Machine learning is one of the hottest topics today, powering everything from self-driving cars to AI-generated code. This blog is for anyone who wants to dive into machine learning from a practical, coding-focused perspective, though keep in mind that understanding the theory and math behind ML is just as important. Coding is simply the tool that brings those concepts to life.
I've divided this guide into three key sections:
- Basic Understanding of ML Algorithms
- Data Processing
- Algorithm Implementation
Basic Understanding of ML Algorithms
Before jumping into coding, you need a solid grasp of
- What Machine Learning Is
- The types of problems it can solve
- Common ML algorithms and how they work
For this, I highly recommend Andrew Ng's Machine Learning Course on Coursera (it's free!). While the course uses MATLAB, I suggest learning Python instead—it's the industry standard for ML and has a massive community.
If you prefer learning in Bangla, check out "Machine Learning Algorithms" by Nafis Nihal, which is an excellent resource.
By the end, you should understand:
- How ML works at a high level
- Different ML techniques (supervised, unsupervised, etc.)
- The intuition behind algorithms like KNN, SVM, Random Forest, and Linear Regression
Data Processing
Since we're using Python, make sure you're comfortable with the basics first. Here's a quick tutorial to brush up:
Where to Get Data?
A great place to find datasets is Kaggle. Most ML data comes in CSV format, which you can manipulate using the Pandas library:
Another essential library is NumPy, which helps with numerical operations on arrays and matrices:
Data Visualization
To visualize your data, use Matplotlib or Seaborn:
Algorithm Implementation
The most widely used library for implementing ML algorithms is Scikit-learn.
Projects to Try
Applying what you've learned is crucial. Here are some beginner-friendly projects:
-
Weather Prediction
-
Movie Recommendation System
-
Stock Price Prediction (Note: Stock prediction is highly complex and often unreliable due to market volatility—treat this as a learning exercise rather than a real-world solution.)
Wrap Up
The resources I've shared helped me learn machine learning, but feel free to explore others. One of the fastest ways to improve is by studying other people's code. Platforms like Kaggle are perfect for this; you can see how experienced developers structure their ML projects.
Happy learning!