10 Free Training Courses on Machine Learning and Artificial Intelligence

List of Top 10 & Best Artificial Intelligence & Machine Learning Courses

I’ve listed Top 10 Best Artificial Intelligence & Machine Learning Courses that will help you turn into the following ML master Google or Apple employs. Obviously, it is diligent work, yet in the event that you will seek after something, you’ll find ways like these to succeed.

1. Udacity’s Intro to Machine Learning

This course shows you everything from grouping to choice trees, from ML calculations such as Adaboost to SVMs. Individuals additionally prescribe you take the foundational Intro to Data Science course which manages Data Manipulation, Data Analysis, Data Communication with Information Visualization, Data at Scale etc. This course is around 10-11 week course shows all you have to know to deal with informational collections utilizing machine learning systems to separate valuable bits of knowledge. Teachers Sebastian Thrun and Katie Malone will anticipate that the tenderfoots will know fundamental factual ideas and Python.

Course Link: https://www.udacity.com/course/intro-to-machine-learning–ud120

2. Google’s Deep Learning

The course leads Vincent Vanhoucke and Arpan Chakraborty anticipates that the learners will have a programming background in Python and some GitHub encounter and to know the fundamental ideas of ML and measurements, straight variable based math, and analytics. The TensorFlow (Google’s own profound learning library) course has an additional preferred standpoint of acting naturally paced. Udacity offers this astonishing free course which “takes machine learning  out to the next level.” Google’s 3-month course is not for novices. It discusses the inspiration for deep learning, deep neural networks, convolutional networks, and deep models for text and sequences.

Course Link: https://www.udacity.com/course/deep-learning–ud730

3. Machine Learning by Andrew Ng

This course covers supervised and unsupervised learning, linear and logistic regression, regularization, and Naïve Bayes. Andrew Ng an Associate Professor at Stanford University utilizes Octave and MatLab takes this 11-12 week course. The course is rich on the off chance that case studies and recent practical applications. Students are relied upon to know the rudiments of probability, linear algebra, and software engineering. The course has good reviews from the clients.

Course Link: https://www.coursera.org/learn/machine-learning

4. Artificial Intelligence: Principles and Techniques by Stanford

This Stanford course discusses how AI utilizes math tools to manage complex issues, for example, machine interpretation, speech and face recognition, and self-ruling driving. You can get to the complete address layout—machine learning ideas; tree search, dynamic programming, heuristics; game playing; Markov decision procedures; constraint satisfaction issues; Bayesian systems; and rationale—and assignments.

Course Link: http://web.stanford.edu/class/cs221/

5. EdX’s Learning from Data (Introductory Machine Learning)

Yaser S. Abu-Mostafa, Professor of Electrical Engineering and Computer Science at the California Institute of Technology, will show you the fundamental hypothetical standards, calculations, and uses of Machine Learning. The course requires an exertion of 10 to 20 hours for every week and keeps going 10-11 weeks.

Course Link: https://www.edx.org/course/learning-data-introductory-machine-caltechx-cs1156x#!

6. Udacity’s Artificial Intelligence for Robotics by Georgia Tech

Offered by Udacity, this course discusses programming a robotic auto the way Stanford and Google do it. It is a piece of the Deep Learning Nanodegree Foundation course. Sebastian Thrun will discuss localization, Kalman and Particle channels, PID control, and SLAM. The solid handle on math concepts such as linear algebra and probability, knowledge of Python, and programming experience are a must.

Course Link: https://www.udacity.com/course/artificial-intelligence-for-robotics–cs373

7. Factual Machine Learning

Your educator of the arrangement of video addresses (on YouTube) in Advanced Machine Learning is Larry Wasserman, Professor in the Department of Statistics and in the Machine Learning Department at the Carnegie Mellon University.

The requirements for this course are his addresses on Intermediate Statistics and Machine Learning planned for Ph.D. understudies. On the off chance that you can’t get to these courses, you have to guarantee you have the required math, software engineering, and details aptitudes.

Course Link: https://www.youtube.com/watch?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE&v=zcMnu-3wkWo

8. Coursera’s Neural Networks for Machine Learning

Emeritus Distinguished Professor Gregory Hinton pioneer in the field of deep learning, Hinton’s videos recordings on YouTube discuss the use of neural systems in image segmentation, human movement, modeling language, speech and object recognition etc. Understudies are relied upon to be alright with math and have imperative involvement in Python programming. This is 16-week propelled course offered by Coursera

Course Link: https://www.youtube.com/watch?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9&v=cbeTc-Urqak

9. EdX’s Artificial Intelligence

This energizing course from EdX discusses AI applications, for example, Robotics and NLP, machine learning algorithms, data structures, games, and constraint satisfaction problems. It keeps going 12 weeks and is a propelled level instructional exercise from Columbia University.

Course Link: https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x

10. For Learning Theory

Below are the free online AI classes which covers many basics.

Courses Link: https://www.coursera.org/specializations/machine-learning

 

ADDITIONAL COURSES which are FREE (Until 2017)

Intro to AI: https://www.udacity.com/course/c…

Neural Networks for Machine Learning: https://www.coursera.org/course/neuralnets

Natural Language Processing: https://www.coursera.org/course/nlangp

AI Planning: https://www.coursera.org/course/aiplan

Computational Neuroscience: https://www.coursera.org/course/compneuro

Old-fashion and modern AI: http://aima.cs.berkeley.edu/

Statistical machine learning: http://scikit-learn.org/stable/

Evolutionary computation: https://code.google.com/p/deap/

MIT opencourseware: http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/

AI Principles & Techniques: http://web.stanford.edu/class/cs221/

Intro AI : http://ai.berkeley.edu/course_schedule.html

CS188.1x course : UC BerkeleyX: CS188.1x: Artificial Intelligence 

Machine Learning Course On Supervised Learning

Machine Learning Course On Unsupervised Learning

Machine Learning Course On Reinforcement Learning

MACHINE LEARNING (FREE) 

MACHINE LEARNING FOUNDATIONS: A CASE STUDY APPROACH (FREE) 

MACHINE LEARNING: REGRESSION (FREE) 

MACHINE LEARNING: CLASSIFICATION (FREE) 

MACHINE LEARNING: CLUSTERING & RETRIEVAL (FREE) 

MACHINE LEARNING FOR DATA SCIENCE AND ANALYTICS (FREE) 

LEARNING FROM DATA (FREE) 

STATISTICAL LEARNING (FREE) 

ML with Python: https://www.edx.org/courses/BerkeleyX/CS188.1x/2013_Spring/about

 

Additional Note:

If you want to learn advanced topics specifically about ML, you can try Andrew Ng’s CS229 Stanford course. You will get the whole course (lectures, assignments, notes etc) on itunes .

 

Summary

In this post, a few of the World class courses are intended to help you begin in the energizing and quickly developing field of Machine Learning and Artificial Intelligence. Others take you through somewhat more propelled angles. The courses recorded are free and the main thing preventing you from getting the most out of them will be an absence of duty.

So once you recognize your learning objectives, and accepting you have dependable access to innovative prerequisites, act naturally restrained, set time limits, remain on calendar, work successfully with others, and, a large portion of all, discover approaches to remain persuaded!

Final Quote:

“Software is eating the world, but AI is going to eat software.” – Jensen Huang – Nvidia CEO

 

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