Machine Learning is an application of Artificial Intelligence that focuses on the science of making machines and systems learn and improve from experiences as humans do, without being explicitly programmed. The process involves exposing machines (computers) to good quality data and use design algorithms to train them to look for patterns in the data and make predictions and decisions based on that data. These algorithms and programs are designed such that they automatically improve over time as they are fed more data.
Machine Learning has been getting a lot of attention in the past decade, and will rightly continue to as AI becomes more and more integrated into our everyday lives. From self-driving cars to online recommendation offers such as those on Netflix and Amazon to fraud detection, machine learning is being used widely in many applications. According to IDC estimates, the spending on AI and ML will grow to around $58 billion by the year 2024. Thus the number of jobs in machine learning will continue to rise.
Our team of experts has curated this list of 10 best machine learning and deep learning courses, certifications and trainings that have benefitted thousands of learners worldwide.
1. Machine Learning Specialization by Stanford University (Coursera)
This Machine Learning Certification offered by Stanford University and DeepLearning.ai through Coursera is hands down the best machine learning course available online. The most popular Coursera Machine Learning course offered by AI visionary Andrew Ng has been rebuilt and expanded into this ML specialization comprising of 3 courses, that teach foundational AI concepts through an intuitive visual approach, and introduce the code needed to implement the algorithms and the underlying math.
The original Machine Learning course was taken by over 4.8 million students and professionals and rated 4.9 out of 5 on Coursera. This new program has quickly gained traction and has already seen over 50K enrolments within a matter of weeks, with a stellar rating.
This program in Machine Learning teaches not only the theoretical underpinnings of effective machine learning techniques but also practical knowledge required to adapt and apply these techniques to new real-world challenges. Through a series of three courses, learners are provided 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.). Learners are also introduced to data mining and statistical pattern recognition. Basic understanding of linear algebra is needed for this specialization.
With a strong focus on applied learning, through the course of the program, participants are fully trained 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
- Build and train a neural network with TensorFlow
- Build and use decision trees and tree ensemble methods
- Generalize models to data and tasks in the real world
- Build recommender systems with a collaborative filtering approach and a content-based deep learning method
- Build a deep reinforcement learning model
This program for Machine Learning has been developed by world renowned expert Andrew Ng (Founder of Coursera and Professor of Computer Science at Stanford University; Also founding lead of Google Brain and Chief Scientist of AI operations at Baidu). It is ideal for those looking to break into AI or build a career in machine learning and is also a great way to refresh foundational ML concepts.
Key Highlights
- Learn Silicon Valley’s best practices in innovation in the field of Machine Learning and AI
- Learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications
- Gain Logistic Regression, Artificial Neural Networks skills. Implement your own neural network for digit recognition.
- Numerous case studies and applications for practical training and insights into solving real world problems
- Learn how to apply machine learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas
- Flexible deadlines and opportunity to learn at your own pace and schedule
- Dozens of code notebooks with code samples and interactive graphs to help you complete graded assignments
Duration : Approx 3 months, 9 hours per week
Rating : 4.9
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2. Deep Learning Certification by deeplearning.ai (Coursera)
This is the most sought after deep learning course by Stanford University Professors and is available on Coursera. It has been designed by globally acclaimed AI expert Andrew Ng with Stanford University lecturer’s Younes Bensouda Mourri and Kian Katanforoosh. Andrew Ng is the Co-founder of Coursera and professor of Computer Science at Stanford. He is also the founder and leader of Google Brain project and has led Baidu’s AI team of over 1300 people. This deep learning certification course has been taken by over 225,000 students online and enjoys a very high rating.
This program is split into 5 courses and teaches fundamentals of deep learning, how to build neural networks and implement machine learning projects to a completion. Topics include: Neural Networks and Deep Learning, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Structuring Machine Learning Projects, Convolutional Neural Networks and Sequence Models.
Coursera suggests around 11 hours of effort per week and approximately 3 months to complete the program at this pace. This course is taught in Python and learners are expected to have basic programming skills, plus a basic knowledge of linear algebra is recommended.
