PCA from scratch
Implement principal component analysis with NumPy and apply it to a face image dataset.
All pathsDiscover Coursera PlusAfter step 1 After step 3 After step 4 After step 5
Data & AI · 05Intermediate
ML & AI Engineer
Developers who want to understand models, not just call them.
The mathematics of machine learning, Python programming, supervised learning, neural networks and reinforcement learning.
343hours of coursework
35weeks at 10 h
5steps
18courses
5universities
Target skills
Model OptimizationArtificial Neural NetworksMachine LearningLinear AlgebraData ManipulationPython ProgrammingDebuggingRecurrent Neural Networks (RNNs)Model TrainingArtificial IntelligenceReinforcement LearningMachine Learning Algorithms
Skill labels as listed on Coursera.
Target jobs
- Machine learning engineer
- Data scientist
- AI engineer
Progression
- 1Imperial College LondonBeginnerPython, NumPy
Stated prerequisites: High school maths; basic Python helpful for course 3
Show the 3 courses
Course Level Length Rating (reviews) Mathematics for Machine Learning: Linear Algebra Beginner 19 h 4.6 (12,618) Mathematics for Machine Learning: Multivariate Calculus Beginner 18 h 4.7 (5,786) Mathematics for Machine Learning: PCA Intermediate 21 h 4.0 (3,190) - 2University of MichiganBeginnerPython, Jupyter, Pillow, pytesseract, OpenCV
Show the 5 courses
Course Level Length Rating (reviews) Python Basics Beginner 27 h 4.8 (18,462) Python Functions, Files, and Dictionaries Beginner 42 h 4.8 (5,448) Data Collection and Processing with Python Intermediate 21 h 4.7 (4,056) Python Classes and Inheritance Intermediate 20 h 4.7 (3,733) Python Project: Software Engineering and Image Manipulation Intermediate 8 h 4.2 (1,533) - 3University of Colorado BoulderIntermediatePython
Show the 3 courses
Course Level Length Rating (reviews) Introduction to Machine Learning: Supervised Learning Intermediate 23 h 4.5 (67) Introduction to Machine Learning: Unsupervised Learning Intermediate 15 h 4.6 (24) Introduction to Deep Learning Intermediate 18 h 4.5 (24) - 4Johns Hopkins UniversityIntermediatePython, TensorFlow, PyTorch
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Course Level Length Rating (reviews) Introduction to Neural Networks Intermediate 18 h 4.2 (10) Advanced Neural Network Techniques Intermediate 11 h — Practical Methodology and Ethics in AI Intermediate 7 h 4.5 (11) - 5University of Alberta (with Alberta Machine Intelligence Institute)IntermediatePython
Stated prerequisites: 1 year undergraduate CS or 2-3 years software development; Python; basic statistics, linear algebra and calculus
Show the 4 courses
Course Level Length Rating (reviews) Fundamentals of Reinforcement Learning Intermediate 15 h 4.8 (2,911) Sample-based Learning Methods Intermediate 22 h 4.8 (1,259) Prediction and Control with Function Approximation Intermediate 22 h 4.8 (851) A Complete Reinforcement Learning System (Capstone) Intermediate 16 h 4.7 (654)
With Coursera Plus
This path takes 343 hours, about 8 months at 10 hours a week. A Coursera Plus subscription gives access to most of its courses during that time.
Projects to build
End-to-end classifier
Train, validate, save and serve a model through a small command-line script.
Neural network from scratch
Write backpropagation in NumPy and recognise the handwritten digits of MNIST.
A learning agent
Program a Q-learning agent that learns to balance Gymnasium’s CartPole.
Free textbooks
Mathematics for Machine LearningM. P. Deisenroth, A. A. Faisal, C. S. OngFree PDF
An Introduction to Statistical Learning (Python)G. James, D. Witten, T. Hastie, R. Tibshirani, J. TaylorFree PDF
Deep Learning: Foundations and ConceptsC. M. Bishop, H. BishopFree online version
Reinforcement Learning: An Introduction, 2nd ed.R. S. Sutton, A. G. BartoFree PDF