Last Updated on December 9, 2019

Deep Learning involves techniques that can’t be understood without an effective teacher. I’ve taken many data science and Machine Learning related courses and audited portions of many more.

I know the options out there, and what skills are needed for learners preparing for a Data Scientist, Machine Learning Engineer or Deep Learning Scientist role.

So I started creating a review-driven guide that recommends the best courses for each subject within Deep Learning and for this guide, I put a tremendous amount of efforts trying to identify every best deep learning course.

I extracted key bits of information from their syllabus and compiling their ratings because techniques like Deep Learning, which underpin many of today’s AI tools, aren’t easy to grasp.

First off, you need to have a solid understanding of advanced mathematical concepts for deep learning. So, if you to want learn the basics or need a refresher, I’ve got you covered in this article about Maths for Machine Learning.

Deep learning offers enormous potential for creative applications and in this guide, for best Deep Learning Courses we interrogate what’s possible. So, without further ado, let’s get started !

The 5+ Best Deep Learning Courses from the World-Class Educators.

These courses will prepare you for the Deep Learning role and help you learn more about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modelling language, and human motion, and more.

Below, I’ve curated a selection of the best available courses in Deep Learning for beginners and experts who aspire to expand their minds. ☟

If you want to learn Deep Learning in Python, this course will introduce you to the fundamental concepts and terminologies used in deep learning, and understand why deep learning techniques are powerful these days. The topics in this course will mostly cover the basics of deep learning and neural networks. This course will also teach you a deep learning framework called Keras using which in just a few lines of code you can train very deep neural networks.

Is it right for you?

This course is suitable for beginners in Deep Learning with good knowledge of Python programming and some experience in Machine Learning. By the end of this course you will also build a simple neural network all by yourself and generate predictions using Python’s numpy library.

What you will learn?

Basics of deep learning and neural networks

Optimizing a neural network with backward propagation

Building deep learning models with keras

Fine-tuning keras models

An Introduction to Practical Deep Learning is taught by AI Principal Engineers at Intel . This course is very dense and informative that aims to help learners to grasp the basics of Deep Learning.

You will also learn to speed up your deep learning and accelerated computing applications for the development of self-driving cars, speech interfaces, genomic sequence analysis, and algorithmic trading.

Is it right for you?

This course is primarily aimed at learner with some background in programming and understanding of basic calculus, but are new to the field deep learning.

What you will learn?

Introduction to Deep Learning and Deep Learning Basics

Convolutional Neural Networks (CNN), Fine-Tuning and Detection

Recurrent Neural Networks (RNN)

Training Tips and Multinode Distributed Training

Hot Research and Intel’s Roadmap

Final Quiz

GO TO COURSE

Introduction to Deep Learning is an advanced 6-week course created by the National Research University Higher School of Economics . This course introduces learners to the basic understanding of modern neural networks and their applications in computer vision and natural language understanding.

The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks.

You will also learn to use popular building blocks to define complex modern architectures in TensorFlow and Keras frameworks.

Is it right for you?

This course is suitable for Developers, analysts, and researchers with the basic knowledge of Python, linear algebra and probability.

The topics covered in this course will help learners who are faced with tasks involving complex structure understanding such as image, sound and text analysis.

What you will learn?

Introduction to optimization

Introduction to neural networks

Deep Learning for images

Unsupervised representation learning

Deep learning for sequences

Final Project

GO TO COURSE

Deep Learning Specialization – Highly Recommended

This is one of the best and highly recommended Deep Learning Specialization, comprised of five courses taught be the AI Pioneer – Andrew Ng , Co-Founder of Coursera, DeepLearning AI and Adjunct Professor at Stanford University

This specialization will help you learn the foundations of Deep Learning, understand techniques to build effective neural networks, and learn how to manage successful machine learning projects.

You will master not only the theory but also see how it is applied in businesses with hand-on-practice in Python and TensorFlow.

Is it right for you?

If you are seeking an opportunity to build a deep learning project with cutting-edge, industry-relevant techniques, this specialization will help you do so.

This specialization assumes that a learner has intermediate skills in Python and basic knowledge of statistics to understand and work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.

What you will learn?

Neural Networks and Deep Learning

Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization

Structuring Machine Learning Projects

Convolutional Neural Networks

Sequence Models

This course on Deep Learning with Keras is Created by Jerry Kurata , Technology Expert and best selling author of Machine Learning and Deep Learning Courses on Pluralsight and Coursera.

This course will get you up to speed with both the theory and practice of using Keras to create powerful deep neural networks.

You will be equipped with the various methods of using Keras for interconnecting various layers of neurons quickly and easily to form the structure of your deep neural networks.

Is it right for you?

This course is suitable for learners with a good knowledge of Python to work with Keras and will help gain the skills required to effectively create deep neural networks through the course’s combination of lecture and hands-on coding.

What you will learn?

Introduction to Deep Learning

Introduction to Keras, TensorFlow and Neural Networks

Introduction to Installation – TensorFlow and Keras

Creating your First Keras Neural Network

Conducting Models in Keras

Employing Layers in Keras Models

Building Convolutional NN with Keras

Implementing Recurrent Neural Nets with Keras

Using Specialty Layers and Functions