23
Top 10 Machine Learning Courses and Certifications [2021 July]
According to the 2020 State of AI and Machine Learning report, the percentage of business executives responsible for machine learning initiatives increased from 39% to 70% between 2019 and 2020 alone!
This is the reason, along with the fact that Machine Learning is part of the Future of Work report by World Economic Forum, that our team has curated a list of top 10 choices of online Machine Learning courses that can take your career to next level.
Disclaimer: We may receive affiliate compensation for some of the links below at no cost to you.
In this exciting Professional Certificate program offered by Harvard University and Google TensorFlow, you will learn about the emerging field of Tiny Machine Learning (TinyML), its real-world applications, and the future possibilities of this transformative technology.
This first course in this series, Fundamentals of TinyML, will teach you the fundamentals of machine and deep learning. In this course, you will understand the language of tiny machine learning, which goes beyond the traditional machine learning toolkit due to the energy and memory constraints of tiny devices. The second course, Applications of TinyML, dives into an array of applications, where you will see how tools like voice recognition works in practice on small devices and you can see and implement common algorithms such as neural networks.
The third course, Deploying TinyML, will give you a chance to use an open-source hardware and prototyping platform to build your own tiny device. Featuring projects based on an Arduino board—TinyML Program Kit—the program emphasizes hands-on experience with training and deploying machine learning into tiny embedded devices.
This program is a collaboration between expert faculty at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS) and innovative members of Google’s TensorFlow team. Taught by Harvard Professor Vijay Janapa Reddi, Lead AI Advocate at Google, Laurence Moroney, and Technical Lead of Google’s TensorFlow and Micro team, Pete Warden, this course offers you the unique opportunity to learn from leaders in the AI and machine learning space.
Top 5 reasons we love this course
If Finance is what excites you, and Analytics is what you want to learn, this course is a near perfect fit for you!
In this professional certificate program, you will learn the key skills for constructing machine learning models, and using data to inform decisions. Whether you are a trader, financial analyst or programmer; whether your focus is on portfolio management or quantitative analytics, you will acquire the skills to apply both Classical Machine Learning and Neural Network/Deep Learning solutions to problems in finance. As important: you will learn a systematic approach to problem solving through data analysis, increasing your value in the emerging data-driven world. Combined with both theory and practical advice, you will be well positioned for solving the supervised and unsupervised learning tasks that will be critically important to all organizations.
Taught by Ken Perry, adjunct Professor of Tandon School of Engineering, New York University; Ken created the Risk department at Och Ziff and served as Chief Risk Officer for nearly 14 years.
The urgent demand for machine learning in finance is only going to grow. But the skills you will develop in this program are key to decision making in many other domains as well. Having these skills will enhance your value in many industries and will be invaluable to your career.
Top 5 reasons we love this course
In this class offered by Stanford University, more than 160,000 learners have studied the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) 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 instructor of the course Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer both in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world's largest MOOC platform.
Top 5 reasons we love this course
This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis.
This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning. You will follow along and code your own projects using some of the most relevant open-source frameworks and libraries.
Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.
Top 5 reasons we love this course
While it is true that working as a Machine Learning engineer does involve a lot of mathematics and programming, we believe that anyone can understand the basic concepts of Machine Learning, and given the importance of this technology, everyone should.
In this course you will learn to understand the basic idea of machine learning, even if you don't have any background in math or programming. Not only that, you will get hands on and use user friendly tools developed at Goldsmiths, University of London to actually do a machine learning project: training a computer to recognize images. This course is for a lot of different people. It could be a good first step into a technical career in Machine Learning, after all it is always better to start with the high-level concepts before the technical details, but it is also great if your role is non-technical.
Dr Marco Gillies is a Senior Lecturer in Computing at Goldsmiths, University of London. He is an expert on computer graphics, animation and new forms of interaction. He teaches on the BSc Computer Science and MA/MSc Virtual and Augmented Reality. He will be your instructor for this course. He has a rating of 4.78 out of 5.
You might be a manager or other non-technical role in a company that is considering using Machine Learning. You really need to understand this technology, and this course is a great place to get that understanding.
Top 5 reasons we love this course
Next on our list is this 4.5 out of 5-star course where you can learn Advanced Machine Learning with Google Cloud. Build production-ready machine learning models with TensorFlow on Google Cloud Platform.
This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text.
The 5-course specialization is taught by Google Cloud Training that has 1.3 million+ learners on Coursera alone.
Top 5 reasons we love this course
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
This Machine Learning course has a rating of 4.5 out of 5 and covers the whole gamut from 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 to Build an army of powerful Machine Learning models and know how to combine them to solve any problem.
The course will be taught by Kirill Eremenko, a Data Science management consultant with over five years of experience in finance, retail, transport and other industries; and Hadelin de Ponteves, the co-founder and CEO at BlueLife AI, which leverages the power of cutting-edge Artificial Intelligence to empower businesses to make massive profits by innovating, automating processes and maximizing efficiency.
Be ready for a super-duper learning experience with 45 sections that have 320 bite sized lectures to give you the skills you need to succeed.
Top 5 reasons we love this course
This is another excellent course on Udacity designed to teach you the basics of machine learning with TensorFlow, created in collaboration with Kaggle and AWS.
In this program, you’ll learn the foundational machine learning algorithms from scratch, including data cleaning, supervised, and unsupervised models. Then, you will move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects. The course forms a part of 2 nanodegree programs – Data Structures and Algorithms and Intel® Edge AI for IoT Developers.
This program is intended for students with experience in Python, who have not yet studied Machine Learning topics. With 11 experts delivering this course, needless to say that you will get the best of everything.
After finishing this nano degree program, you’ll receive a certificate of completion to share with employers and your LinkedIn profile.
Top 5 reasons we love this course
Interested? Sign Up Here
Learn foundational machine learning techniques -- from data manipulation to unsupervised and supervised algorithms.
This nano degree program is prepared to teach you the foundational machine learning techniques, including data manipulation, supervised, and unsupervised algorithms. It is ideally prepared for students who have experience in Python and want to expand their knowledge in other subjects of machine learning.
The curriculum is designed in collaboration with Kaggle and AWS and comes as a part of 2 nanodegree programs – Intro to Machine Learning with TensorFlow and Data Structures and Algorithms, which means you’ll be learning from the best instructors in the world with access to real-world projects from industry experts, technical mentor support, and career services that are additionally offered to help you improve your learning experience.
Top 5 reasons we love this course
Interested? Sign Up Here
Last but not the least on our list is this 4.9 rating course offered by deeplearning.ai on Coursera that has more than 600,000 enrollments and comes highly recommended by our team.
In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.
You will be taught by 3 instructors, all of them have Top Instructor badge on Coursera.
The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.
Top 5 reasons we love this course