Machine learning and AI: the new trend

Pranjali Sawant — Wed, 11 Jun 2025 17:37:49 +0000

🧠 Catching Up with AI: What I Learned from My First ML & DL Cohort as a Student

Unless you’ve been living completely offline, you’ve probably seen how AI and Machine Learning are reshaping the future. From ChatGPT to self-driving cars, the hype is real — and as a student, I felt the urge to catch up.

That’s when I joined a student-friendly Machine Learning & Deep Learning cohort, and it became one of the best learning experiences of my tech journey so far.

🚀 Why I Joined the ML/DL Cohort

I’ve always been more focused on frontend development — HTML, CSS, JavaScript, React — but I didn’t want to ignore the growing role of AI. So when I saw a chance to explore Machine Learning, I grabbed it.

The cohort was designed for beginners: structured lessons, hands-on exercises, and clear explanations. No math-heavy fear, just curiosity and code.

📚 What I Learned

Here’s a breakdown of the concepts and topics we explored:

🔹 Machine Learning Basics

ML is about training a model using data to make predictions or generate output.
There are 3 main types of ML:
- Supervised Learning (with labeled data)
- Unsupervised Learning (without labels)
- Reinforcement Learning (with rewards/penalties)

🔹 Core Concepts in Supervised Learning

Features & Labels: Features are input data; labels are the output we want to predict.
Model Training: The model adjusts its parameters based on the difference between its predictions and the actual label (aka loss).
Evaluation: After training, the model is tested using data it hasn’t seen before.

🔹 Regression & Classification

Regression predicts a numeric value (e.g., predicting rainfall).
Classification predicts categories (e.g., spam or not spam).

🔹 Gradient Descent & Loss

We use loss functions (like MAE, MSE) to measure how wrong predictions are.
Gradient descent is used to minimize this loss by updating weights and biases.

🧠 What About Deep Learning?

We also explored Deep Learning, a powerful subset of ML inspired by the human brain.

🔸 Neural Networks

Neural networks are built with layers of neurons.
Each neuron processes inputs with weights and biases, then passes it to the next layer.
Activation functions decide whether a neuron should "fire" or not.

🔸 Types of Neural Networks

Convolutional Neural Networks (CNNs) – great for image classification.
LSTMs – ideal for sequence data like speech or text.

🛠 Tools We Used

Python – the language we used for almost everything
Pandas & NumPy – for handling and cleaning data
Matplotlib & Seaborn – for data visualization
Scikit-learn – for training models like Linear Regression, Decision Trees
Google Colab – to run code in the cloud without setup

💡 My Takeaways as a Beginner

Start small – Learn the concepts, not just the syntax.
Practice more than you watch – Run code, tweak parameters, see what changes.
Visualization helps – Graphs and charts made everything more understandable.
Don't fear the math – You can still learn the logic first and go deeper later.
ML can fit anywhere – Even my digital journal app might use mood-based ML soon!

🌱 What’s Next?

While my focus remains on frontend and UI/UX, I now see how ML can complement my work. Whether it’s a recommendation engine, mood tracker, or smart health alert — I’ve opened the door to AI-powered features in my future projects.

🙌 Final Thoughts

To anyone who’s scared to get started with ML or feels like it’s “too advanced” — don’t worry. I started with no confidence in this domain, and now I’m genuinely excited to keep learning.

If you’ve just started or are curious about ML/DL, let’s connect! Drop a comment or DM me — always happy to chat with fellow learners!

Thanks for reading! 🚀

Beginner in Tech

Pranjali Sawant — Wed, 11 Jun 2025 17:22:34 +0000

My Frontend Journey So Far: From Confused to Curious

Like many students in tech, I started learning frontend with a mix of excitement and confusion. I’d heard about HTML, CSS, JavaScript—but had no idea how they all fit together.

This post is a reflection on where I started, what I'm learning now, and how building small projects is helping me become more confident in frontend development.

🌱 Where I Started

I began with basic HTML and CSS, trying out small layouts and forms. It was fun, but I quickly realized there was so much more to learn—especially when I reached JavaScript.

At that stage, it felt overwhelming. DOM manipulation? APIs? React? I didn’t even know where to begin.

💡 How Things Started Clicking

Things started making more sense when I worked on a college project using the MERN stack. Even though I wasn’t the main coder, observing how things were structured gave me clarity.

Eventually, I started learning React on my own and began building personal projects — like a digital journal app and a medical web app to connect doctors and patients.

🛠️ Tools & Tech I'm Exploring

JavaScript (especially functions, arrays, DOM)
React.js (components, props, hooks)
MongoDB + Express
Git and Postman
UI/UX design and thinking principles

📚 What I’ve Learned So Far

You don’t need to feel “ready” to start building.
Breaking projects into small parts makes them less overwhelming.
It’s okay to get stuck — that’s where real learning happens.

🔭 What's Next?

I’ll be publishing more posts like this to share what I’m learning, mistakes I make, and how I fix them.

If you’re also learning frontend, let’s connect!

💬 Drop your tips, experiences, or questions below — I’d love to chat with other learners like me!

Thanks for reading! 😊

Forem: Pranjali Sawant