Learning artificial intelligence isn’t easy and probably would never be.
But, it’s more accessible now than ever before, thanks to the availability of online classes.
When it comes to learning, one prime concern of the course takers is - Where to take the course from? Is the course provider reliable?
What can be better than learning from the best universities globally?
We have listed AI courses from the top universities, including the likes of Harvard, MIT & Stanford.
The course content from these platforms goes much deeper on the subject than an average article or video you would have chosen to spend time on.
These online courses are taught by top AI researchers or experts, and are available for free! Take your pick.
This specialized course in Artificial Intelligence with Python is from Harvard and it covers the modern artificial intelligence concepts and algorithms.
The course includes hands-on projects to help students learn about theories behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning that can be incorporated in Python programs.
10 - 30 hours per week
FREE. Add a Verified Certificate for ₹14,967
CS50 or prior programming experience in Python.
David J. Malan, Brian Yu
- Graph search algorithms
- Adversarial search
- Knowledge representation
- Logical inference
- Probability theory
- Bayesian networks
- Markov models
- Constraint satisfaction
- Machine learning
- Reinforcement learning
- Neural networks
- Natural language processing
2. Data Science: Machine Learning by Harvard
Data Science: Machine Learning by Harvard is a part of the Professional Certificate Program in Data Science, and covers popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
With this course, you will explore training data, use a set of data to discover potentially predictive relationships, and train algorithms using training data to predict the outcome for future datasets.
- The basics of machine learning
- How to perform cross-validation to avoid overtraining
- Several popular machine learning algorithms
- How to build a recommendation system
- What is regularization and why it is useful?
The course is designed for students and professionals in biostatistics, computational biology, bioinformatics, and data science.
The course includes video lectures, case studies, peer-to-peer engagements and use of computational tools and platforms (such as R/RStudio, and Git/Github), and a reproducible research project.
Curtis Huttenhower, John Quackenbush, Lorenzo Trippa & Christine Choirat
- Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research
- Fundamentals of reproducible science using case studies that illustrate various practices
- Key elements for ensuring data provenance and reproducible experimental design
- Statistical methods for reproducible data analysis
- Computational tools for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder), reproducible data (Data repositories/Dataverse) and reproducible dynamic report generation (Rmarkdown/R Notebook/Jupyter/Pandoc), and workflows
- How to develop new methods and tools for reproducible research and reporting
- How to write your own reproducible paper
4. Artificial Intelligence by MIT
Artificial Intelligence by MIT offers an introduction to basic knowledge representation, problem solving, and learning methods of artificial intelligence.
It includes video lectures, captions/transcript, assignments, exams (no solutions), and recitation videos.
Prof. Patrick Henry Winston
- Develop intelligent systems by assembling solutions to concrete computational problems
- Understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering
- Understanding human intelligence from a computational perspective
This course explores linear algebra with applications to probability and statistics and optimization, and a complete explanation of deep learning.
It includes video lectures, tutorials as well as assignments and projects.
Lecture 1: The Column Space of A Contains All Vectors Ax ||
Lecture 2: Multiplying and Factoring Matrices
Lecture 3: Orthonormal Columns in Q Give Q’Q = I
Lecture 4: Eigenvalues and Eigenvectors
Lecture 5: Positive Definite and Semidefinite Matrices
Lecture 6: Singular Value Decomposition (SVD)
Lecture 7: Eckart-Young: The Closest Rank k Matrix to A
Lecture 8: Norms of Vectors and Matrices
Lecture 9: Four Ways to Solve Least Squares Problems
Lecture 10: Survey of Difficulties with Ax = b
Lecture 11: Minimizing ‖x‖ Subject to Ax = b
Lecture 12: Computing Eigenvalues and Singular Values
Lecture 13: Randomized Matrix Multiplication
Lecture 14: Low Rank Changes in A and Its Inverse
Lecture 15: Matrices A(t) Depending on t, Derivative = dA/dt
Lecture 16: Derivatives of Inverse and Singular Values
Lecture 17: Rapidly Decreasing Singular Values
Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points
Lecture 19: Saddle Points Continued, Maxmin Principle
Lecture 20: Definitions and Inequalities
Lecture 21: Minimizing a Function Step by Step
Lecture 22: Gradient Descent: Downhill to a Minimum
Lecture 23: Accelerating Gradient Descent (Use Momentum)
Lecture 24: Linear Programming and Two-Person Games
Lecture 25: Stochastic Gradient Descent
Lecture 26: Structure of Neural Nets for Deep Learning
Lecture 27: Backpropagation: Find Partial Derivatives
Lecture 30: Completing a Rank-One Matrix, Circulants!
