10 Best Machine Learning Courses in 2020
If you are ready to take your career in machine learning to the next level, then these top 10 Machine Learning Courses covering both practical and theoretical work will help you excel.
By Ahmad Bin Shafiq, Machine Learning Student.
Practical/Hands-on Courses with Less Theory
Taught by: One of the most famous and practical courses on the internet, taught by Jeremy Howard, Research Scientist at the University of San Francisco, the chair of WAMRI, and is Chief Scientist at the platform.ai. He was the President and Chief Scientist of the data science platform Kaggle, where he was the top-ranked participant in international machine learning competitions 2 years running.
Course Outcomes: This course is a hands-on introduction to deep learning, where you will dive straight into deep learning via making a state of the art classifier. You will learn a lot of practical aspects of deep learning without knowing the underlying theory.
Taught by: Rachel Thomas is an American computer scientist and founding Director of the Center for Applied Data Ethics at the University of San Francisco. Together with Jeremy Howard, she is co-founder of fast.ai.
Course Outcomes: This course is a hands-on introduction to NLP, where you will code a practical NLP application first as the name suggests, then slowly start digging inside the underlying theory in it.
Applications covered include topic modeling, classification (identifying whether the sentiment of a review is positive or negative), language modeling, and translation. The course teaches a blend of traditional NLP topics (including regex, SVD, naïve Bayes, tokenization) and recent neural network approaches (including RNNs, seq2seq, attention, and the transformer architecture), as well as addressing urgent ethical issues, such as bias and disinformation.
Price: $129 (on sale $10-$20)
Taught by: Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies.
Course Outcomes: This course is a very practical introduction to Machine Learning and data science. It does not assume any previous knowledge, starts from teaching basic Python to Numpy Pandas, then goes to teach Machine Learning via sci-kit learn in Python, then jumps to NLP and Tensorflow, and some big-data via spark.
This is definitely one of the best courses out there, as Jose is a really good instructor.
Taught by: Laurence Moroney is a Developer Advocate at Google working on Artificial Intelligence with TensorFlow. He is also the author of many books.
Course Outcomes: In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. Lawrence will start teaching you the basics of TensorFlow, slowly progressing towards the state of the art applications using Tensorflow.
Price: $25/month or $300/year
Taught by: Multiple industry professionals
Course Outcomes: With no prior coding experience, you will be taught coding from scratch, then moving to advanced libraries and frameworks. Each lesson is accompanied by some exercises or tasks. You will also have access to projects at data camp, which will improve your coding experience as well as your resume.
Theoretical Courses with Less Practical work
Taught by: Andrew Ng is CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist, Baidu, and founding lead of Google Brain.
Course Outcomes: You will learn all the underlying theory behind famous machine learning algorithms, from Supervised Learning to Unsupervised Learning. You will also get a chance to code them from scratch in MATLAB/Octave.
Instructor: Andrew Ng
Course Outcomes: This 5 parts specialization will teach you the underlying theory behind of Deep Learning from Single Layer Network to Multi-Layer Dense Networks, from the basics of CNN to performing object detection with YOLO along with underlying theory, from basics of RNN to Sentiment analysis.
This course will also give you an introduction to the basics of Deep Learning frameworks such as Tensorflow or Keras.
Taught by: Andrej Karpathy, the Sr. Director of AI at Tesla, leads the team responsible for all neural networks on the Autopilot. Previously, he was a Research Scientist at OpenAI working on Deep Learning in Computer Vision, Generative Modeling, and Reinforcement Learning. He received his Ph.D. from Stanford University.
Course Outcomes: This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. Students will learn to implement, train, and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The focus is on teaching how to set up the problem of image recognition, the learning algorithms (e.g., backpropagation), practical engineering tricks for training, and fine-tuning the networks.
Taught by: Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research.
Course Outcomes: You will learn all the underlying theory of famous Machine Learning Algorithms from Neural Networks to supervised and Unsupervised Learning.
This course is originally taught at the University of Wisconsin-Madison by Dr. Sebastian.
Taught by: Ava Soleimany is a Ph.D. student in the Harvard Biophysics program and at MIT, where she works with Sangeeta Bhatia at the Koch Institute for Integrative Cancer Research and am supported by the NSF Graduate Research Fellowship.
Alexander Amini is a Ph.D. student at MIT, in the Computer Science and Artificial Intelligence Laboratory (CSAIL), with Prof. Daniela Rus. He is an NSF Fellow and completed my Bachelor of Science and Master of Science in Electrical Engineering and Computer Science at MIT, with a minor in Mathematics.
Course Outcomes: 6.S191 is MIT’s official introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms.
Students will also get practical experience in building neural networks in TensorFlow.
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