Top 7 Online Machine Learning Courses to Improve your Career

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Safalta Expert Published by: Vanshika Jakhar Updated Thu, 24 Nov 2022 01:47 AM IST

Machine learning, which has deep roots in statistics, is quickly emerging as one of the most fascinating and dynamic areas of computer science. Machine learning can increase the productivity and intelligence of countless markets and applications. Finding patterns and developing mathematical models for things that are sometimes impossible for humans to do is made possible by machine learning. Machine learning courses, in contrast to data science courses, are solely concerned with teaching machine learning algorithms. Click here to learn more https://www.safalta.com/data-science-with-python-classes.

Table of Content
#1: Machine Learning—Coursera
#2: Deep Learning Specialization—Coursera
#3: Machine Learning Crash Course — Google AI.
#4: Machine Learning with Python — Coursera.
#5: Advanced Machine Learning Specialization — Coursera.
#6: Machine Learning—EdX

#1: Machine Learning—Coursera

Andrew Ng, a Stanford professor, co-founder of Google Brain, Coursera, and the vice president who expanded Baidu's AI team to thousands of scientists, is the instructor and creator of this introductory course. For the assignments, the course uses the open-source programming language Octave rather than Python or R. For those who find this to be a deal-breaker, Octave is a straightforward way to learn the foundations of machine learning.
 

#2: Deep Learning Specialization—Coursera

Anyone interested in learning about neural networks and deep learning and how they work should take this specialization, which is a more advanced course series. Each course uses the TensorFlow library for neural networks and the Python programming language for assignments and lectures. Being exposed to Python for machine learning while still receiving a similar lecture format makes this a natural follow-up to Ng's Machine Learning course.
 

#3: Machine Learning Crash Course — Google AI.

This course is offered by Google AI Education, a platform that offers articles, videos, and interactive content for free. The topics covered in the Machine Learning Crash Course are essential for quickly resolving ML issues. Python is the programming language of choice, and TensorFlow is introduced, just like in the previous course. A Google Colab-hosted interactive Jupyter notebook is included in each major section of the curriculum.
 

#4: Machine Learning with Python — Coursera.

Another introductory course, but this one is solely concerned with the most basic machine learning algorithms. The combination of the instructor, slide animations and algorithm explanations works very well to give you a natural sense of the fundamentals. This course makes use of Python and is less heavy on the algorithms' underlying mathematics. You'll have the opportunity to launch an interactive Jupyter notebook in your browser for each module to practice the new ideas you've just learned. Each notebook serves to solidify your understanding and provides step-by-step guidance for applying an algorithm to actual data.
 

#5: Advanced Machine Learning Specialization — Coursera.

Another advanced course set with a very broad scope is this one. This specialization is the key to a well-rounded and comprehensive online curriculum if you're interested in learning about as many machine learning techniques as you can. This course offers excellent instruction that is both clear and concise. You will need more math than for any other course on the list because of how advanced it is. This is a good option to complete the remaining requirements for your machine learning certification if you have already taken a beginner's course and have reviewed your linear algebra and calculus.

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#6: Machine Learning—EdX

Of all the courses on this list, this advanced course has the strictest math requirements. You'll require a strong foundation in programming, calculus, probability, and linear algebra. The course does not teach either Python or Octave, but it has interesting programming assignments in both of those languages. The course's coverage of the probabilistic approach to machine learning is one of the biggest differences.
 

#7: Introduction to Machine Learning—Fast. ai

This top-notch, free machine learning course was created by Fast.ai for people who have had around a year of Python programming experience. The founders of Fast.ai have put an incredible amount of time and effort into this course and other courses on their website. The lectures are conducted in a classroom with students, much like the MIT OpenCourseWare format, as the material is based on the University of San Diego's Data Science program.

It's a lot of fun and exciting to learn about and experiment with machine learning, and I hope you were able to find a course that fits your path into this fascinating field. One element of data science is machine learning. A guide that resembles this one's format is the top data science courses if you're also interested in learning about statistics, visualization, data analysis, and other topics.

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