Learn Machine Learning from Zero to Job-Ready

The biggest mistake beginners make: jumping into deep learning frameworks before understanding the fundamentals. Start with the math intuition and classical ML first — neural networks will make 10x more sense when you get there.

The Learning Path (in order)

1. Python basics (2–4 weeks) — You need loops, functions, lists, dicts, and classes. If you already code in another language, this is 1 week.
2. NumPy + Pandas + Matplotlib (2–3 weeks) — These are your data manipulation tools. Every ML job uses these daily.
3. Classical ML with scikit-learn (4–6 weeks) — Linear regression, decision trees, random forests, SVMs, k-means clustering. This is where 80% of real-world ML actually lives.
4. Math fundamentals (parallel track) — Linear algebra (matrices, dot products), statistics (mean, variance, distributions), calculus (what a derivative means intuitively). You do NOT need to be a mathematician — you need enough to understand WHY algorithms work.
5. Deep Learning (after all of the above) — PyTorch is the industry standard now. TensorFlow is declining in favour of PyTorch.
6. Build projects + Kaggle competitions — This is what gets you hired. A GitHub with 3 solid projects beats a certificate every time.

Best Free Starting Point

Go to fast.ai for deep learning (top-down, practical-first) OR Andrew Ng's ML Specialization on Coursera for a rigorous bottom-up foundation. Pick one and finish it — do not course-hop.

Realistic Timeline

• 3 months: Comfortable with classical ML, can do basic data analysis
• 6 months: Can build and deploy models, familiar with neural networks
• 12 months: Job-ready with a portfolio

Pro tip: Kaggle is free and gives you real datasets, competitions, and notebooks from top practitioners. Even browsing winning solutions for 30 minutes a day will accelerate your learning faster than most paid courses.