One of the more accessible resources to get a true sense into machine learning is Google’s Machine Learning Crash Course. It has short lessons and interactive visualizations with hands-on exercises to make things real, rather than just read about! It’s helpful for developers, analysts and beginners who don’t want to go through the thick mathematical material to get in.

You’ll learn: Supervised learning, regression, classification, numerical and categorical data, overfitting, neural networks and embeddings, production ML and responsible AI.

Appropriate for learners who have an introduction to programming and math experience.

What we like about it: It is a very good starting point that may be free, as it combines clear explanations with interactive practice.

Kaggle’s introduction course provides a working model in a timely manner. But instead of getting caught up in theory, it guides you through the process for a beginner: Examine your data, select features, train a model, and determine whether the model makes any predictions that are useful. A great appetizer before the main course sample of machine learning!

You will learn: How ML models work, data exploration, features, decision trees, model validation, underfitting, overfitting and random forests.

For: Students, analysts and Python newbies seeking to create their first ML model.

Why this is our recommendation: It’s short, hands-on, and provides a natural progression into Kaggle’s intermediate content.

This course builds on the introduction course, and is intended to take students into messier, more realistic data sets. You will experience the common issues that come with real projects such as missing values, categorical variables, data leakage, learn how to deal with them, pipelines, cross-validation and boosting techniques like XGBoost.

They will learn: Handling missing values, categorical data, pipelines, cross validation, XGBoost and data leakage.

Audience: This course is for individuals having some knowledge of supervised learning, who wish to improve the quality of their models.

It is recommended due to its frequent appearance in real ML projects in the data-prep and validation skills.

Feature engineering is the art of making features out of raw, messy data to make the data usable for a model. This course focuses on that skill and demonstrates how improved features become evident from surface patterns of a model.

Knowledge to be gained: Feature creation, Feature transformations, Categorical representation, Numerical features and Improving models practically.

Best suited for: Learners that have already learned how to train basic ML models and are looking for sharper predictions.

This time is well spent as good feature engineering can move the needle more than changing algorithms.

Kaggle has created this course for you to learn from the experts.Kaggle has designed this course for you to learn from the experts in about 4 hours.

This course is an introduction to deep learning and neural networks; you will learn them through examples that you can run. You will learn to use TensorFlow and Keras, understand the behavior of neurons, the training of a network, and how using dropout and batch normalization stabilizes training. Once you get used to classical ML, it’s a logical progression.

What you’ll do: Neurons, neural networks, stochastic gradient descent, overfitting, dropout, batch normalization and TensorFlow/Keras.

It is suitable for: ML learners who are looking to progress from basic models to deep learning.

Why we like it: It is easy, full of code and will get you started with neural networks in no time.

This will be a self-paced course hosted by Kaggle Learn.This will be an intermediate-level, self-paced course on Kaggle Learn.

This course is mainly concerned with image machine learning. You’ll go through the convolutional neural network and how deep learning models learn patterns and relate to visual data which will be very useful for image classification, object recognition or visual inspection.

The skills you will acquire: CNNs, classification of images, image augmentation, model training and TensorFlow/Keras.

It is for all those who are interested in learning about the usage of images and video in relation to AI.

Our recommendation: It provides you with a targeted introduction to computer vision without having to start with an entire deep learning course.

Time-dependent data is different from a regular spreadsheet, and this course goes through concepts of forecasting developed specifically for time-dependent data. A decent option for business or any time you have to foretell what is going to happen next.

The following topics will be covered: Time series features, forecasting, trends, seasonality and testing of models.

Audience: Data analysts, business users and ML learners who are interested in forecasting.

Why we recommend it: It does not present a specialization based on theory, but rather on examples.

Learn how to use the Seaborn library to create graphs from data.Learn how to use Seaborn library for creating graphs from data.

A model may be accurate, and yet be a “black box. In this course you learn how to extract it, how and which features are contributing to predictions, and how its relevance can be discovered as soon as the models start influencing the decision making process in the business, finance or healthcare world.

This course teaches you about Feature importance, Permutation importance, Partial dependence and model interpretation.

Why we recommend it: It pushes you past accuracy metrics into thinking about how a model actually behaves.

Machine learning isn’t only about getting the numbers right. This course is about the ethical aspects of building and deploying models, fairness and bias, responsibility, and complements any technical ML course taken concurrently with it.

You will learn: AI ethics, fairness, responsible AI and practical ethical considerations.

Audience: programmers or not, anyone learning AI or ML.

Why we like it: It adds a responsible AI touch that can easily be overlooked when considering the technical aspects.

For over a decade, MIT OpenCourseWare has made its MIT courses available for free online, allowing students to listen to lectures, view and download lecture notes, and take exams.

MIT6.036 is a demanding course in machine learning, designed for those who seek to not only learn what algorithms are, but how they work. It includes modelling, prediction, representation, generalization and some of the core learning paradigms and would require more mathematical and programming proficiency than a platform meant for the beginner.

What you’ll learn: Supervised learning, classification, regression, representation, overfitting, generalization, clustering and reinforcement learning.

