Semi-Supervised learning algorithms and other methods to do so. According to one blog published by Google AI, they have mentioned that Google search algorithm uses a variant of Semi-Supervised learning to rank the relevance of a webpage for a given query.

YouTube Channels To Follow For Artificial Intelligence, Machine Learning And Data Science in 2023

John Doe — Sun, 26 Mar 2023 12:34:47 +0000

In this post, I am going to share some of the best AI, ML, Deep Learning and Data Science youtube channels that can help you in learning everything from Programming languages (Python or R) to Maths (Linear Algebra, Calculus, Probability, Statistics) to Basic Fundamentals (Intro to AI, ML and DS) to Intermediate & Advanced Concepts.

YouTube Channels for AI, ML, Data Science & Python Enthusiast are

👉 Yannic Kilcher

Yannic Kilcher focuses on explaining deep learning research papers. He introduces the paper and describes the novelty behind it. He reads the paper meticulously to you, and he tells it like a story. He covers topics on deep learning architectures, natural language processing and reinforcement learning. The level of details is incredible. Yannic breaks up a dense paper into parts and uncovers each idea paragraph by paragraph. He explains and scribbles on the screen to walk you through the thought process. When there are concepts not covered by the paper, he references additional resources to reveal any concepts outside of the paper.

👉 Two Minutes Papers

Two Minute Papers is an awesome channel for anyone who loves to be updated with the latest research going on in the Machine Learning domain. Two Minute Papers make 2 minutes (almost) long videos explaining a research paper. If you are keen into research field then you may want to check out the following series

👉 Lex Fridman

Lex Fridman Podcast is one of the most popular and best machine learning YouTube channels. Its host is an AI researcher working on autonomous vehicles, human-robot interaction, and machine learning at MIT and beyond. Lex talks with his guests on everything related to AI and ML. But he’s not limiting himself only to this theme. He talks about other things that can inspire, teach, and push you to exceed your limitations. Insights from all the superstars, influencers, and leading scientists from the world of machine learning. He interviewed such personas like Elon Musk, Nick Bostrom, Andrew Ng, Yann LeCun, Vladimir Vapnik, Matt Botvinick and many more.

👉 Alexander Amini

This channel is for the Deep Learning geeks or for the one who wants to start with Deep Learning and understand it in few weeks, at least that is what the professors are claiming.

The channel offers Deep Learning material from an MIT University’s course, packed in 42 videos. It is the best course or one of the best courses out there. The materials start to form the basics and work it way up to how to use it in real-world projects using Reinforcement Learning techniques, NVIDIA, IBM, Google’s software and more.

👉 Abhishek Thakur

Abhishek Thakur is a chief data scientist, world’s first “four times Kaggle grandmaster” . He completely focuses on applied machine learning, deep learning, and data science. He is a great problem solver. His videos on YouTube are just pure magic and it will help you solve problems like a pro. He has also recently published a book on “How to Approach any Machine Learning Problem” which is an absolute masterclass.

👉 Kaggle

Last by not least, Kaggle, Your Home for Data Science. If Kaggle is where you do and learn, then the Kaggle YouTube channel is where you watch and learn. There are “how-tos” coding tutorials on deep learning, building dashboards, and data processing. They have live coding sessions on how to solve data science problems.

For updates on the latest NLP research, there is a playlist, Kaggle Reading Group. She reads and discusses NLP research papers. She will explain every single paragraph, diagrams, code, mathematics equations to you. If you are a new researcher or new to academic papers, watch these videos, and you will learn how to dissect papers.

👉 Sentdex

If you are someone who likes to understand everything from scratch then this is by the far the best YouTube channel to learn about Machine Learning. Harrison Kinsley who is the owner of the YouTube channel Sentdex educates people about various technologies included Python Programming, Web-development, Machine Learning, etc. If you are keen to learn every algorithm’s workflow like how does bias and intercept get updated at every epoch, or how to implement a given machine learning algorithm from scratch then you must check the following series made by Harrison Kinsley himself.

👉 StatQuest with Josh Starmer

Statistics and data analysis are a lot easier than most people think. Because computers do the math for you, the most important thing is to understand the concepts and main ideas, and which are made pretty simple by the host Josh Starmer, who makes learning statistics fun. The videos are short, informative and make a great source for those who are starting out in the field of data science, and also to those who need to have a quick look at concepts before an interview.

👉 TensorFlow

This channel is one of the best out there. There are tons of video tutorials explained by the best of the best engineers, the ones who developed TensorFlow. It has tons of videos of conferences where many other engineers, professors, and researchers present their projects. If you are interested in Deep Learning, this is the channel for you. One of the best playlist on their channel is Machine Learning - Zero to Hero by Laurence Moroney.