Key Highlights
- Learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization
- Work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing
- Interviews of many top leaders in Deep Learning
- Programming assignments to help you practice the ideas and techniques learnt
- Gain insights and career advice from best in the industry
- Rated as best Coursera deep learning certification
Duration : Approx 3 months, 11 hours per week
Rating : 4.9
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3. Machine Learning Nanodegree Program (Udacity)
A regular degree from a University has a few core courses, a few electives and some projects and takes around 4 years to complete. Udacity’s Nanodegree program is much like a regular university degree course in the sense that it also has some core and some elective parts, but the duration of a nanodegree program is much smaller, somewhere between 3 to 12 months and hence the name ‘nano’. Udacity offers several Nanodegree tracks, Machine Learning being a very popular one.
There are 2 programs to choose from based on your level of prior experience – Intro to Machine Learning and Machine Learning Engineer. If you are a beginner new to the world of Machine Learning, their Intro to Machine Learning Nanodegree program suits you best. It is an entry point to learn fundamental machine learning concepts such as data cleaning and supervised models. While if you already have some experience in this field, you can start with Machine Learning Engineer Nanodegree program that focuses on the latest in machine learning production and deployment technologies.
Intro to Machine Learning Nanodegree Program – To enrol for this program, you should have basic knowledge of Python programming, probability and statistics. You will learn foundational machine learning algorithms, data cleaning, supervised learning, unsupervised learning methods, deep learning including neural network design and training in PyTorch. There are several exercises and projects to test and apply the skills learnt.
Machine Learning Engineer Nanodegree Program – Udacity lists intermediate Python programming skills and knowledge of machine learning algorithms as prerequisites for this program. This Udacity machine learning nanodegree program will teach you advanced machine learning techniques focussed around how to package and deploy your models to a production environment. You will also learn to evaluate performance of your models including A/B testing and also how to update the models as they are fed more data over time. You will learn Amazon SageMaker for deployment of models to cloud. Several Machine Learning case studies are included. The program finishes with a Capstone Project.
Key Highlights
- Immersive content and real world projects from industry experts
- Learn popular frameworks like Sklearn, Tensorflow, and Keras
- In-lecture quizzes for practice
- Learn practical industry best practices to be well equipped in the job market
- 1-on-1 technical mentor who will answer your questions and guide your learning
- Access to career coaching services, interview prep advice from professionals and resume review
- Flexibility to learn at your own pace and schedule
- Student support community to exchange ideas and clarify doubts
Duration : 3 months, 10 hours per week
Rating : 4.8
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4. Machine Learning A-Z™: Hands-On Python & R in Data Science (Udemy)
This Machine Learning course by Udemy takes you step-by-step into the world of machine learning algorithms. It is extensive in terms of content and is taught in Python and R. The course is structured in a manner that learners of all levels are able to grasp the concepts with ease, making it suitable for both beginners and advanced learners. The course has been designed by two professional data scientists and AI experts Kirill Eremenko and Hadelin de Ponteves. It includes 285 video lectures (approx 41 hours), 31 articles and 5 downloadable resources. At the time of writing, more than 430,000 students have already enrolled for this program, lending great credibility to its content.
No special skills are needed to take this course. Basic knowledge of high school mathematics is sufficient. Learners with basic knowledge of machine learning can also enrol to explore the different fields of machine learning, learn advanced concepts and gain practical skills required in the industry.
This program teaches you how to do powerful analysis and make accurate predictions. You will also be able to make your own robust machine learning models. It covers the following in detail – (i) Data Preprocessing, (ii) Regression (Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression), (iii) Classification (Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification), (iv) Clustering (K-Means, Hierarchical Clustering), (v) Association Rule Learning (Apriori, Eclat), (vi) Reinforcement Learning (Upper Confidence Bound, Thompson Sampling), (vii) Natural Language Processing (Bag-of-words model and algorithms for NLP), (viii) Deep Learning (Artificial Neural Networks, Convolutional Neural Networks), (ix) Dimensionality Reduction (PCA, LDA, Kernel PCA), (x) Model Selection & Boosting (k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost).
Key Highlights
- Build powerful Machine Learning models and know how to combine them to solve any problem
- Know which Machine Learning model to choose for each type of problem
- Hands-on Practical and interactive exercises based on real life examples to learn building your own models
- Python and R code templates that you can download and use on your own projects
- Handle complex topics like Reinforcement Learning, NLP and Deep Learning
- Comprehensive Q&A Section that addresses most of the commonly encountered issues
Duration : 41 hours on-demand video, 31 articles, 5 downloadable resources
Rating : 4.5
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5. Professional Certificate in Deep Learning by IBM (edX)
This Deep Learning Certification program has been designed and developed by an expert team at IBM and is delivered on edX platform. It gets the learners ready to use new technologies in the fields of Machine Learning, Data Science and AI, thus helping to advance their careers well.
