How to start learning Machine Learning and Deep Learning?

Original article can be found here (source): Deep Learning on Medium

How to start learning Machine Learning and Deep Learning?

The biggest advances that we are seeing today is due to the increase in the use of machine learning and deep learning. In the early days, these fields are less popular due to the computational power to run these algorithms. Since the last decade, the computational power to run these algorithms has dramatically increased which made many breakthroughs in recent years.

These kinds of advances in machine learning and deep learning will benefit many fields in the coming years. Some of the advantages of artificial intelligence in the industry level are in:

[1] Manufacturing

[2] Retail

[3] Healthcare

[4] Travel and Hospitality

[5] Financial Services and many more.

Some of the Machine Learning and Deep Learning courses to get started:

[1] Machine Learning A-Z™: Hands-On Python & R In Data Science

By Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support (Udemy)

This course covers many machine learning algorithms such as regression, classification, clustering, association rule learning, reinforcement learning, natural language processing, and deep learning. All the algorithms are coded in R and Python. The intuition behind all the algorithms is explained in a simple way to understand.

[2] Deep Learning A-Z™: Hands-On Artificial Neural Networks

By Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team (Udemy)

One of the top deep learning courses available. The course covers the supervised and unsupervised deep learning algorithms. Here you can learn Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Self Organizing Maps, Boltzmann Machines, and Auto Encoders.

For mathematics behind the algorithms:

[3] Deep Learning

By Prof. Prabir Kumar Biswas | IIT Kharagpur (Swayam)

Many deep learning algorithms are covered in this course from a theoretical perspective. Some of the topics include Neural Networks, Convolutional Neural Networks, Auto Encoders, Transfer Learning, Gradient Descent, Normalization, Object Detecting, Image Denoising, Long Term Short Term Memory Networks, Generative Adversarial Networks and many more.

[4] Introduction to Machine Learning

By Prof. Balaraman Ravindran | IIT Madras (Swayam)

This course also provides the mathematics behind the machine learning algorithms extensively. From regression to gaussian mixture models this course provides everything to know the math behind the algorithms.

There are many courses out there on YouTube and different online learning platforms, books to develop a small project which will really help you to implement whatever you have learned. You also need to learn some corporate-level tools like Azure Machine Learning to do real-time applications for machine learning.

Keep learning and keep updated.