👉 Jeremy Howard

Jeremy Howard is a data scientist who has an educational background in philosophy but later out of the curiosity he harnessed himself with the knowledge of stats and programming to build the most effective and easy-to-use library for deep learning tasks fastai. Making deep learning models was never that easy before fast.ai came into the picture. If you are someone who wants to build deep learning models that complete the task in the field of computer vision like image segmentation, image classification, image restoration with the minimal coding, and maximum results then the fast.ai is suitable for you. You might wanna binge watch the following series to get a good grasp of deep learning with the help of the fast.ai library.

👉 Preserve Knowledge

Preserve Knowledge shares selective presentation from conferences such as NeurIPS and interviews with machine learning legends like Geoffrey Hinton and Yann LeCun. Preserve Knowledge covers videos on advances in mathematics, computer science, and artificial intelligence. Its goal is to bring together the world's leading researchers and students in computer science and related fields.

👉 Corey Schafer

Alternative of Sentdex and freecodecamp for learning python for machine learning. On his channel, you will find a huge library of information on Python, Git, development environments, terminal commands, SQL, programming terms, JavaScript, computer science fundamentals, and plenty of other tips and tricks that will help you in your ai or ML career.

👉 DataSchool

Kevin Markham who is the founder of dataschool.io and the owner of the YouTube channel Data School educates machine learning enthusiasts. You can get a comprehensive understanding of machine learning regardless of your educational background thanks to Kevin‘s teaching. Kevin also makes videos that cover several tools like pandas, NumPy, scikit-learn that will help you build your machine learning models. You can binge-watch the following series created by Kevin to get a good grasp of the machine learning fundamentals.

👉 Stanford Online

Stanford University has been at the forefront of innovation for nearly a century. The school has a tradition of pursuing multidisciplinary collaboration aimed at solving the most pressing global problems. On their channel, you will find playlist like Stastics and Data cScience, Deep Multi Task and Meta Learning, Machine Learning with Graphs, Deep Learning and Natural Language Processing and more.

👉 Steve Brunton

Steven Brunton is an associate professor of mechanical engineering and applied mathematics and a data science fellow at the University of Washington in Seattle. To learn all the fundamentals of data science and machine learning, Check out his well known playlist "Intro to Data Science". In addition to this, he has also covered linear algebra used in data science and zipped it in the playlist.

👉 Chai Time Data Science

Led by Sanyam Bhutani, a 2x Kaggle Grandmaster and Machine Learning Engineer at H2O.ai, this channel is must follow after Lex Fridman youtube channel. On his channel, you will find videos like "Learning Data Science - Journey to becoming Kaggle Grandmaster", "Computer Vision & Research at the Dynamic Vision and Learning Group", "Journey to becoming Kaggle Kernels Grandmaster", "The Numerai Story - Building the largest hedge fund" and more.

👉 Computer Science Crash Course

As you can guess from the name, Crash Course is a YouTube channel which consists of different kinds of series which cover important topics related to several fields in an intense manner. These topics include but not limited to sociology, computer science and statistics. The Computer Science series also have videos about Computer Vision and Machine Learning & Artificial Intelligence.

👉 FreeCodeCamp

FreeCodeCamp is an incredible non-profit organization. It is an open-source community that offers a collection of resources that helps people learn to code for free and create their projects. In addition to this, you can also find playlist related to machine learning and data science like Deep Learning with Pytorch, Data Analysis with Python, Data Science 10Hrs Small Course, Videos on Tensorflow, Python and more.

👉 3Blue1Brown

Mathematics will play a major role in your path to AI or ML or Data Science. If math was not your forte in school than 3 Blue 1 Brown is for you. With its engaging visuals, the channel’s creator works hard to convey complex mathematical topics in the simplest ways for viewers. Topics like probability and statistics will be crucial to your understanding of AI, ML and Data Science.

👉 Daniel Bourke

Going back to school to learn anything is expensive. Nevertheless, the resourceful YouTuber, created his own at home AI curriculum, using the excellent resources available online, and began his path towards AI mastery. For those looking to make the career switch into AI, Daniel Bourke’s YouTube channel is a great source of inspiration and a healthy reminder that it is possible. His energy is infectious and great place for those looking to forge their own path towards a career in artificial intelligence.

👉 Data Professor

Data Professor is a YouTube channel of Chanin Nantasenamat. He has done a Ph.D. and is an Associate Professor of Bioinformatics at a Research University where he leads a research laboratory that harnesses data science for unraveling the hidden knowledge of big data in medicine. Here you will get Data Science contents consisting of explainer videos and practical tutorials. The main aim of the channel is to help students and data enthusiasts learn about Data Science and how it can be used to make sense of data.

👉 DeepMind

This is also one of the best channel where you can learn about AI. Artificial intelligence could be one of humanity's most useful inventions. DeepMind aims to build advanced AI to expand their knowledge and find new answers. By solving this one thing, they believe they could help people solve thousands of problems. Some of the best playlist on their youtube channel are Deep Learning Lecture Series, Reinforcement Learning Course and more.

