10 Machine Learning Online Courses For Beginners
The following is a list of, mostly free, machine learning online courses for beginners.
If video lectures aren’t your thing, and books better suit your learning style, then be sure to check out our list of free machine learning books.
1. MACHINE LEARNING (FREE)
Andrew Ng
First, and arguably the most popular course on this list, Machine Learning provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
Topics include:
Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
The course is 11 weeks long and averages a 4.9/5 user rating, currently. It is free to take, but you can pay $79 for a certificate upon course completion.
2. MACHINE LEARNING FOUNDATIONS: A CASE STUDY APPROACH (FREE)
Carlos Guestrin, Emily Fox
In Machine Learning Foundations: A Case Study Approach, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of it you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.
By the end of this course, you will be able to:
Identify potential applications of machine learning in practice.
Describe the core differences in analyses enabled by regression, classification, and clustering.
Select the appropriate machine learning task for a potential application.
Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
Represent your data as features to serve as input to machine learning models.
Assess the model quality in terms of relevant error metrics for each task.
Utilize a dataset to fit a model to analyze new data.
Build an end-to-end application that uses machine learning at its core.
Implement these techniques in Python.
The course is 6 weeks long and requires about 5-8 hours of commitment per week. It currently averages a 4.6/5 user rating and is free to take, but you can pay $59 for a certificate upon completion.
3. LEARNING FROM DATA (FREE)
Yaser S. Abu-Mostafa
Learning From Data is an introductory course in machine learning that will cover basic theory, algorithms, and applications.
It balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion:
What is learning?
Can a machine learn?
How to do it?
How to do it well?
Take-home lessons.
You’ll learn how to:
Identify basic theoretical principles, algorithms, and applications of Machine Learning
Elaborate on the connections between theory and practice in Machine Learning
Master the mathematical and heuristic aspects of Machine Learning and their applications to real world situations
The course is 10 weeks long and requires about 10 – 20 hours per week of commitment. It is free to take, but you can add a verified certificate of completion for $49.
4. STATISTICAL LEARNING (FREE)
Trevor Hastie, Rob Tibshirani
This is an introductory-level course in supervised learning, with a focus on regression and classification methods.
The syllabus includes:
Linear and polynomial regression, logistic regression and linear discriminant analysis
Cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso)
Nonlinear models, splines and generalized additive models
Tree-based methods, random forests and boosting; support-vector machines
Also, some unsupervised learning methods are discussed like principal components and clustering (k-means and hierarchical).
This is not a math-heavy class and all computing is done in R. If you are not familiar with R that is ok. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.
The class is free to take and is expected of you to commit 3 – 5 hours per week to work through the course material. If you complete the course, and achieve a passing grade of 50% on the quizzes. If you get 90% or higher, your statement will be “with distinction”.
5. MACHINE LEARNING: REGRESSION (FREE)
Carlos Guestrin, Emily Fox
In Machine Learning: Regression, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data — such as outliers — on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.
By the end of this course, you will be able to:
Describe the input and output of a regression model
Compare and contrast bias and variance when modeling data
Estimate model parameters using optimization algorithms
Tune parameters with cross validation
Analyze the performance of the model
Describe the notion of sparsity and how LASSO leads to sparse solutions
Deploy methods to select between models
Exploit the model to form predictions
Build a regression model to predict prices using a housing dataset
Implement these techniques in Python
The course requires 6 weeks of your time and approximately 5 – 8 hours per week to study the material. It’s current user rating averages a 4.8/5. The course is free to take, but you can pay $59 to receive a certificate of completion at the end.
6. MACHINE LEARNING: CLASSIFICATION (FREE)
Carlos Guestrin, Emily Fox
In Machine Learning: Classification, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data.
By the end of this course, you will be able to:
Describe the input and output of a classification model
Tackle both binary and multiclass classification problems
Implement a logistic regression model for large-scale classification
Create a non-linear model using decision trees
Improve the performance of any model using boosting
Scale your methods with stochastic gradient ascent
Describe the underlying decision boundaries
Build a classification model to predict sentiment in a product review dataset
Analyze financial data to predict loan defaults
Use techniques for handling missing data
Evaluate your models using precision-recall metrics
Implement these techniques in Python (or in the language of your choice, though Python is highly recommended)
The course is 7 weeks long and currently averages a 4.6/5 user rating. While the course materials are provided for free, you will need to pay $59 to earn a course completion certificate.