Lecture 31: Eigenvectors of Circulant Matrices: Fourier Matrix
Lecture 32: ImageNet is a Convolutional Neural Network (CNN), The Convolution Rule
Lecture 33: Neural Nets and the Learning Function
Lecture 34: Distance Matrices, Procrustes Problem
Lecture 35: Finding Clusters in Graphs
Lecture 36: Alan Edelman and Julia Language
6. Statistical Learning by Stanford School of Humanities & Sciences
This course by the Stanford School of Humanities & Sciences is focused on supervised learning, regression and classification methods.
It also discusses some unsupervised learning methods including principal components and clustering (k-means and hierarchical).
First courses in statistics, linear algebra, and computing.
Trevor Hastie and Robert Tibshirani
- Linear and polynomial regression
- Logistic regression and linear discriminant analysis
- Cross-validation and the bootstrap model selection and regularization methods
- Nonlinear models, splines and generalized additive models
- Tree-based methods, random forests and boosting
- Support vector machines
7. Mining Massive Data Sets by Stanford School Of Engineering
Mining Massive Data Sets gives an introduction to modern distributed file systems, MapReduce, algorithms for extracting models and information from large datasets.
You will also learn how Google's PageRank algorithm models importance of Web pages and various extensions used in a range of purposes.
Jure Leskovec, Anand Rajaraman, & Jeffrey Ullman
- Link Analysis -- PageRank
- Locality-Sensitive Hashing -- Basics + Applications
- Distance Measures
- Nearest Neighbors
- Frequent Itemsets
- Data Stream Mining
- Analysis of Large Graphs
- Recommender Systems
- Dimensionality Reduction
- Computational Advertising
- Support-Vector Machines
- Decision Trees
- MapReduce Algorithms
- More About Link Analysis - Topic-specific PageRank, Link Spam
- More About Locality-Sensitive Hashing
The course is designed for participants with little or no programming experience.
It aims to provide students with an understanding of the role computation can play in solving problems and to help students write small programs.
It uses Python 3.5 programming language.
Prof. Eric Grimson, Prof. John Guttag, & Dr. Ana Bell
Lecture 1: Introduction and Optimization Problems
Lecture 2: Optimization Problems
Lecture 3: Graph-theoretic Models
Lecture 4: Stochastic Thinking
Lecture 5: Random Walks
Lecture 6: Monte Carlo Simulation
Lecture 7: Confidence Intervals
Lecture 8: Sampling and Standard Error
Lecture 9: Understanding Experimental Data
Lecture 10: Understanding Experimental Data (cont.)
Lecture 11: Introduction to Machine Learning
Lecture 12: Clustering
Lecture 13: Classification
Lecture 14: Classification and Statistical Sins
Lecture 15: Statistical Sins and Wrap Up
9. Predictive Analytics using Machine Learning by Harvard
Predictive Analytics using Machine Learning offers an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python.
With the help of these models, you can derive smart analytics tools and use it for various purposes including image classification, text and sentiment analysis, among others.
The course contains two case studies - forecasting customer behaviour after a marketing campaign, and flight delay and cancellation predictions.
Week 1 - Decision trees
Week 2 - Random forests and support vector machines
Week 3 - Support vector machines
Week 4 - Neural networks
Week 5 - Neural network estimation and pitfalls
Week 6 - Model comparison
This course covers various statistics topics such as multiple testing problems, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. You will then learn about statistical modeling and its application in high-throughput data.
Rafael Irizarry & Michael Love
- Organizing high throughput data
- Multiple comparison problem
- Family Wide Error Rates
- False Discovery Rate
- Error Rate Control procedures
- Bonferroni Correction
- Statistical Modeling
- Hierarchical Models and basics of Bayesian Statistics
- Exploratory Data Analysis for High throughput data
All the best!