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So you already know about Machine Learning? - Ìmọ̀ Ẹ̀kọ́ Ẹ̀rọ́ Ṣe Pàtàkì L'asìkò Tawà Yì

STEMInstructor — Thu, 20 Jan 2022 15:07:31 +0000

A Bit of Machine Learning

ML helps systems the ability to learn automatically and improve from experience without being programmed explicitly. It allow computers and systems to discover useful insights.

For short, ML is a new way of telling your wishes to a system.

Iwulo Pàtàkì Ìmọ̀ Ẹ̀kọ́ Ẹ̀rọ́

Detecting Fraud — Fraud detection in credit card transactions.
Identification of Spam Emails — Helps in categorizing inbox and spam mails.
Recommendation Apps and Websites — Websites like Ebay can recommend you products based on your previously checked or favorited list of products and many more.

Written by Rasheedah

If you like this article please like and share. Follow us on Twitter. Visit Rollademy to learn more about Data Science free of charge.

Examples of Pandas In Real Life - Ẹ Gbà Ẹ Tọwó

STEMInstructor — Thu, 20 Jan 2022 15:05:08 +0000

Some of the real life examples of Python Pandas is that it is widely used in:

Recommendation Systems: Netflix, YouTube or Spotify have been giving us great recommendations. This is only possible by handling a lot of data and by learning user inputs.
Stock Prediction: With Pandas, we can make models that will predict the Stock Market results.
Economics: Helps to know how the Economy is growing in various sections. Economists can use Pandas to analyze huge datasets.

Other Uses by Pandas Website

Pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool.

Pandas is a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python.

Pandas has been used extensively in production in financial applications.

Written by Rasheedah

If you like this article please like and share. Follow us on Twitter . Visit Rollademy to learn more about Data Science free of charge.

Real Life Examples of Pandas - Ẹ Gbà Ẹ Tọwó

STEMInstructor — Wed, 19 Jan 2022 12:36:23 +0000

Some of the real life examples of Python Pandas is that it is widely used in:

Recommendation Systems: Netflix, YouTube or Spotify have been giving us great recommendations. This is only possible by handling a lot of data and by learning user inputs.
Stock Prediction: With Pandas, we can make models that will predict the Stock Market results.
Economics: Helps to know how the Economy is growing in various sections. Economists can use Pandas to analyze huge datasets.

Other Uses by Pandas Website

Pandas is a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python.

Pandas has been used extensively in production in financial applications.

Written by Rasheedah

If you like this article please like and share. Follow us on Twitter . Visit Rollademy to learn more about Data Science free of charge.

Matploitlib - Python's Library

STEMInstructor — Tue, 11 Jan 2022 16:25:46 +0000

The most popular Python Plotting Library is Matploitlib. It is very useful for those working in Numpy and Python. It’s interface looks like Matlab, just that you have to write more codes but it comes with lots of freedom.

What is Matploitlib?

It is a comprehensive library used for creating animated and interactive visualizations in Python.

Advantages of Matploitlib

Matploitlib is not only powerful, it is efficient, fast and open-source. Also, it has:

High-quality graphics
Large community support
Cross-platform

give full control over plot styles

In Yorùbá you can say:

Ògòngò lọba ẹyẹ, kìnìún lọba ẹranko - so therefore, Python Matploitlib lagbade fun.

Matploitlib can therefore be regarded as king of data visualization.

Written by Rasheedah

If you like this article please like and share. Follow us on Twitter. Visit Rollademy to learn more about Data Science free of charge.

Data Visualization - Basic Things To Know

STEMInstructor — Mon, 10 Jan 2022 15:15:06 +0000

What is Data Visualization?

Data Visualization is the representation of data in a chart, a graph or other visual representation. One of the basic things to know about data visualization is that it communicates the relationship of data with images.

Data Visualization is the way our brains work and how the brain translates ideas into reality. There’s a Yorùbá adage that says:

Oun tí ojú àgbàlágbà nri lorí ìjókò, bí ọmọdè bá gun orí igi ojú e kò le rí lae lae

Breaking it down…

Another basic thing to know about data visualization is that it is a form of visual art that makes us keep our attention on the message that makes us keep our attention on the message.