This deep learning specialization program is structured into 5 graduate-level courses and requires between 52 to 104 hours of total effort. It introduces learners to concepts and applications in Deep Learning, including various kinds of Neural Networks for supervised and unsupervised learning. It also teaches how to apply the skills by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow. The program finishes with a Capstone project where you’ll use either Keras or PyTorch to develop, train, and test a Deep Learning model to solve a real world problem.
Key Highlights
- Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders
- Master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision
- Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, text analytics, Natural Language Processing, recommender systems, and other types of classifiers
- Series of hands-on labs, assignments, and projects inspired by real world challenges and data sets from the industry
Duration : 5 courses, 5 to 6 weeks per course, 2 – 4 hours per week
Rating : 4.6
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6. Machine Learning Specialization by University of Washington (Coursera)
This is an intermediate level specialization program developed by two leading researchers at the University of Washington – Carlos Guestrin (Computer Science and Engineering) and Emily Fox (Statistics), both Amazon Professors of Machine Learning. It focuses on major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval through a series of practical case studies. You will learn the necessary skills for using the machine learning techniques to solve complex real-world problems, by identifying the right method for your task and implementing the right algorithm successfully.
It is a very comprehensive Machine Learning training program that comprises of 4 courses spread over multiple weeks. A learner is expected to put in around 6 hours of effort per week to complete the program in approx 8 months time. Most assignments in this specialization make use of Python programming language. Some knowledge of mathematics and experience with computer programming are listed as prerequisites to take this course.
Key Highlights
- Analyze large and complex datasets, create systems that adapt and improve over time
- Learn Data Clustering and Classification algorithms
- Handle very large sets of features and select between models of various complexity
- Build intelligent applications that can make predictions from data
- Learn to deploy your solution as a service
- Practical case studies and programming assignments
Duration : 4 courses, Flexible Schedule, 6 hours per week
Rating : 4.8
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7. Mathematics for Machine Learning Specialization by Imperial College London (Coursera)
Mathematics is the most important foundation block of Machine Learning. Without the working knowledge of machine learning mathematics, it is very difficult to understand the concepts underlying Python/R APIs. One cannot easily relate the mathematics taught at school and university level to the way it is used in data science. This specialization course on Mathematics for machine Learning bridges that gap, getting learners up to speed in developing an intuitive understanding of mathematics and how it relates to machine learning and data science.
This specialization has 3 courses with each course spanning 4-6 weeks.
i. First course is on Linear Algebra which looks at what linear algebra is and how it relates to data science. This also teaches vectors and matrices and how to work with them to solve problems. Towards the end of the course, you’ll write code blocks and encounter Jupyter notebooks in Python. This course also has famous Google’s Page Ranking algorithm exercise to demonstrate how vectors and matrices are important to the field of machine learning.
ii. The second course is on Multivariate Calculus which teaches 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. This course helps in developing the concepts of calculus with respect to the higher dimensional graphs, and how to use them to find optimal solutions.
iii. The third course is on Dimensionality Reduction with Principal Component Analysis, and uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and requires basic knowledge of Python and numPy.
Key Highlights
- Gain prerequisite mathematical knowledge to pursue advanced courses in machine learning
- Graded assignments with peer feedback
- High school maths knowledge needed. Basic Python skills are an added advantage.
- Flexible schedule and self-paced learning
Duration : 3 courses, approx 2 months, 12 hours per week
Rating : 4.6
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8. Advanced Machine Learning Specialization by HSE (Coursera)
This Machine Learning Specialization program by HSE program contains advanced series of 7 courses that are devoted to popular machine learning fields and also courses that are devoted to filling the gap between theory and practice. You will learn how to build high rank solutions with focus on practical usage of machine learning. The courses cover the following – Introduction to Deep Learning, Bayesian methods and their application, Practical Reinforcement learning, Deep learning in Computer Vision, Natural language Processing and Addressing Large Hadron Collider (LHC) challenges. Once you complete all courses and finish the programming assignment and hands-on project, you will be awarded your certificate.
