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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

9 Free AI Courses from Stanford

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 08 Jul, 2024
6 min read

Introduction

Artificial Intelligence (AI) is transforming industries and creating new possibilities in various fields. Stanford University, renowned for its contributions to AI research, offers several free courses that can help you get started or advance your knowledge in this exciting domain. Whether you’re a beginner or an experienced professional, these courses provide valuable insights into AI concepts and techniques. In this article, we’ll explore nine AI courses from Stanford that are available online for free.

Meanwhile, you can check out this free introductory course on AI offered by Analytics Vidhya, which can help you get started.

9 Free AI Courses from Stanford

Table of Contents

9 Free AI Courses from Stanford

Here are 9 online courses on AI offered by Stanford, for free.

1. Supervised Machine Learning: Regression and Classification

Supervised Machine Learning: Regression and Classification | Free Stanford AI Courses

Course Highlights

  • Instructor: Andrew Ng
  • Focus: Supervised learning techniques.
  • Topics: Linear regression, logistic regression, neural networks.
  • Key Features: Practical examples, programming assignments, and quizzes to test understanding.

Pre-requisites

  • Basic understanding of linear algebra, calculus, and probability.
  • Familiarity with programming (preferably in Python or Octave).

Description

This course provides a comprehensive introduction to supervised learning. It covers key techniques like linear and logistic regression, as well as neural networks. It includes practical assignments that help solidify the foundational theoretical concepts. The content is beginner-friendly and is the first course in the Machine Learning Specialization track.

2. Unsupervised Learning, Recommenders, Reinforcement Learning

Unsupervised Learning, Recommenders, Reinforcement Learning | Free Stanford AI Courses

Course Highlights

  • Instructors: Andrew Ng, Eddy Shyu, Aarti Bagul.
  • Focus: Unsupervised learning and reinforcement learning techniques.
  • Topics: Clustering, dimensionality reduction, recommender systems, reinforcement learning.
  • Key Features: Practical projects and applications.

Pre-requisites

  • Completion of the “Supervised Machine Learning: Regression and Classification” course or equivalent knowledge.
  • Understanding of linear algebra, calculus, and probability.

Description

This course is the second one in Stanford’s Machine Learning Specialization track. It explores unsupervised learning techniques and their applications in recommender systems and reinforcement learning. It is ideal for learners who want to understand how to extract insights from unlabelled data and develop systems that learn from their environment.

3. Advanced Learning Algorithms

Advanced Learning Algorithms | Free Stanford AI Courses

Course Highlights

  • Instructors: Andrew Ng, Eddy Shyu, Aarti Bagul.
  • Focus: Advanced machine learning algorithms.
  • Topics: Deep learning, unsupervised learning, generative models.
  • Key Features: Hands-on assignments and real-world applications.

Pre-requisites

  • Completion of the “Supervised Machine Learning: Regression and Classification” course or equivalent knowledge.
  • Understanding of linear algebra, calculus, and probability.

Description

This last installment in the Machine Learning Specialization track teaches more advanced machine learning techniques. It builds on the foundational knowledge from the Supervised Machine Learning course and is designed for those looking to deepen their understanding of complex algorithms and their applications.

4. Algorithms: Design and Analysis

Algorithms: Design and Analysis | Free Stanford AI Courses

Course Highlights

  • Instructors: Tim Roughgarden.
  • Focus: Core principles of algorithms.
  • Topics: Sorting, searching, graph algorithms, data structures.
  • Key Features: Rigorous theoretical foundation and practical coding exercises.

Pre-requisites

  • Basic programming knowledge.
  • Familiarity with discrete mathematics and proof techniques.

Description

This course covers the fundamental principles of algorithms, including sorting, searching, and graph algorithms. It provides a strong theoretical foundation along with practical coding exercises. It is suitable for anyone looking to understand the mechanics behind algorithm design and analysis.

5. Statistical Learning with Python

Statistical Learning with Python

Course Highlights

  • Instructors: Trevor Hastie, Robert Tibshirani.
  • Focus: Statistical methods and data analysis techniques using Python.
  • Topics: Linear regression, classification, resampling methods, unsupervised learning.
  • Key Features: Practical coding assignments and case studies.