Example:

Google Map Traffic Visualization - See here

Hard Disk Drive Data Visualization

Visual representation of data etc.

Written by Rasheedah

If you like this article please like and share. Follow us on Twitter. Visit Rollademy to learn more about Data Science free of charge.

Google's Machine Learning Crash Course - 43 Rules of Machine Learning (summary)

STEMInstructor — Thu, 12 Aug 2021 10:58:51 +0000

At the end of this article, you will understand the basic knowledge of machine learning and get the benefit of Google's best practices in machine learning:

This approach will work well for a long time. Diverge from this approach only when there are no more simple tricks to get you any farther.

Rule #1: Don’t be afraid to launch a product without machine learning

Rule #2: First, design and implement metrics.

Rule #3: Choose machine learning over a complex heuristic.

Rule #4: Keep the first model simple and get the infrastructure right.

Rule #5: Test the infrastructure independently from the machine learning.

Rule #6: Be careful about dropped data when copying pipelines.

Rule #7: Turn heuristics into features, or handle them externally.

Rule #8: Know the freshness requirements of your system.

Rule #9: Detect problems before exporting models.

Rule #10: Watch for silent failures.

Rule #11: Give feature columns owners and documentation.

Rule #12: Don’t overthink which objective you choose to directly optimize.

Rule #13: Choose a simple, observable and attributable metric for your first objective.

Rule #14: Starting with an interpretable model makes debugging easier.

Rule #15: Separate Spam Filtering and Quality Ranking in a Policy Layer.

Rule #16: Plan to launch and iterate.

Rule #17: Start with directly observed and reported features as opposed to learned features.

Rule #18: Explore with features of content that generalize across contexts.

Rule #19: Use very specific features when you can.

Rule #20: Combine and modify existing features to create new features in human¬-understandable ways.

Rule #21: The number of feature weights you can learn in a linear model is roughly proportional to the amount of data you have.

Rule #22: Clean up features you are no longer using.

Rule #23: You are not a typical end user.

Rule #24: Measure the delta between models.

Rule #25: When choosing models, utilitarian performance trumps predictive power.

Rule #26: Look for patterns in the measured errors, and create new features.

Rule #27: Try to quantify observed undesirable behavior.

Rule #28: Be aware that identical short-term behavior does not imply identical long-term behavior.

Rule #29: The best way to make sure that you train like you serve is to save the set of features used at serving time, and then pipe those features to a log to use them at training time.

Rule #30: Importance-weight sampled data, don’t arbitrarily drop it!
Rule #31: Beware that if you join data from a table at training and serving time, the data in the table may change.

Rule #32: Re-use code between your training pipeline and your serving pipeline whenever possible.

Rule #33: If you produce a model based on the data until January 5th, test the model on the data from January 6th and after.

Rule #34: In binary classification for filtering (such as spam detection or determining interesting emails), make small short-term sacrifices in performance for very clean data.

Rule #35: Beware of the inherent skew in ranking problems.

Rule #36: Avoid feedback loops with positional features.

Rule #37: Measure Training/Serving Skew.

Rule #38: Don’t waste time on new features if unaligned objectives have become the issue.

Rule #39: Launch decisions are a proxy for long-term product goals.

Rule #40: Keep ensembles simple.

Rule #41: When performance plateaus, look for qualitatively new sources of information to add rather than refining existing signals.

Rule #42: Don’t expect diversity, personalization, or relevance to be as correlated with popularity as you think they are.

Rule #43: Your friends tend to be the same across different products. Your interests tend not to be.

