Best Machine Learning Courses

Machine Learning Specialization – Stanford/Deeplearning.ai

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. 

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. 

• Supervised Machine Learning: Regression and Classification
• Advanced Learning Algorithms
• Unsupervised Learning, Recommenders, Reinforcement Learning

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

By the end of this Specialization, you will be ready to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
• Build and train a neural network with TensorFlow to perform multi-class classification.
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.

Rating: 4.9 | Course Duration: 2months | Level: Beginner

Instructor: Andrew ng | Provider: Coursera | Course Fee: Free for Audit ($49 for Certificate Paid)

IBM Machine Learning Professional Certificate

Prepare for a career in the field of machine learning. In this program, you’ll learn in-demand skills like AI and Machine Learning to get job-ready in less than 3 months.

This program consists of courses that provide you with a solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning. Topics covered include Supervised and Unsupervised learning, Regression, Classification, Clustering, Deep learning and Reinforcement learning.

You will follow along and code your own projects using some of the most relevant open-source frameworks and libraries, and you will apply what you have learned in various courses by completing a final capstone project. 

Upon completion, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You’ll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support.

This Professional Certificate has a strong emphasis on developing the real-world skills that help you advance a career in Machine Learning and Deep Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments, and projects that will provide you with practical skills to use on Machine Learning jobs.

In this course you will learn algorithms like Supervised and Unsupervised learning, Regression, Classification, Clustering, Linear Regression, Ridge Regression, Machine Learning (ML) Algorithms, Decision Tree, Ensemble Learning, Survival Analysis, K-means clustering, DBSCAN, Dimensionality Reduction.

Rating: 4.6 | Course Duration: 3 months | Level: Intermediate

Instructor: IBM | Provider: IBM | Course Fee: Free for Audit ($49 for Certificate Paid)

Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] – Udemy

This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

This course can be completed by either doing either the Python tutorials, or R tutorials, or both – Python & R. Pick the programming language that you need for your career.

This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:

• Part 1 – Data Preprocessing
• Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
• Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
• Part 4 – Clustering: K-Means, Hierarchical Clustering
• Part 5 – Association Rule Learning: Apriori, Eclat
• Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
• Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
• Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
• Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And last but not least, this course includes both Python and R code templates which you can download and use on your own projects.

• Master Machine Learning on Python & R
• Have a great intuition of many Machine Learning models
• Make accurate predictions
• Make powerful analysis
• Make robust Machine Learning models
• Create strong added value to your business
• Use Machine Learning for personal purpose
• Handle specific topics like Reinforcement Learning, NLP and Deep Learning
• Handle advanced techniques like Dimensionality Reduction
• Know which Machine Learning model to choose for each type of problem
• Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Course Duration: 42.5hrs | Rating: 4.5 | Provider: Udemy

Coding Exercises: 5 | Total Articles: 41 | Downloadable Resources: 10

IBM: Machine Learning with Python: A Practical Introduction – edX

This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

You’ll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

You’ll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error and Random Forests.

Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

• Explain the difference between the two main types of machine learning methods: supervised and unsupervised
• Describe Supervised learning algorithms, including classification and regression
• Describe Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
• Explain how statistical modelling relates to machine learning and how to compare them
• Discuss real-life examples of the different ways machine learning affects society
• Build a prediction model using classification

Course Duration: 5 weeks | Provider: IBM | Course Fee: Free for Audit

IBM Machine Learning with Python

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more.

You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN.

With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

• Describe the various types of Machine Learning algorithms and when to use them 
• Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression 
• Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 
• Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics 

Rating: 4.7 | Course Duration: 13 hours | Level: Intermediate

Instructor: IBM | Provider: IBM | Course Fee: Free for Audit ($49 for Certificate Paid)

Machine Learning Fundamentals in Python – Data Camp

Discover the machine learning fundamentals and explore how machine learning is changing the world. Join the ML revolution today! If you’re new to the discipline, this is an ideal place to start. You’ll cover the machine learning basics with Python, starting with supervised learning with the scikit-learn library.

You’ll also learn how to cluster, transform, visualize, and extra insights from data using unsupervised learning and scipy. As you progress, you’ll explore the fundamentals of neural networks and deep learning models using PyTorch.

You’ll finish the track by covering reinforcement learning, solving a myriad of problems as you go! By the time you’re finished, you’ll understand the essential machine learning concepts and be able to apply the fundamentals of machine learning with Python.

Course Duration 16 hours | Total Courses: 4 | Total Projects:3

Python for Data Science and Machine Learning Bootcamp – Udemy

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We’ll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning:

• Programming with Python
• NumPy with Python
• Using pandas Data Frames to solve complex tasks
• Use pandas to handle Excel Files
• Web scraping with python
• Connect Python to SQL
• Use matplotlib and seaborn for data visualizations
• Use plotly for interactive visualizations
• Machine Learning with SciKit Learn, including:
• Linear Regression
• K Nearest Neighbors
• K Means Clustering
• Decision Trees
• Random Forests
• Natural Language Processing
• Neural Nets and Deep Learning
• Support Vector Machines

Rating: 4.6 | Provider: Udemy | Level: Beginners

Course Duration: 25hrs | Total Articles:13 | Total Downloadable Resources: 5

HarvardX: Data Science: Machine Learning – edx

In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

• 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?
• Machine Learning Algorithms, Recommender Systems, Algorithms, Machine Learning, Forecasting, Data Science, Principal Component Analysis, Speech Recognition

Course Duration: 8 weeks | Provider: HarvardX | Course Fee: Free for Audit

Mathematics for Machine Learning Specialization

This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.

At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.

Rating: 4.7 | Course Duration: 2 months | Level: Intermediate

Instructor: University of Washington | Provider: UoW | Course Fee: Free for Audit ($49 for Certificate Paid)

Other Machine Learning Courses on Coursera: