Want to Learn Machine Learning? Start With This 3-Step Plan

Want to get into machine learning, but don’t know where to start? You’re not alone. It’s easy to get overwhelmed by buzzwords, complex math, and endless tutorials that don’t really teach you anything. But don’t worry—I’ve got you. ✨

This 3-step plan will save you months of confusion and help you build real skills with confidence. No fluff, no jargon, just a clear path forward.

✅ Step 1: Learn Python (Don’t Skip This!)

Before you touch any machine learning model, you need a solid foundation in Python. It’s the go-to language for data science and ML because of its simplicity and wide range of libraries.

You don’t need to know anything up front. Just follow along and build your skills one step at a time.
👉 Recommended Resource: Free Python course on Codecademy
It’s interactive, beginner-friendly, and completely free.

Take your time here — getting comfortable with loops, conditionals, functions, and basic data structures will pay off big time when you start building models later.

✅ Step 2: Learn the Fundamentals of Machine Learning (Without the Math)

Once you’ve got a handle on Python, it’s time to peek under the hood of machine learning. No, you don’t need to become a math wizard overnight. But you do need to understand the core concepts like:

• What is machine learning?
• Supervised vs. unsupervised learning
• Training vs. testing data
• Overfitting and underfitting
• Evaluation metrics

👉 Recommended Resource: Free beginner-friendly ML course by Andrew Ng on DeepLearning.AI
This course breaks it down in plain English and gives you the foundational knowledge without overwhelming you with formulas.

✅ Step 3: Build Your Own Machine Learning Models

Now the fun begins. You’ve got the knowledge—now let’s apply it.

Start small. Pick a project you care about or a dataset that seems interesting. It could be predicting house prices, classifying tweets, or recognizing handwritten digits. The key is to get hands-on and build something.

👉 Tool Suggestions:

• Kaggle – Free datasets and beginner-friendly notebooks
• Google Colab – Write and run Python code in the cloud
• Scikit-learn – A powerful and easy-to-use ML library for Python

Kaggle offers a wide variety of datasets for machine learning projects

Don’t worry if your model isn’t perfect. The goal here is to learn by doing. You can check out this model I built that analyzes your favorite perfumes and makes a recommendation for other fragrances you may like. It was soo fun - I call it NoseKnows 😂.

🎯 The Bottom Line

If you follow these three steps—learn Python, understand ML fundamentals, and build your own models — you won’t just be watching videos or reading articles. You’ll actually understand machine learning and start to think like a ML Engineer.

This is the same approach I wish someone had given me when I was getting started.

✨ Pro Tip: Bookmark this post and revisit it as you progress. Every time you level up, it’ll mean something new.

Hope this helps,

Kedasha 😊