Google's Machine Learning Crash Course - Introduction to ML

STEMInstructor — Wed, 11 Aug 2021 18:55:08 +0000

Here is the summary of Video 1: Introduction to ML

At the end of the article, you will be able to:

Recognize the practical benefits of mastering machine learning
Understand the philosophy behind machine learning

Machine learning is a valuable tool that help software engineers make sense of their data. First, it gives you a tool to reduce the time you spend programming. Second, it will allow you to customize your products, making them better for specific groups of people. And third, machine learning lets you solve problems that you, as a programmer, have no idea how to do by hand.

As a human being, I have the ability to recognize my friends' faces and understand their speech, but I do all of this subconsciously so if you asked me to write down a program to do it, I'd be completely baffled. But these are tasks that machine learning algorithms do very well; I don't need to tell the algorithm what to do, I only need to show the algorithm lots of examples, and from that the task can be solved. In a nutshell, Machine learning changes the way you think about a problem.

Is Machine Learning For You?

STEMInstructor — Tue, 29 Jun 2021 10:57:47 +0000

Machine learning (ML) is a method of data analysis that automates analytical model building. It is one of the branches of artificial intelligence (AI) based on the idea that systems can learn from data, make decisions and identify patterns with little or no human intervention. ML can also be described as a type of AI that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.

Why is it recommended that you learn ML?

ML is very important because it gives organizations an idea about changes in customer behavior and business operational patterns, and also in the support of the development of new products.

Do you think you would like to pick up a Machine Learning Tutorial or eBook soon? Please let me know in the comment section.

I hope this helps :-)

If you enjoy this, please subscribe to my Newsletter and check out my work here. You can also hook me up on Twitter

Prioritizing your Mental Health As a Developer

STEMInstructor — Mon, 28 Jun 2021 04:09:11 +0000

There is no perfect answer when it comes to telling people where you work about the condition of your mental health. What you have chosen to tell others really depends on how much it affects your role, your relationships with your colleagues and the amount of support you have outside the workplace. The following are some of the things you can do to prioritize your mental health.

Eat well—poor food choices can affect brain function. More healthier foods like vegetables, fruits and grains contribute greatly to an improved mental health.

Rest—Good hours of sleep at night helps improves your ability to handle problems and react in a good way to situations.

Exercise—a regular physical exercise can improve the metabolic function and produces endorphins which helps reduces tension, increase alertness and improves good mood.

I hope this helps :-)

If you enjoy this, please subscribe to my Newsletter and check out my work here. You can also hook me up on Twitter

Basic Unity3D Interface

STEMInstructor — Wed, 05 May 2021 15:42:20 +0000

WHAT IS UNITY
Unity3D is a powerful cross-platform 3D game engine and has a user-friendly development environment. Unity is a flexible engine for beginners and expert game developers. Unity is used to create 3D games and applications for mobile, desktop, the web, and consoles. You can download and launch unity by visiting https://unity3d.com/get-unity/download.

UNITY USER INTERFACE

THE SCENE VIEW: Imagine yourself that you are a director of a movie, so basically a scene view is everything including the stage. You have the cameras, the lights, maybe some actors off stage that are about to come in i.e., your game objects, in just a moment. So basically, you see everything that will make up your game and this is also where you're going to be designing your game. (See page 2 for image).

Contd:
Download the free e-book here (3 -paged Manual)

https://bit.ly/3uqvIta

Subscribe to Newsletters on How to Create 3D Games Easily here:
https://bit.ly/2PQzZXU

Flip Yo Bg Color in JavaScript ✌😊

STEMInstructor — Tue, 27 Oct 2020 17:01:45 +0000

Have you been looking for how to flip yo bg in JavaScript?😊 Yeah Here's how & as usual, with the JavaScript codes explained line by line. Another edition of #OneAppADay Series.

https://rasheedatoba.com.ng/category/build-an-app/javascript/

Please like and share. 😊😉

Instagram User Online with HTML & CSS

STEMInstructor — Mon, 26 Oct 2020 14:47:05 +0000

Coding everyday has been a wonderful thing and here's sharing with you how to code an Instagram User Online interface with HTML and CSS with detailed explanation of each line of codes. Here is a link to it. Enjoy! :-)

https://rasheedatoba.com.ng/instagram-user-online/