👉 Brandon Rohrer

Brandon Rohrer owns one of the greatest interactive tutorial channels on YouTube. He is a data scientist at Facebook and is clearly aware of what he is talking about. He cuts down on the jargon surrounding data science and conveys the concepts with intuitive real-world examples. If you had to watch one channel to learn and know the language of a data scientist, then it should be this one.

👉 Andreas Kretz

Andreas Kretz is a data engineer and founder of Plumbers of Data Science. He broadcasts live tutorials on his channel on how to get hands-on experience in data engineering and videos with questions and answers about data engineering with Hadoop, Kafka, Spark, and so on.

👉 MITCBMM

CBMM aims to create a new field — the Science and Engineering of Intelligence — by bringing together computer scientists, cognitive scientists, and neuroscientists to work in close collaboration. This new field is dedicated to developing a computationally based understanding of human intelligence and establishing an engineering practice based on that understanding.

👉 Jordan Harrod

Jordan Harrod is a graduate student at Harvard and MIT researching brain-machine interfaces and machine learning for medicine. She has always fascinated by the way AI is being integrated into our lives, from social media to the military. In her channel, she explores the way we humans interact with Ai and algorithms, and their uses in our everyday life. Jordan is great at conveying complex topics such as AI to the mass audience and makes it easier to understand. Check out her playlist on AI!

👉 Weights & Biases

Weights & Biases is a company that develops tools for Machine Learning. On their channel you can find presentations by important people in AI and related fields. The topics on their channel include: Machine Learning, Kaggle data science competitions, NLP, Autonomous vehicles, Deep (Reinforcement) Learning, Hyperparameter tuning, the TensorFlow software library, Github platform, Timeseries forecasting, and much more…

👉 Codebasics

The goal of this channel is to fulfill the vision by teaching the programming in most simplest and intuitive manner. Dhaval teaches simple programming, data science, data analytics, artificial intelligence, machine learning, data structures, software architecture etc on his channel.”

👉 MLConf

MLConf is created to host the thought leaders in Machine Learning and Data Science to discuss their most recent experience with applying techniques, tools, algorithms and methodologies to the seemingly impossible problems that occur when dealing with massive and noisy data.

Check out other remaining 20+ Youtube Channels from here: Remaining Youtube Channels

Top 20 Free Machine Learning, Data Science And Python Books

John Doe — Wed, 18 May 2022 13:36:46 +0000

Hi Dev Community. In this post, You'll see some of the best free Machine Learning, Deep Learning, Python, & Data Science Books. You can read this book on authors website or download PDF for free.

Note: All the eBooks are open source. If you still have any doubts on sources, please comment below

1. Machine Learning for Humans

Authors: Vishal Maini and Samer Sabri
Who should read this?

a. Technical people who want to get up to speed on machine learning quickly

b. Non-technical people who want a primer on machine learning and are willing to engage with technical concepts

c. Anyone who is curious about how machines think

This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn’t necessary to have prior knowledge of them to gain value from this series.

In short, this book contains simple, plain-English explanations accompanied by math, code, and real-world examples.

Download link: Machine Learning for Humans PDF

2. Approaching (Almost) Any Machine Learning Problem

Author: Abhisek Thakur
About this Special eBook: This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.

Download link: Approaching (Almost) Any Machine Learning Problem PDF

3. Machine Learning Engineering

Author: Andriy Burkov
What experts says about this book: The most comprehensive book on the engineering aspects of building reliable AI systems.

a. "If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." - Cassie Kozyrkov, Chief Decision Scientist at Google

b. "Foundational work about the reality of building machine learning models in production." - Karolis Urbonas, Head of Machine Learning and Science at Amazon.

Read here: Machine Learning Engineering eBook

4. Understanding Machine Learning: From Theory to Algorithms PDF

Book by Shai Ben-David and Shai Shalev-Shwartz
Aim of this Textbook: To provide Introduction to ML & Algorithmic paradigms

Download Link: Understanding Machine Learning PDF

5. Neural Networks and Deep Learning PDF

Book by Michael Nielsen
What you will Learn: You'll learn the concepts behind neural networks and deep learning.

Download Link: Neural Networks and Deep Learning PDF

6. The Elements of Statistical Learning: Data Mining, Inference, and Prediction PDF

Book by Trevor Hastie, Robert Tibshirani, & Jerome Friedman
Some of the topics inside this Book: Overview of Supervised Learning, Linear Methods for Regression, Basis Expansions and Regularization, Neural Networks, Support Vector Machines and Flexible Discriminants, Unsupervised Learning, Random Forests, Ensemble Learning, and more

Download Link: Elements of Statistical Learning PDF

7. Natural Language Processing with Python

Book by Steven Bird
What this Book offers: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text & email filtering to automatic summarization & translation. With it, you'll study how to write Python programs that work with long collections of unstructured text.