It is recommended for: University students, engineers and technically minded self-learners.

Why we strongly recommend it: It is much more in-depth than most short introductory courses.

MIT’s 6.S191 is an introduction to modern deep learning with lectures, labs and real applications, including neural networks for computer vision, natural language processing and biological applications. Once you are beyond classical ML, it’s a smart step.

This course will cover: Deep neural networks, computer vision, NLP, generative models, sequence models and deep learning applications.

Target audience: Students with some programming and basic ML knowledge.

Why we recommend it: It combines solid content with practical laboratories and up-to-date practical applications of deep learning.

Time is limited to support each participant.The time will be limited so that each participant can receive support in a lecture-based presentation.

This is a highly theoretical course that has the reputation for it. Explores the mathematics underlying the ability of learning algorithms to learn from training data and generalize to examples they have not seen before. It is a good choice for further studying advanced ML concepts such as VC dimension, bias-variance tradeoff, regularization and support vector machines.

What you will get out of it: Understanding learning theory, generalization, VC dimension, bias-variance, linear models, SVMs, neural networks and regularization.

Audience: Students with intermediate knowledge of probability and math.

Why we recommend it: One of the best to understand the theory behind ML algorithms rather than relying on them.

Unlike a theory class, such as Caltech’s, fast.ai starts with building something useful immediately. Early you’ll be creating working applications, rather than deep learning as just a mathematical problem, and you’ll be learning about a variety of data types and modern techniques.

You will learn: Neural networks, computer vision, NLP, tabular data, transfer learning, training and deployment.

Who should take it: Python programmers who’d rather learn by building than by reading theory first.

Why it’s recommended: With a project-first approach, you can acquire skills and develop a portfolio simultaneously.

Machine learning projects often fail because the problem is not defined correctly before development begins. Machine Learning Problem Framing from Google teaches learners how to translate real-world business and technical problems into well-defined machine learning problems. Instead of focusing only on algorithms, the course explains how to decide whether machine learning is appropriate, define the desired outcome, identify useful data and establish measurable objectives. This makes it a useful step between introductory machine learning concepts and practical model development. The course is particularly relevant for learners who already understand basic ML terminology and want to understand how machine learning is applied to real-world problems.

What you’ll learn: Problem framing, defining ML objectives, identifying useful data, selecting appropriate outcomes and evaluating whether ML is suitable for a problem.

Target audience: Beginners and intermediate learners who want to understand how real-world problems are converted into machine learning tasks.

Why it is useful: It develops the planning and problem-definition skills needed before building an ML model.

Decision Forests is a Google machine learning course that brings practical applications and decision tree models to the forefront. Decision forests are an alternative to neural-network approaches and are convenient for many classification, regression and ranking problems. This course will give students an introduction to the basic concepts behind decision forests and how they can be applied to various machine learning problems. When learners know a bit about supervised learning, it is a helpful exercise to explore other model families. Students will also be able to recognize the reasons for using different algorithms for various datasets and tasks.

Topics covered: Decision Trees, Decision Forests, Classification, Regression, Ranking and applications in real life.

Audience: Intermediate ML learners with some basic ML concepts and interested in experimenting with other model architectures.

Why it’s useful: It helps you to learn beyond simple regression and neural network models, and adds another set of useful ML algorithms.

The IBM course is very practical, and it is closely related to real-life ML applications, along with the most popular supervised and unsupervised methods, and the use of Python libraries to train and evaluate models. Helpful when transitioning from general programming to data science.

The goal of the learning is to learn classification, regression, clustering, Python machine learning libraries and model evaluation.

Aimed at: Python developers and future data scientists.

Why we like it so much: It introduces Python coding into the workflow of ML tasks, instead of teaching Python in isolation.

This course is about the two fundamental ideas of supervised learning – predicting numbers, predicting categories – and the math and intuition behind regression and classification in an accessible way.

What you’ll get to learn: Linear regression, logistic regression, classification, cost functions, gradient descent and model evaluation.

It is recommended for beginners who wish to learn and practice more complex algorithms after starting with some basic ones.

It’s used in almost every learning path in ML, so it’s a good starting point to be used repeatedly

This course builds on an introductory supervised learning course, and introduces neural networks and decision tree techniques and shows examples of their use in prediction problems.

What you will learn: How the neural network and decision trees work as well as advanced supervised learning techniques.

Who should take it: If you have done a basic supervised ML course.

Why we’re recommending it because it’s a built from the ground up and this goes towards more capable models.

In addition to supervised prediction, the course introduces three areas of research on prediction that go beyond basic supervised prediction: Prediction in unlabelled data, Recommendation systems, and Learning from interacting with an environment.

What you will get to know: Clustering, anomalous detection, recommender systems and reinforcement learning.

Target Language Learners: Intermediate learners who are looking to explore wider ML applications.

Why we recommend it: It covers some of the important areas that are not covered in beginner courses.

One of the oldest university level machine learning courses is CS229 and is appropriate for students who have a strong mathematical and programming background. It has a wide range of ML methods, and connects them to math.

