If you want to break into AI, these Deep Learning Courses will help you do so

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.

Deep learning offers enormous potential for creative applications and in this guide, for best Deep Learning Courses we interrogate what's possible.

Best Courses in Deep Learning [2018 Updated]

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


1. An Introduction to Practical Deep Learning

Deep Learning Course by Intel 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.

Deep Learning Course

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

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2. Introduction to Deep Learning 

National Research University Higher School of Economics LOGOIntroduction to Deep Learning is an advanced 6-week course created by the National Research University Higher School of Economics. This course teaches learners 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.

Deep Learning Course

What you will learn?

  • Introduction to optimization
  • Introduction to neural networks
  • Deep Learning for images
  • Unsupervised representation learning
  • Deep learning for sequences
  • Final Project

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3. Deep Learning Specialization - (Highly Recommended)

Deep Learning CourseThis 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 to 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.

Deep Learning Specialization
Source: Coursera

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

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4. Deep Learning with Keras

Pluralsight LogoThis 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. And equip you 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.

Deep Learning Course

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

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5. Applied AI with Deep Learning 

IBM LogoThis course, Applied Artificial Intelligence with DeepLearning gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines.

You will learn about the fundamentals of Linear Algebra and Neural Networks and also we understand Anomaly Detection, Time Series Forecasting, Image Recognition and NLP by building up models using Keras one real-life examples from IoT, Financial Marked Data, Literature or Image Databases.

Is it right for you?

This course requires some background in programming, preferably Python and the basic understanding of math (linear algebra) is a plus. This course is suitable for learners who want to master Deep Learning and learn to use the most popular Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML.

Deep Learning Course

What you will learn?

  • Introduction to deep learning
  • Deep learning frameworks
  • DeepLearning Applications
  • Scaling and Deployment

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6. AI & Deep Learning with TensorFlow

edureka-logoThis course, AI & Deep Learning in TensorFlow with Python Certification is taught by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Auto-encoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TensorFlow Deep Learning Library.

This course provides an introduction to AI and helps learners explore neural networks, understand deep learning frameworks, implement various machine learning algorithms using Deep Networks. You will also explore how different layers in neural networks do data abstraction and feature extraction using Deep Learning.

Is it right for you?

If you have some experience in Python and want to attend Instructor-led AI & Deep Learning with TensorFlow live online classes, then this course is perfect to start. This course will provide a strong theoretical knowledge, and equip you to work on various real-life data projects using different neural network architectures as a part of solution strategy.

Deep Learning Course

What you will learn?

  • Introduction to Deep Learning
  • Understanding Neural Networks with TensorFlow
  • Deep dive into Neural Networks with TensorFlow
  • Master Deep Networks
  • Convolution Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Restricted Boltzmann Machine (RBM) and Autoencoders
  • Keras API
  • TFLearn API
  • In-Class Project

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7. Creative Applications of Deep Learning with TensorFlow

Kadenze LogoThis course, Creative Applications of Deep Learning with TensorFlow introduces learners to deep learning with the state-of-the-art approach to building artificial intelligence algorithms. You will learn the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks.

This course aims to help learners build the necessary components of certain algorithms and understand how to apply them for exploring creative applications. You will learn to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image.

Is it right for you?

This course is suitable for learners who have some programming experience with Python or similar, e.g. MATLAB, Octave, C/C++, Java, or Processing. This course will equip you to take an approach to effective problem solving and interacting with the results of your work with your peers.

Deep Learning Course

What you will learn?

  • Introduction to TensorFlow
  • Training A Network W/ TensorFlow
  • Unsupervised And Supervised Learning
  • Visualizing And Hallucinating Representations
  • Generative Models

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Before you go

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, modeling language, and human motion, and more.

If you know of any courses that I may have missed, please let me know in the comments below!

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happy learning . . .👇🏾

Learning How to Learn