Read Here: Natural Language Processing with Python eBook

8. Reinforcement Learning: An Introduction

Book by Richard S. Sutton & Andrew G. Barto
Focus of this Book: This book focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

Download Link: Reinforcement Learning: An Introduction PDF

9. Computer Vision: Algorithms and Applications

Book by Richard Szeliski
This book also reflects my 20 years’ experience doing computer vision research in corporate research labs […] I have mostly focused on problems and solution techniques (algorithms) that have practical real-world applications and that work well in practice. Thus, this book has more emphasis on basic techniques that work under real-world conditions and less on more esoteric mathematics that has intrinsic elegance but less practical applicability.

Download link: Computer Vision: Algorithms and Applications

10. Mathematics for Machine Learning

Author: A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth
About This Special eBook: If you ever need a place to start learning about the maths behind machine learning, then this a highly recommended book. This book provides great coverage of all the basic mathematical concepts for machine learning.

Download link: Mathematics for Machine Learning PDF

11. Pattern Recognition and Machine Learning eBook

Author: Christopher M. Bishop
About This Special eBook: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Download link: Pattern Recognition and Machine Learning PDF

12. Think Stats: Exploratory Data Analysis in Python

Book by Allen B. Downey
About this eBook: If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

Download link: Think Stats: Exploratory Data Analysis in Python PDF

13. An Introduction to Statistical Learning with Applications in R

Book by Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani
Some of the topics inside this Book: T0opics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, and much more.

Download link: An Introduction to Statistical Learning with Applications in R PDF

14. Python Data Science Handbook

Author: Jake VanderPlas
What's Special about this eBook:

With this handbook, you’ll learn how to use:

Python and Jupyter: provide computational environments for data scientists using Python
NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
Matplotlib: includes capabilities for a flexible range of data visualizations in Python
Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Read Here: Python Data Science Handbook eBook

15. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Author: Garrett Grolemund and Hadley Wickham
What's Special about this eBook: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.

Read Here: R for Data Science eBook

16. Text Mining with R: A Tidy Approach

Author: David Robinson and Julia Silge
What's Special about this eBook: With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.

The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.

Read Here: Text Mining with
R eBook

17. Think Python: How to Think Like a Computer Scientist

Author: Allen B. Downey
What's Special about this eBook:

Start with the basics, including language syntax and semantics
Get a clear definition of each programming concept
Learn about values, variables, statements, functions, and data structures in a logical progression
Discover how to work with files and databases
Understand objects, methods, and object-oriented programming
Use debugging techniques to fix syntax, runtime, and semantic errors
Explore interface design, data structures, and GUI-based programs through case studies

Download link: Think Python: How to Think Like a Computer Scientist PDF

18. Gaussian Processes for Machine Learning

Author: Carl Edward Rasmussen & Christopher K. I. Williams
About This Special eBook: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning.

Download link: Gaussian Processes for Machine Learning PDF

19. The Data Science Handbook

Author: William Chen, Henry Wang, Carl Shan, Max Song
What's Special about this eBook:

The Data Science Handbook contains candid interviews with 25 of the world’s best data scientists.

This book contains insight and interviews with data scientists from established companies such as Facebook, LinkedIn, Pandora, Intuit, and The New York Times.

We also spoke with data scientists at fast-growing startups such as Uber, Airbnb, Mattermark, Quora, Square and Khan Academy

In The Data Science Handbook, You’ll learn from industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You’ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program.

Read Here: The Data Science Handbook eBook

20. R Programming for Data Science

Book by Roger D. Peng
About this Book: This book is about the fundamentals of R programming. You will get incited with the basics of the R programming language, learn how to write functions, how to manipulate datasets, & how to debug and optimize code.

Download link: R Programming for Data Science PDF

30+ Free Web3 Resources (Recommended By Experts)

John Doe — Sat, 07 May 2022 08:50:10 +0000

Hi Web3 Developers, Professionals, and Enthusiasts. We have something more valuable pieces of stuff to share with you. In this article, we've curated some of the interesting websites and best resources that can help you to learn, interact, earn, share and keep yourself updated with Web3. If you have been hearing about Web3 or already know a lot about it, we've valuable web3 websites for all. So, without a bit more conversation, Let's dive straight into this collection of free web3 resources.

1. Blockchain Demo - A Visual Demo

Blockchain Demo is an attempt at demystifying the technology behind cryptocurrencies. It has a living blockchain, a peer-to-peer network, and a user tour. Don't miss this website, just visit it once!