You will learn: supervised learning, unsupervised learning, learning theory, generative models and optimization.

Designed for: advanced students, engineers and researchers.

Why we recommend it: It is a valuable resource for those seeking to gain a deep understanding of academic ML.

Introduction to NLP using machine learning from Kaggle Learn teaches basic natural language processing (NLP) techniques for machine learning. This course is helpful for students with some prior knowledge of Python and machine learning, who want to use these skills for text data analysis. It presents basic NLP concepts and offers some exercises for learners to work with the language data sets. Some of the applications of NLP include sentiment analysis, text classification, information extraction and language-based search. It will present a hands-on introduction to the course before diving into more in-depth subjects of natural language processing (NLP) or large language models (LLM).

This course will cover: Text Processing, Natural Language Data, Natural Language Processing techniques, Machine Learning practical approach for text.

Audience: Python and machine learning learners looking to get started with natural language data.

It will be useful because: It gives you a great machine learning specialization and hands-on experience with NLP problems.

Intermediate Natural Language Processing extends basic NLP concepts and introduces students to more advanced NLP methods of processing and understanding texts. The course is geared towards those who have some experience using Python, machine learning and basic NLP (natural language processing). It offers a chance to utilize more advanced NLP techniques and grasp the potential of using machine learning in more complex language tasks. The course may be especially beneficial for students who want to explore the real-world applications of NLP, such as text classification and language understanding. It is also an ideal follow up course for anyone who has taken a beginner’s NLP course and wishes to further enhance their practical skills.

The following is what you will learn: Advanced NLP concepts, Text Processing techniques, Text based machine learning work flow and NLP applications.

Audience: Intermediate level of mastering the language, with basic knowledge of Python and NLP.

Why it is useful: It constructs a progression of learning from basic NLP ideas to more complex language-processing applications.

Model Evaluation is an important aspect of machine learning because it determines if a trained model is performing well or not, and if its predictions are trustworthy. Selecting the methods for evaluating models is also crucial as much as model building in an ML workflow. This course allows students to learn how to evaluate the machine learning models with suitable metrics and validation methods. It is particularly beneficial after finishing introductory supervised learning courses since it links the training of the models with immediate evaluation. These concepts can be applied by the learner when comparing different models, finding weaknesses and enhancing their machine learning processes.

You will learn about: Model evaluation techniques, performance metrics, validation and comparison approaches of machine learning models.

Audience: Intermediate students with some knowledge of basic supervised learning and looking to enhance their model assessment abilities.

Why it’s useful: When evaluating, learners can determine if a model is useful or not, not just because it makes predictions.

Personalized Recommendations using Machine Learning: Introduces the machine learning techniques used to make personalized recommendations. Recommendation systems are widely used in e-commerce, video streaming, music discovery and many more. This Google course introduces you to how a recommendation system works, and introduces machine learning to identify relevant items for every user. It can be a helpful specialization for anyone who is familiar with the basics of machine learning and wishes to explore a particular application area. The course may also allow students to understand how machine learning models are used in products that need to predict in a personalised manner on a massive scale.

The objectives: to learn about recommendation systems, personalised prediction, recommendation techniques and machine learning to make relevant recommendations.

This is for intermediate machine learning learners with an interest in practical application and technologies like personalized recommender systems.

Why it’s useful: Recommendation systems are a valuable real-world application of machine learning and are a useful specialization once you understand the fundamental concepts of machine learning.

Introduction to Machine Learning Concepts is a beginner’s course in machine learning from Microsoft Learn that develops a solid understanding of machine learning concepts. It focuses on basic principles that underpin today’s ML systems, without needing students to start at an advanced level in maths or coding. The module describes the various forms of machine learning and provides an introduction of training and evaluating models. It also explains regression, binary and multi-class classification, clustering and the correlation between machine learning and deep learning. The module consists of 11 units and will take around 1 hour and 33 minutes. It is also a valuable resource for students who intend to learn more about the development process of machine learning models before enrolling in more advanced courses. Some elementary mathematical background is desirable, but not necessary, especially in ML.

For beginners, aspiring Data Scientists, AI learners and professionals who want to know the basics of ML.

It is a structured introduction to key concepts of ML from Microsoft that are ideal for starting with before more advanced training.

Machine Learning for Beginners is a free, open-source 26-lesson Microsoft learning series. It is an introduction to classical machine learning with examples and exercises in programming. Students will learn about key concepts like regression and classification and also be introduced to various machine learning tools such as Python, NumPy, Pandas, Matplotlib, Scikit-learn, and Jupyter Notebooks. It is intended for those who are brand new to machine learning and others who are looking to brush up on the classical machine learning concepts. It is not all theory; concepts are related to coding and model-building exercises. This makes it ideal for a learner who wants to work his way up from having to understand the terminology of ML into the basics of implementing simple machine learning models.

If you are a beginner interested in a hands-on introduction to machine learning, the classical way, via Python.

It has an integrated theory and coding experience and offers a full beginners’ course and not just a quick introduction.