This machine learning certification has been developed by a team of 21 lecturers and professors including top Kaggle machine learning practitioners and CERN scientists who share their valuable experience of solving real world problems with ML. It is very well structured for deriving the maximum benefit. Due to its advanced nature, you need to have basic or intermediate knowledge of Machine learning, Probability theory, Linear algebra and calculus, and Python programming to enrol for this specialization course. So it is suggested that you take a beginners machine learning course first and brush on your maths and then move on to this course to fill out the rest of your learning around the subject.
Key Highlights
- Use modern deep neural networks for various machine learning problems with complex input
- Participate in data science competitions and use the most popular and effective machine learning tools
- Adopt the best practices of data exploration, preprocessing and feature engineering
- Perform Bayesian inference, understand Bayesian Neural Networks and Variational Autoencoders
- Use reinforcement learning methods to build agents for games and other environments
- Solve computer vision problems with a combination of deep models and classical computer vision algorithms
- Outline state-of-the-art techniques for natural language tasks, such as sentiment analysis, semantic slot filling, summarization, topics detection, and many others
- Build goal-oriented dialogue agents and train them to hold a human-like conversation
- Understand limitations of standard machine learning methods and design new algorithms for new tasks
Duration : 7 courses, Flexible Schedule
Rating : 4.6
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9. Deep Learning A-Z™: Hands-On Artificial Neural Networks (Udemy)
If you really want to master Deep Learning for image recognition, stock trading, business analytics and more, this course is for you. It has been designed by 2 machine learning and data science experts Kirill Eremenco and Hadelin De Pontves and is unarguably one of the best courses on deep learning out there. It aims at providing the most cutting edge deep learning models and techniques and also the knowledge essential to understanding the intuition for the concepts behind deep learning algorithms.
Anyone with knowledge of high-school level mathematics and basic Python programming skills can enrol for this program. It has a very robust structure with tutorials grouped into 2 volumes representing the two fundamental branches of deep learning – Supervised Deep Learning and Unsupervised Deep Learning (with each volume further focussing on three distinct algorithms). This course naturally extends into data science career with practical tutorials, hands-on-coding exercises and 6 projects to solve real world problems. Learners also get the best in-course support, with promise that queries will be answered within max 48 hours by a team of professionals.
Key Highlights
- Work on real world datasets and design algorithms to solve real world challenges
- Learn the most popular open-source libraries Tensorflow and Pytorch and understand which one to use in certain circumstances
- Learn other libraries like Theano, Keras and Scikit-Learn
- Learn to evaluate the performance of our models (with most relevant technique, k-Fold Cross Validation) and improve them with effective Parameter Tuning
- Understand the intuition behind Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks
- Understand Self-Organizing Maps and apply them in practice
- Understand Boltzmann Machines and effectively apply them in practice
- Learn about Stacked autoencoders technique and how to use it
- Work on six real world case studies with updated datasets – Churn Modelling Problem, Image Recognition, Stock Price Prediction, Fraud Detection, Recommender Systems (like Amazon product suggestions and Netflix movie recommendations)
Duration : 22.5 hours on-demand video
Rating : 4.6
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10. Machine Learning with Python by IBM (Coursera)
This Machine Learning Certification by IBM is an intermediate level course that focuses on the fundamentals of Machine Learning using the programming language Python. You will learn how machine learning is used in many key fields in the industry. The course is structured as 2 main components – first delves into the purpose of machine learning and application of the concepts to the real world, second delves deeper into the techniques like supervised and unsupervised learning, model evaluation and machine learning algorithms. At the end of the course you will need to submit a project to demonstrate your learning from the course.
This course is a part of two specializations – IBM Artificial Intelligence Professional Certificate and IBM Data Science Professional Certificate. So when you complete this course, it will be counted as a step towards your progress in any of these specializations.
Key Highlights
- Learn to use various libraries to build machine learning models, like Scikit Learn
- Built-in lab environment (Jupyter notebook) with sample code
- Learn Regression, Classification, Clustering, Recommender Systems, SciPy
- Practice with different classification algorithms, such as KNN, Decision Trees,
Logistic Regression and SVM - Work on real world projects including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more
Duration : Approx 12 hours
Rating : 4.7
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