2. Ethereum Development Tutorials (Official Ethereum Website)

Ethereum Community members and enthusiast shares expert's written guides and articles related to Blockchain, Alchemy, Defi, Aave, NFT, Metamask, Hardhat, Solidity, Truffle, The Graph, Web3js, Web3py, etc. It's an official Ethereum website and on of the best source where you can learn from professional related to Ethereum and Web3.

3. Eth Build - Learn Ethereum Visually

An Educational Sandbox for Web3 Developers and Enthusiasts! Use the open-source building blocks to get a visual perspective of how Ethereum and other stuff works. In this tool, You can even drag and drop the blocks to build your own smart contract. Must recommended website!

4. WTF is Ethereum?

Are you a 5 years old? Are you an average person trying to learn about Ethereum? A podcasts lover? An entrepreneur? A video lover? Whoever you're, this website is for you and for all those who wants to learn about Ethereum

5. Odyssey - Web3 For Beginners

Odyssey is a learning DAO on a mission to onboard the next 1 million people to web3. They've written quality ELI5 guides for intro to web3, DeFi, DAOs, NFTs, and more. We must recommend you to check this website as it is one of the best resource that can help you to learn Web3 in a very simple manner.

6. NFTSchool - Learn And Share About NFTs

What's going on with non-fungible tokens, or NFTs? If you're the one excited and wants to know, learn and keep yourself updated with NFTs, then this website is for you. NFT School is an open-source project that welcomes contributors of all specialties from all over the world! You can share your knowledge with your peers and contribute a tutorial, how-to, or concept guide to this open-source NFT site.

7. Solidity by Example

An introduction to Solidity with simple examples. It's the best resource to learn Solidity from the whole list. Learning to read code is more important than writing code. Must recommended website for all who wants to learn Solidity.

8. Metaschool - Learn Web3 Development

Metaschool is a platform where developers hangout to learn web3 with each other, collaborate on projects, earn NFTs for completing their work, and connect with talented folks building the future of the web!

9. Crypto Zombies - Learn To Code Ethereum DApps By Building Your Own Game

CryptoZombies is an interactive code school that teaches you to write smart contracts in Solidity through building your own crypto-collectables game. The course is beginners friendly and is available for free.

10. Web3 is Going Great - Web3 News That Matters

Web3 Is Going Great is a project to track some examples of how web3 isn't actually going as well as its proponents might like you to believe. The timeline tracks events in cryptocurrency and blockchain-based technologies, dating back to the beginning of 2022. So many websites won't show you some realities related to Web3 but this website will.

11. Buildspace - Web3 Projects

Learn Web3 by building some of the best web3 projects. Some of the projects available on Buildspace are

Build a Web3 App with Solidity + Ethereum Smart Contracts
Mint your own NFT collection and ship a Web3 app to show them off
Create your own mini turn-based NFT browser game
Build a Web3 app on Solana with React and Rust
Build your own DAO with just Javascript in a weekend
And More.

12. Coinbase - Earn Crypto While Learning About Crypto

Discover how specific cryptocurrencies work and get a bit of each crypto to try out for yourself. You can watch tutorials, then You’ll receive a simple quiz testing what you’ve learned. You can earn crypto for every quiz you complete.

13. UseWeb3 - Best Web3 Resources

UseWeb3 is a platform for developers to explore and learn about Web3. Whether you're a new dev getting your hands dirty for the first time, or a seasoned developer making the transition into the Web3 space. On this website, You'll explore one of the best web3 resources including movies, Podcasts, Videos, Tutorials, Jobs, Courses and a lot more related to Web3.

14. Web3 University

A small university for web3 enthusiasts. It covers the fundamentals of web3 development: from writing Solidity, to minting NFTs, to building full-stack dApps. Web3 University has partnered with Alchemy, Buildspace, OpenSea, Polygon, Chainshot and other big platforms.

15. Web3 Predictions - Best Twitter Threads On Web3

A Curated Collection of 50+ Twitter threads to get ahead on NFTs, DAOs, DeFi, P2E, BTC, ETH, L1s, L2s, etc.

That's it. You can check the remaining 15+ Web3 Resources From here: 15+ Best Free Web3 Resources (Recommended by Experts) [Don't Miss It!]

300+ Free Programming Books (Updated For 2022)

John Doe — Thu, 05 May 2022 18:54:48 +0000

Hi Everyone, I hope you all are very well. In this post we'll go through 300+ Coding Books For 20+ Programming Languages. Some of the programming languages covered here are javascript, python, java, c, c++, c#, php, kotlin, swift, Golang, Typescript, Perl, Lisp, Rust, Ruby, Scala, Haskell, Objective-C, Julia and more.

Note: These eBooks are listed according to the publishing year/book updated year (in descending order). And all the eBooks listed below are open-sourced.