7. MACHINE LEARNING: CLUSTERING & RETRIEVAL (FREE)
Carlos Guestrin, Emily Fox
In Machine Learning: Clustering & Retrieval you will examine similarity-based algorithms for retrieval. You will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce.
By the end of this course, you will be able to:
Create a document retrieval system using k-nearest neighbors
Identify various similarity metrics for text data
Reduce computations in k-nearest neighbor search by using KD-trees
Produce approximate nearest neighbors using locality sensitive hashing
Compare and contrast supervised and unsupervised learning tasks
Cluster documents by topic using k-means
Describe how to parallelize k-means using MapReduce.
Examine probabilistic clustering approaches using mixtures models
Fit a mixture of Gaussian model using expectation maximization (EM)
Perform mixed membership modeling using latent Dirichlet allocation (LDA)
Describe the steps of a Gibbs sampler and how to use its output to draw inferences
Compare and contrast initialization techniques for non-convex optimization objectives
Implement these techniques in Python
The course is 6 weeks in length and currently averages a 4.9/5 user rating. The course materials are free, but you’ll need to pay $59 if you want a course completion certificate.
8. UNSUPERVISED MACHINE LEARNING HIDDEN MARKOV MODELS IN PYTHON ($50)
Justin C
While the current fad in deep learning is to use recurrent neural networks to model sequences, this course will introduce you to a machine learning algorithm that has been around for several decades now – the Hidden Markov Model.
In Unsupervised Machine Learning Hidden Markov Models in Python, you’ll learn to measure the probability distribution of a sequence of random variables.
In this course you’ll learn:
How to use gradient descent to solve for the optimal parameters of an HMM, as an alternative to the popular expectation-maximization algorithm.
How to work with sequences in Theano, a popular library for deep learning
How to look at a model of sickness and health, and calculate how to predict how long you’ll stay sick, if you get sick
How Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO
Practical applications of Markov models, including generating images, smartphone autosuggestions, and using HMMs to answer one of the most fundamental questions in biology – how is DNA, the code of life, translated into physical or behavioral attributes of an organism?
The course is comprised of 35 videos and runs a total time of 4 hours. It currently averages a 4.7/5 user rating. However, the course is not free, it costs $50.
9. DATA SCIENCE AND MACHINE LEARNING WITH PYTHON – HANDS ON! ($35)
Frank Kane
If you’ve got some programming or scripting experience, Data Science and Machine Learning with Python – Hands On! will teach you the techniques used by real data scientists in the tech industry – and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice.
The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. It covers the machine learning and data mining techniques real employers are looking for, including:
Regression analysis
K-Means Clustering
Principal Component Analysis
Train/Test and cross validation
Bayesian Methods
Decision Trees and Random Forests
Multivariate Regression
Multi-Level Models
Support Vector Machines
Reinforcement Learning
Collaborative Filtering
K-Nearest Neighbor
Bias/Variance Tradeoff
Ensemble Learning
Term Frequency / Inverse Document Frequency
Experimental Design and A/B Tests
The course costs $35 and currently has an average user rating of 4.6/5.
10. MACHINE LEARNING FOR DATA SCIENCE AND ANALYTICS (FREE)
Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer, Mihalis Yannakakis, Peter Orbanz
Machine Learning for Data Science and Analytics is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. You will also examine why algorithms play an essential role in Big Data analysis.
In this course, you’ll learn:
What machine learning is and how it is related to statistics and data analysis
How machine learning uses computer algorithms to search for patterns in data
How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth
How to uncover hidden themes in large collections of documents using topic modeling
How to prepare data, deal with missing data and create custom data analysis solutions for different industries
Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming
The course is 5 weeks and requires a commitment of 7-10 hours per week. It is free, but you have the option of paying $99 for a verified certificate of completion.