Machine Learning Specialization
The foundational online program in machine learning by Andrew Ng. Learn core ML concepts and build real-world AI applications.
Exam Details
Your Progress
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Step-by-Step RoadmapEstimated total: 3 to 4 months
Python & Math Refresh
Ensure you have the Python and math foundations needed for ML.
What to Learn
- Python: NumPy, Pandas, Matplotlib
- Linear Algebra (Vectors, Matrices)
- Calculus (Derivatives, Chain Rule)
- Probability & Statistics
- Jupyter Notebooks
Resources
- Python for Everybody (Coursera - free audit)
- 3Blue1Brown Linear Algebra YouTube series
Don't get stuck here! You need basic Python (loops, functions, lists) and conceptual math, not PhD-level theory. Andrew Ng's course does a great job explaining the math intuitively.
Supervised Machine Learning
Learn the core supervised learning algorithms: linear regression, logistic regression, and decision trees.
What to Learn
- Linear Regression & Gradient Descent
- Logistic Regression & Classification
- Regularization (L1, L2)
- Decision Trees
- Random Forests
- Boosting (XGBoost)
Resources
- Course 1 of ML Specialization (Coursera)
Focus on the intuition, not just the math. Understanding WHY gradient descent works matters more than deriving it from scratch. Complete all programming labs!
Advanced Learning Algorithms
Dive into neural networks, the foundation of modern deep learning.
What to Learn
- Neural Networks & Backpropagation
- Activation Functions
- TensorFlow & Keras
- Model Evaluation (Bias vs Variance)
- ML Development Process
- Multi-class Classification
Resources
- Course 2 of ML Specialization
Build and train your first neural network in TensorFlow! The hands-on labs use Jupyter notebooks. Don't skip them — this is where the learning really happens.
Unsupervised Learning & Recommender Systems
Learn clustering, anomaly detection, and how recommendation systems work.
What to Learn
- K-Means Clustering
- Principal Component Analysis (PCA)
- Anomaly Detection
- Collaborative Filtering
- Content-Based Filtering
- Reinforcement Learning intro
Resources
- Course 3 of ML Specialization
Unsupervised learning is used when you don't have labeled data. Anomaly detection (finding unusual patterns) is huge in real business applications like fraud detection.
Build Projects & Get Certified
Complete capstone projects, earn the certificate, and build your portfolio.
What to Learn
- Complete all graded assignments
- Build a personal ML project
- Share on GitHub/LinkedIn
- Earn certificate
Resources
- Kaggle.com beginner datasets for practice projects
- GitHub for portfolio
Build one real project using a dataset you care about! Apply classification to predict something interesting. Put it on GitHub with a README explaining what you did and why.