Starting with JavaScript

Free JavaScript Books

JavaScript Wikibook (2022)
JavaScript for Impatient Programmers (ES2022 edition) (2022)
The Modern JavaScript (2022)
JavaScript Enlightenment (2021)
Notes on Douglas Crockford's Javascript the Good Parts (2021)
Clean Code JavaScript (2021)

Free Python Books

Cosmic Python (2022)
Python for You and Me PDF (2022)
Clean Architectures in Python - A Practical Approach to Better Software Design PDF (2022)
Non-Programmer's Tutorial for Python 3 (2021)
Learn Python the Right Way (2021)
Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners (2020)

Free Java Books

Google’s Java Style Guide (2022)
Introduction to Programming Using Java (2021)
Java Programming Wikibook PDF (2021)
The Java Tutorials (2021)
Javaparser: Visited - Analyse, Transform And Generate Your Java Code Base PDF (2021)
Think Java, 2nd Edition PDF (2019)

Free C++ Books

Learn C++ (2022)
C++ Annotations (2021)
Practical Guide to Bare Metal C++ (2021)
C++ Notes for Professionals PDF (2018)
Fundamentals of Programming C++ (2018)

Free PHP Books

PHP Programming Wikibook (2022)
PHP Internals (2022)
Clean Code PHP (2022)
PHP The Right Way (2022)
PHP Best Practices: A Guide for Common and Confusing PHP Tasks (2021)

Free Ruby And Ruby on Rails Books

A Community-driven Ruby Style Guide (2022)
Ruby on Rails Tutorial: Learn Web Development with Rails (2022)
Ruby Wikibook PDF (2021)
Ruby Regexp - A Magical Tool For Text Processing PDF (2020)
Ruby Notes for Professionals PDF (2018)
Ruby on Rails Notes for Professionals PDF (2018)

Free C Programming Books

Beej's Guide to C Programming PDF (2022)
C Programming (Wikibook) (2021)
An Introduction to C & GUI Programming PDF (2019)
Modern C (2019)
C Notes for Professionals PDF (2018)
How To Think Like A Computer Scientist: C Version PDF (2018)

Free Swift & iOS Books

The Swift Programming Language (2022)
100 Days Of Swift (2020)
Hacking with iOS: SwiftUI Edition (2020)
Swift: A Quick Reference Guide (2019)
iOS Developer Notes for Professionals PDF (2018)

Free Flutter Books

Flutter Cookbook (2022)
Dart Overview (2022)
Flutter Libraries We Love (2021)
Flutter Succinctly (2019)
Getting Started with Flutter - Flutter Tutorials Handbook (2019)

Free C# Books

Free Go Programming Books

Effective Go (2022)
Production Go - Build Modern, Production-y Web Services In Go PDF (2021)
Go 101 (2021)
How To Code in Go PDF (2020)

Free Rust Books

Free HTML and CSS Books

HTML & CSS Is Hard (2021)
HTML to React: The Ultimate Guide PDF (2021)
Learn to Code Advanced HTML & CSS (2014)

Free Typescript Books

Tackling TypeScript: Upgrading from JavaScript (2022)
The Typescript Handbook PDF (2022)
Typescript in 50 Lessons PDF (2020)

You can check all the remaining 250+ Python, JavaScript, Java, C++, C, C#, Scala, Haskell, Objective-C, Julia, R, Typescript, Perl, Lisp, and other Programming eBooks From here: Free Programming Books

Please don't forget to check all the 300+ free as well as best programming books from the above given link. After checking all books, If you find any free programming pdf or eBook missing, please share it in the comments. And if you like this post, Please give a heart and share this or original post with your friends.

Flutter Projects For Beginners, Intermediate And Experienced Developers

John Doe — Sat, 26 Jun 2021 06:10:31 +0000

Popularity of flutter is getting higher everyday! If you are thinking about a hybrid app, then most people will suggest you flutter. If you are an experienced Flutter Developer, You might have heard as well as searched for this question “What are some real-world flutter projects for beginners & those at an intermediate skill-level and which projects should I work on to gain real time experience?" (when you're a beginner) in different communities like GitHub, Reddit, Quora or maybe on the Web.

In this project based learning guide, You’ll find example projects with step-by-step instructions, video tutorials and source code that’ll take you through building some cool, interesting, unique and real-world Flutter projects, from scratch.

👉 Spotify Clone UI for Web and Desktop

Quick Guide: In this Project, The author will teach you how to build Spotify's user interface for desktop and web. With Flutter, it's super easy to develop responsive apps that work across different platforms. Users will be able to select tracks and our UI updates accordingly. You'll use provider to share state throughout this application and keep track of the currently selected song. Throughout the tutorial, You'll be hit upon some easy UI tricks to develop a clean and beautiful user experience.

