Supervised, semi-supervised or unsupervised deep learning is part of a broader family of machine learning methods, that teach you the basics of neural networks. Learn from the Top 10 Deep Learning Courses curated exclusively by Analytics Insight and build your deep learning models with Python and NumPy.
Beginner Level
1. Structuring Machine Learning Projects
Platform- Coursera
Offered by – Deeplearning.ai
Rating- 4.8
Instructors- Andrew Ng, Kian Katanforoosh, Younes Bensouda
Level- Beginner
Timeline- Approx. 5 hours to complete
Taught by one of the best Data Science experts of 2020 Andrew Ng, this course teaches you how to build a successful machine learning project. You will understand the complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. Over 20 videos spread across the entire module will explain you error analysis and different kind of the learning techniques. Over the entire course, you will learn Machine Learning, Deep Learning, Inductive Transfer and Multi-task learning.
2. AI for Everyone
Platform- Coursera
Offered by –Deeplearning.ai
Rating- 4.8
Instructors- Andrew Ng
Level- Beginner
Timeline- Approx. 6 hours to complete
This course is aimed at non-technical professionals who have a passion to learn deep learning. You will learn AI strategy, AI terminology and workflow of machine learning and data science projects. The course is divided into four weeks, explaining to you what is AI, Building AI projects, Building AI in your company, and the concept of AI & Society.
This course is excellent for those who wish to start a new career in AI and wish to learn the business aspects of Artificial Intelligence.
3. Machine Learning
Platform- Coursera
Offered by –Stanford University
Rating- 4.9
Instructors- Andrew Ng
Level- N.A
Timeline- Approx. 54 hours to complete
One of the highest-rated courses in Coursera, Machine Learning taught by Andrew Ng instructs you about the most effective machine learning techniques. You will learn about some of Silicon Valley’s best innovation practices about machine learning and AI.
Topics include supervised learning covering parametric/non-parametric algorithms, support vector machines, kernels and neural networks, unsupervised learning that covers clustering, dimensionality reduction, recommender systems and deep learning, and the best practices in machine learning explaining bias/variance theory; innovation process in machine learning and AI.
4. Improving Deep Neural Networks- Hyperparameter tuning, Regularization and Optimisation
Platform- Coursera
Offered by – Deeplearning.ai
Rating- 4.9
Instructors- Andrew Ng, Kian Katanforoosh, Younes Bensouda
Level- Beginner
Timeline- Approx. 18 hours to complete
Another top-rated course for beginners, it will teach you how to enhance your neural network algorithms and improve their accuracy. The introduction of Tensor Flow is an added plus point. The course is divided into three weeks, introducing you to the practical aspects of deep learning, optimisation algorithms and Hyperparameter tuning, Batch Normalization and Programming Frameworks.
Intermediate Level
5. Neural Networks and Deep Learning
Platform- Coursera
Offered by –Deeplearning.ai
Rating- 4.9
Instructors- Andrew Ng, Kian Katanforoosh, Younes Bensouda
Level-Intermediate
Timeline- Approx. 20 hours to complete
Even if you are already familiar with deep neural networks, take it as a complementary course for any pieces you may have missed previously. Learn the mathematics behind the Neural Networks in this course.
A must for every Data science enthusiast, we suggest you undergo the Stanford Andrew Ng Machine Learning course first and then take this specialization for a better understanding.
6. Deep Learning Specialization
Platform- Coursera
Offered by – Deeplearning.ai
Rating- 4.8
Instructors- Andrew Ng, Kian Katanforoosh, Younes Bensouda
Level- Intermediate
Timeline- Approx. 4 months to complete
In this deep learning specialization course, you will get valuable career advice learning from the best Deep Learning practitioners. This course covers the foundations of Deep Learning, Xavier/He initialization, RNNs, Convolutional networks, BatchNorm, LSTM, Adam, Dropout, and more. You will not only learn the theory but understand the practical approach through case studies taken from natural language processing, autonomous driving, sign language reading, healthcare and music generation.
7. Convolutional Neural Networks
Platform- Coursera
Offered by – Deeplearning.ai
Rating- 4.9
Instructors- Andrew Ng, Kian Katanforoosh, Younes Bensouda
Level- Intermediate
Timeline- Approx. 20 hours to complete
This is the fourth course of the deep learning specialization from the Andrew Ng series. Divided into four weeks convolutional neural networks covers the foundations of convolutional neural networks explained in 12 videos, 4 readings, 3 quizzes, deep convolutional models through case studies explained in 11 videos, 1 reading, 2 quizzes, object detection covered in 10 videos, 2 readings and 2 quizzes.
The course ends with a special application to facial recognition & neural style transfer explained over 11 videos, 3 readings and 3 quizzes.
8. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Platform- Coursera
Offered by – Deeplearning.ai
Rating- 4.7
Instructors- Laurence Moroney
Level- Intermediate
Timeline- Approx. 30 hours to complete
Don’t miss this one especially if you are a beginner into Deep Learning for Computer Vision. You will learn how to use TensorFlow, a popular open-source framework for machine learning to start building and applying scalable models to real-world problems. Besides, you will also be training a neural network for a computer vision application.
This course is best suited to software developers aiming to build scalable AI-powered algorithms. To have a grip on this course it is advisable to take the Deep Learning Specialization course before starting this one.
9. Reinforcement Learning Specialization
Platform- Coursera
Offered by – University of Alberta, Alberta Machine Intelligence Institute
Rating- 4.7
Instructors- Martha White and Adam White
Level- Intermediate
Timeline- Approx. 5 months to complete
This course introduces you to statistical learning techniques covering the fundamentals of Reinforcement learning, Sample-based learning models, Prediction and control with functional approximation, and a capstone project to end the course to build and implement a complete Reinforcement learning solution.
Advanced Level
10. Machine Learning with Python: from Linear Models to Deep Learning
Platform- Edx
Offered by – Massachusetts Institute of Technology
Rating- N.A
Instructors- Regina Barzilay, Tommi Jaakkola, Karene Chu
Level- Advanced
Timeline- Approx. 15 Weeks, 10–14 hours per week
A must for Python lovers! You will start with understanding the principles behind machine learning algorithms with an emphasis on classification, regression, clustering, and reinforcement learning models. The extensive course covers linear regression, non-linear classification, Neural networks, Deep learning, backpropagation to name a few, taught by professors from the Massachusetts Institute of Technology.