Pre-requisites

  • Basic knowledge of statistics and probability.
  • Familiarity with Python programming.

Description

This course introduces statistical learning methods with a strong emphasis on hands-on programming in Python. It’s suitable for those who want to enhance their data analysis skills using a widely-used programming language in data science and AI.

6. Statistical Learning with R

Statistical Learning with R

Course Highlights

  • Instructors: Trevor Hastie, Robert Tibshirani.
  • Focus: Statistical learning methods using R.
  • Topics: Linear regression, classification, resampling methods, unsupervised learning.
  • Key Features: Practical coding assignments using real-world datasets.

Pre-requisites

  • Basic knowledge of statistics and probability.
  • Familiarity with R programming.

Description

This course offers a comprehensive introduction to statistical learning techniques, focusing on its practical implementation using R. It is ideal for those looking to apply statistical methods to real-world data analysis problems.

7. Intro to Artificial Intelligence

Intro to Artificial Intelligence | Free Stanford AI Courses

Course Highlights

  • Instructors: Peter Norvig, Sebastian Thrun.
  • Focus: Foundational concepts and applications of AI.
  • Topics: Search algorithms, logic, probability, machine learning.
  • Key Features: Broad overview of AI including practical examples.

Pre-requisites

  • Basic programming knowledge.
  • Familiarity with linear algebra and probability.

Description

This introductory course provides a broad overview of AI to learners who are just beginning their journey. It covers essential concepts and techniques including machine learning algorithms and the applications of AI. It is a great starting point for those new to AI, offering a solid foundation to build upon with more advanced courses.

8. The AI Awakening: Implications for the Economy and Society

The AI Awakening: Implications for the Economy and Society

Course Highlights

  • Instructors: Stefano Ermon, Percy Liang.
  • Focus: Impact of AI on various sectors.
  • Topics: Economic implications, societal changes, ethical considerations, future trends.
  • Key Features: Insights from leading experts and real-world case studies.

Pre-requisites

  • No specific pre-requisites, but an interest in AI and its societal impact is beneficial.

Description

This course explores the broader implications of AI, focusing on its impact on the economy and society. It’s ideal for learners interested in understanding how AI is shaping the world and the challenges and opportunities it presents.

9. Fundamentals of Machine Learning for Healthcare

Fundamentals of Machine Learning for Healthcare

Course Highlights

  • Instructors: Nigam Shah, Matthew Lungren.
  • Focus: Application of machine learning in healthcare.
  • Topics: Predictive models, treatment effect estimation, healthcare data analysis.
  • Key Features: Case studies and practical projects.

Pre-requisites

  • Basic understanding of machine learning concepts.
  • Familiarity with healthcare data and basic programming skills.

Description

This course focuses on the use of machine learning in healthcare. It covers topics such as predictive models, treatment effect estimation, and clinical data analysis. It is perfect for those interested in applying machine learning techniques to improve healthcare outcomes.

Also Read: Machine Learning & AI for Healthcare in 2024

Conclusion

These free online courses from Stanford offer a wealth of knowledge and practical skills for anyone interested in AI and data science. From foundational courses to specialized topics like natural language processing (NLP) and reinforcement learning, there’s something for everyone. These courses are excellent resources to get you started with AI or to advance your career by updating yourself with the latest developments in AI. So, go ahead and explore! Happy learning!

Frequently Asked Questions

Q1. Are Stanford’s AI courses completely free?

A. Yes, the AI courses listed in this article are available online for free. However, you may need to pay a fee if you want a certificate of completion.

Q2. Do I need prior knowledge to take these courses?

A. While some courses, like Andrew Ng’s Supervised Machine Learning, are beginner-friendly, others may require some background in computer science and mathematics. Do check the pre-requisites before enrolling.

Q3. Can I get a certificate for completing these courses?

A. You can get a certificate for a fee. However, the course content is entirely free.

Q4. How long do these courses take to complete?

A. Course durations vary, as most of them are self-paced. They can be completed within a few weeks to a few months, depending on your pace.

Q5. What is the best course to start with?

A. The course on “Supervised Machine Learning: Regression and Classification” by Andrew Ng is highly recommended for beginners. It comprehensively covers the basics of ML and AI.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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