Tutorial & Code: Spotify Clone by MarcusNg (Tutorial) and Source Code

👉 Create a 2D Snake Game in Flutter

Summary: Learn how to use Flutter as a simple game engine by creating a classic 2D Snake Game. Get to know the basics of 2D game graphics and how to control objects. In this Project, You'll learn about how to use Flutter as a game engine, Move objects, Control movement, Build game UI, Add game elements.

Code, Demo & Tutorial: 2D Snake Game by Ray Wenderlich (Demo), Tutorial & Code

👉 Online Shop App UI using Flutter

Summary of this Project: In this project, The author will share with you a nice and clean online shop app UI by using Flutter. The UI has two pages one for the product page which has a horizontal list of categories. Then on the details page, it shows the price and short description of the product with the buy now button.

Tutorial, Code & Demo: Online Shop App UI, Project by The Flutter Way (Tutorial), Demo and Source Code

👉 Flutter WhatsApp Clone

Project Summary: The tutorial attached below will take you through a step by step implementation of how to build a clone of WhatsApp UI in flutter. Check out the demo, code and tutorials from the below given links.

Tutorial & Code: Flutter WhatsApp Clone (Tutorial) by MTechViral and Source Code

👉 Portfolio Website using Flutter

Project Summary: In this project, you will create a nice clean Portfolio Website for Designer or developer using Flutter. This website will include almost everything that you want to show. At first, your name and a big picture. Then About section shows more info about you with a cool experience card. Then the list of Services that you offer. It also has a hire me card then it also have a section where it shows some recent work, then what your clients say about you and more.

Tutorial, Demo & Code: Portfolio Website by The Flutter Way (Demo), Tutorial and Code

👉 Book Search App

Project Summary: This project is for beginners only. It's a simple app and author has divided tutorials into four parts. In the 1st part, you'll learn about the logic, some piece of codes and implementation. In 2nd tutorial, you will learn about Animation and Database integration. And in last two medium guides, you'll learn and implement strucutres, creating screens, managing data, handling errors, etc.

Tutorial, Source Code & Demo: Book Search Project (Tutorial 1, 2, 3 & 4) and Demo & Code

👉 Messenger UI using Flutter

Summary: In this project, you are going to create the clone of the Messenger UI using the the flutter framework. You will create the main conversation home screen of the messenger app which will contain a top app bar, a search bar, a stories section, and a conversation list section.

Tutorial & Code: Messenger UI by FreeCodeCamp, Project Tutorial and Source Code

👉 Clean Login UI with Hero Animation in Flutter

Summary: In this project based learning tutorial, you are going to create a clean and simple login UI screen with a basic hero animation in Flutter, a mobile Sdk framework by Google. Go through the code if you face any problems.

Tutorial, Code & Demo: Clean Login UI with Hero Animation by Code Versitas (Demo), Tutorial and Source Code

👉 Hangman Game using Flutter

Summary: The project is simple and suitable for beginners. Code is available for both android and iOS. The programming languages used to create this game are Dart, a little bit Ruby, a small chunk of Objective C and Java.

Code & Demo: Hangman Game using Flutter (Demo), Guide & Code

👉 Todo App using Flutter

Quick Guide: This project will be lengthy and focused for intermediate developers that are fairly familiar with Flutter framework. This project based tutorial will help you to learn Google's Business Logic Component Architecture Design Pattern (Bloc), Reactive Programming using Dart streams (Similar to Redux), Asynchronous events & operations (Future events), CRUD operations I/O using Local Database and more.

Tutorial, Code & Demo: Todo App using Flutter, Project Tutorial, Demo and Source Code

👉 Gmail Clone using Flutter

Summary of this Project: The purpose of this project is to showcase how to build Gmail Clone with Flutter. Tutorial and Code are available below. Go through it and create your own Gmail app.

Tutorial & Source Code: Gmail Clone Project Tutorial and Code

👉 Emoji UI for Food Ordering using Flutter

Project Summary: In this small but amazing project, you will create some great UI screens using emojis and other stuffs. This project is one of the best in this list because the UI inspiration taken by the author from dribbble is very hot.

Tutorial, Code & Demo: Emoji UI for Food Ordering by Raja Yogan (Demo Screen 1, 2, 3), Tutorial and Source Code

If you like the above projects and want to see more, then check out remaining 60+ projects from the below given link

Check out other flutter projects from here: Remaining 60+ Flutter Projects for Beginners, Intermediate and Experienced Developers (With Tutorials and Source Code)

70+ Python Projects For Beginners, Intermediate And Experienced Developers

John Doe — Mon, 21 Jun 2021 08:25:41 +0000

Building hands-on projects will help you gain practical coding skills. One step at a time, you’ll be putting your theoretical knowledge to use and build an impressive portfolio. If you are an experienced Python Developer, You might have heard as well as searched for this question “What are some python projects for beginners & those at an intermediate skill-level and Which projects should I work on to gain real time experience?” (when you're a beginner) in different communities like GitHub, Reddit or Quora.

