8 Best Machine Learning Courses for 2020 1. Learning a highly technical field such as machine learning will not be easy, but with the right mindset, it’s more than possible. Through trial and error, exploration and feedback, you’ll discover how to experiment with different techniques, how to measure results, and how to classify or make predictions. The course covers topic like Regression in machine learning, how to Improve Supervised Models, non-linear modelling, Clustering, and Recommender systems. In the MIT tradition, you will learn by doing. – Gain best practices and advice from the instructor. There is no other course like this. With each module you’ll get a chance to spool up an interactive Jupyter notebook in your browser to work through the new concepts you just learned. To immerse yourself and learn ML as fast and comprehensively as possible, I believe you should also seek out various books in addition to your online learning. Created by Kirill Eremenko and Hadelin de Ponteves, this is one of the Best Deep Learning Course that you will find out there. Udacity’s advanced machine learning online course, “Machine Learning Engineer”, covers the following topics: In addition, the two courses will teach you how to create real-life projects in the field of machine learning. In this four-course specialization, you will primarily focus on applications of the Machine Learning to various practical problems in Finance. If you’ve been interested in reading a textbook, like Machine Learning: A Probabilistic Perspective — which is one of the most recommended data science books in Master’s programs — then this course would be a fantastic complement. Specialists in machine learning are becoming highly sought after, with big tech companies offering generous salaries to any trained professional willing to join their team. Overview of AI and Machine Learning Engineering Stack, Data Wrangling at Scale and Statistics for AI, Recommendation engines and time series modeling, Statistical and heuristic aspects of machine learning, Various Python-based machine learning models, Differentiating the different big data and machine learning toolsets offered by Google, Migrating existing services over to the Google Cloud Platform, Employing BigQuery and Cloud Datalab for interactive data analysis, Differentiating the different data processing services of the Google Cloud Platform, Supervised & Unsupervised Machine Learning, Linear Regression & Regularized Linear Regression, Probabilistic & non-probabilistic viewpoints, Optimization & inference algorithms for model learning, You’ve been looking for a way to get into machine learning, without any prior knowledge in the field. However, all of the content covered by the course is highly valuable, and any budding machine learning enthusiast will learn something new from the material in this program. Their concise introduction to various machine learning terms is beneficial for beginners. The case studies covered include Trends in World Health and Economics, US Crime Rates, the Financial Crisis of 2007-2008, election Forecasting, Building a Baseball Team and Movie Recommendation Systems. Machine learning is incredibly fun and interesting to learn and experiment with, and I hope you found a course above that fits your own journey into this exciting field. You will also create deep learning models in many different fields like autonomous driving, healthcare, natural language processing, music generation, etc. – The first module is available for a free preview. This is one of the best machine learning online course you can get your hands on this year, thanks to its thoughtfully curated syllabus and well put course materials. Fundamentals of statistics that are necessary to bring out the underlying hidden potential information from the dataset. The course also gives learners a quick overview of the more advanced concepts of machine learning such as building multi-class neural networks. – Get introduced to the fundamental concepts of data science, such as working with different data types and operations in Python, writing functions in Python, data manipulation and analysis, data visualization, and much more. Uses Python to introduce participants with popular algorithms such as random forest and k-mean. 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This interactive course offered by Google Cloud and New York Institute of Finance, aims to equip finance professionals, and machine learning professionals who seek upgrade their skills for trading strategies.. The contents are very high quality while the exercises are thoughtfully curated at the same time. Save my name, email, and website in this browser for the next time I comment. – Apply the knowledge gained in these lectures in an array of fields such as robotics, vision and physical simulations. – Implement the concepts covered in the lessons by writing your first Python program and experimenting with the different techniques. An intermediate level of knowledge in programming is required for admission, and the course material will be difficult to chew through if you’re not fully dedicated and motivated to start a career in the field of machine learning. Throughout the classes, you will learn the R programming language, statistical concepts, and data analysis techniques simultaneously. These courses can be a great stepping stone for you if you want to get a new job or switch careers or just want to learn something new! Duration: Approx 55 hours, 7 hours per week, Review : Truly an exceptional class. This is a course that heavily leans on the practical side of machine learning, preferring to show rather than tell. Much of the course content is applied, so you'll learn how to not only how to use the ML models but also launch them on cloud providers, like AWS. – Projects and thesis in collaboration with top technology-based companies. Unlike many other courses, this course covers some significant parts of data mining. By the end of the classes, you will have a strong mathematical footing to take more advanced lessons in ML and become a professional. One of the best things about this course is the practical advice given for each algorithm. – Tons of practical exercises and quizzes to measure your grasp on the concepts covered in the lectures.