In this post, you’ll find example projects with step-by-step instructions, tutorials and source code that’ll take you through building some cool, interesting, unique and real-world Python projects, from scratch.

👉 Bitcoin Price Notifications using Python

Summary of this Project: In this Project, You'll learn about HTTP requests and how to send them using the requests library. You will also learn about webhooks and how to use them to connect your Python app to external services like phone notifications.

Tutorial & Code: Bitcoin Price Notifications Project by Real Python

👉 Crawl Wikipedia Pages with Python

Summary: Learn how to build a basic web-scraping app with python that scrapes Wikipedia pages and follows random links endlessly to simulate a web crawler. You can expand this project into many creative ideas, for example, you can use it to verify links on your websites by crawling all of them and notifying you when a dead link is found.

Tutorial & Code: Crawl Wikipedia Pages with Python Project by FreeCodeCamp

👉 Python Face Recognition Project

Project Summary: This is a fun intermediate python project. This Face Recognition project detects faces and places a box around it and identifies the face based on those in a given list. It works by analyzing a photo and comparing it to the faces in the list to determine if it’s a match or if it’s an unknown identity. A few python modules are required to download in order to complete this project and have it run properly.

Tutorial & Code: Python Face Recognition Project by Tech with Tim

👉 Blog Web Application Project using Python

Project Summary: If you’ve ever wanted to create a blog from scratch, this project is for you. Corey Schafer‘s tutorial series utilizes Python’s Django framework for the back end development. This is among the more difficult projects on this list but each part of the project is explained thoroughly. Not to mention, if you make any mistakes you can always refer to the project source code.

Based on Django, The Source Code and Tutorials: Python Full-Stack Blog Web Application Project by Corey Schafer Django based Tutorials and Source Codes

Based on Flask, The Source Code and Tutorials: Python Full-Stack Blog Web Application Project by Corey Schafer Flask based Tutorials and Source Codes

👉 Amazon Price Tracker Project

Summary of this Project: This python project tracks the price of an item on Amazon and emails you when the item has had a reduction in price. It does this by scraping the product information from the web page for the price of the item and compares it to whatever price you choose (The price you’re willing to buy the item for). If the price of the item falls below the value that you set, it sends you an email with the new price as well as the description of the item.

Tutorial: Amazon Price Tracker Project by Dev Ed

Download for Free (All are Open Source): "50+ Free Python Programming eBooks"

👉 Face Clustering with Python

Project Summary: In this project, you'll write two Python scripts: One to extract and quantify the faces in a dataset and another to cluster the faces, where each resulting cluster (ideally) represents a unique individual

Tutorial & Codes: Face Clustering with Python, Project by Adrian Rosebrock (PyImageSearch)

👉 Detecting Fake News with Python

Summary of this Project: In this Project, You will train a dataset of shape 7796×4 on news.csv. You’ll mainly use two things- a TfidfVectorizer and a Passive Aggressive Classifier. A TfidfVectorizer turns a collection of raw documents into a matrix of TF-IDF features. And a Passive Aggressive Classifier is an online learning algorithm that stays passive for a correct classification and becomes aggressive when there’s a miscalculation.

Tutorial & Code: Detecting Fake News with Python by DataFlair

👉 Build a Simple Blockchain in Python

Prerequisite: You should be comfy reading and writing some basic Python, as well as have some understanding of how HTTP requests work, since we’ll be talking to our Blockchain over HTTP.

Tutorial & Code: Build a Simple Blockchain in Python by Hackernoon

👉 Memory Puzzle Game

Summary of this Project: Build a Python game with PyGame that will test your memory. You'll learn about nested for loops and the pygame library.

Tutorial: Memory Puzzle Game by AI Sweigart

👉 Python Full-Stack E-Commerce Project

Summary: This is probably one of our favorite Python project on this list. If you are curious in building an eCommerce website like Amazon or Shopify or Walmart or Flipkart, and wants to sell your own products directly to your customers, then now you can do it. The tutorial attached with this project will take you step-by-step through building a full-stack eCommerce project using Python and Django. This is one of the hardest projects on this list so thankfully Dennis has provided the source code.

Source Code & Tutorial: Python Full-Stack E-Commerce Project by Dennis Ivy (Tutorial) & Code

👉 Scrape StackOverFlow with Scrapy and MongoDB

Project Summary: In this project, you will take a deep dig to build a scraper for an actual freelance gig where the client wants a Python program to scrape data from Stack Overflow to grab new questions (question title and URL). Scraped data should then be stored in MongoDB. It’s worth noting that Stack Overflow has an API, which can be used to access the exact same data.

Tutorial: Scrape StackOverFlow with Scrapy and MongoDB Project by Real Python