Best Coursera Deep Learning Courses You Must Enroll

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Coursera is a well known and popular MOOC teaching platform that partners with top universities and organizations to offer online courses.

A typical Coursera deep learning course includes pre recorded video lectures, multi-choice quizzes, auto-graded and peer reviewed assignments, community discussion forum and a sharable electronic course completion certificate.

Through the audit option, it allows learners to take the coursera deep learning free courses without an official certificate.

Also, if you enroll to coursera deep learning specialization, you have to opt for monthly subscriptions to get course completion certificate.

Coursera hosts around a good number of courses, specializations, certificate programs and master’s degree in the field of deep learning.

All these courses are curated and taught by professors from world’s best universities.

How much do Coursera deep learning courses cost?

Deep learning is a sub field of machine learning, where it teaches a computer to understand and perform tasks from data in the form of images, text or sound.

To achieve the desired results, deep learning algorithms uses large amount of labelled data and multiple layers of neural network architectures.

If you opt for the whole deep learning specialization or a single course from a specialization series, you need to go for a monthly subscription.

However the course video lectures can be audited (without paying) for free. But you won’t get access to assessments, peer grading of projects and course completion certificate.

What are the best deep learning courses on coursera?

Below are our best picks of Coursera deep learning courses if you want to break into cutting edge AI.

Neural Networks and Deep Learning –

Launching into Machine Learning – Google Cloud

Introduction to Deep Learning – National Research University

Applied AI with Deep Learning – IBM

Natural Language Processing – National Research University

Deep Learning for Business – Yonsei University

Neural Networks and Deep Learning

This is the introductory course of the very popular coursera deep learning specialization from Andrew Ng.

The neural networks and deep learning coursera course runs for 4 weeks long and requires fundamental knowledge in Python programming, deep learning libraries and maths concepts like linear algebra and calculus.

The course gives you a good overview of neural networks, the basics of deep neural networks, know to build your very own deep neural networks and practice assignments on cloud using Jupyter notebooks.

Course Ratings: 4.9+ from 47,981+ students

Key Learning’s from the Course:

  • What is deep learning
  • Understand the basics of neural networks
  • How to use vectorization when using a ML model in the Python Numpy library
  • Know the use of forward propagation and backpropagation to build shallow neural networks
  • The key computations required to build and train deep neural networks

Who is this course best suited?  Anyone interested in ML, deep learning and AI.

Skills Gained from the course: Artificial Neural Network, Backpropagation, Python Programming and Deep Learning

Coursera Deep Learning Reviews:


Introduction to Deep Learning

An advanced deep learning course, it dives into the tools used in deep learning techniques with extensive math concepts, tough project assignments and gives a great overview about neural networks, CNN and NLP.

This is the introductory course of 7 course series of advanced machine learning specialization from national research university.

Course Ratings: 4.6+ from 805+ students

Key Learning’s from the Course:

  • Introduction to linear models and stochatic optimization methods
  • Introduction to deep neural networks concepts
  • How to build CNN architectures for images
  • How to generate, morph and search images with deep learning
  • How to use deep learning for sequences such as texts, video, audit, etc
  • Learn RNN architectures applications with sequences
  • Develop your own neural networks for real world images

Who is this course best suited?  If you want to learn core concepts of deep learning techniques and a hands on coding assignment of TensorFlow

Skills Gained from the course: Recurrent Neural Network, Tensorflow, Convolutional Neural Network and Deep Learning

Coursera Deep Learning Reviews:

Applied AI with Deep Learning

Offered by IBM, the coursera deep learning course is part of their advanced data science with IBM specialization.

The course teaches neural network fundamentals, insights into deep learning models using real-life examples from Internet of Things, financed marked data, literature and image databases.

Since it is an advance course, solid knowledge in Python and linear algebra is necessary in order to attempt coding assignments.

Course Ratings: 4.4+ from 291+ students

Key Learning’s from the Course:

  • Understand deep learning models and it applications in NLP, computer vision, time series analysis, etc
  • Learn fundamentals of linear algebra and neural networks
  • Introduction to popular DL frameworks like Keras, TensorFlow, etc
  • Learn about anomaly detection, time series forecasting, image recognition and NLP
  • How to scale artificial brains using Kubernetes, Apache Spark and GPUs

Who is this course best suited?  If you want to learn deep learning frameworks like TensorFlow, Keras, PyTorch, SystemML, DL4J and Apache Spark in IBM cloud

Skills Gained from the course: Machine Learning, Deep Learning, Long Short-Term Memory (ISTM) and Apache Spark

Deep Learning Coursera Reviews:


Natural Language Processing

Part of National Research University’s Advance Machine Learning Specialization, the course covers a wide range of deep learning techniques used in NLP.

It is an advanced course and runs for 5 weeks.

Course Ratings: 4.7+ from 299+ students

Key Learning’s from the Course:

  • Broad overview of NLP topics such as classification, sentiment analysis, spam filtering, etc
  • Learn how to convert raw data into predicted classes using linear classifiers and convolutional neural nets
  • Know language modeling and is usage in search, machine translation, chat-bots, etc
  • Understand sequence tagging prediction for a sequence of words
  • What are traditional models of distributed semantics and the usage of modern tools for word and sentence embeddings
  • How to formulate a sequence to sequence task in NLP
  • Task oriented dialog systems and detail understanding of Natural language understanding and dialog manager

Who is this course best suited?  If you want solid introduction to NLP with tough and challenging assignments

Skills Gained from the course: Chatterbot, Tensrflow, Deep Learning and Natural Language Processing

Coursera Deep Learning Reviews:


Deep Learning for Business

Offered by Yonsei University, the course is a gentle introduction on how to use deep learning for business professionals with real world examples.

The course runs for 6 weeks and intends to teach practical aspects of deep learning basics for non-IT professionals.

Course Ratings: 4.3+ from 254+ students

Key Learning’s from the Course:

  • Introduction to Deep learning products like IBM Watson, Amazon Echo, LettuceBot, etc with respect to industry evolutions
  • Learn business strategy based modeling using deep learning
  • Introduction of popular DL software TensorFlow, CNTK, Keras, Caffe, Theano and their characteristics
  • Introduction to deep learning neural networks basics
  • Know how CNN and RNN technology enable deep learning and is usage in speech recognition, sequence data analysis, etc
  • Deep learning project with TensorFlow Playground

Who is this course best suited? If you want to learn deep learning techniques and its applications in business scenarios

Skills Gained from the course: Artificial Intelligence (AI), Artificial Neural Network, Machine Learning and Deep Learning

Deep Learning Coursera Reviews:


Launching into Machine Learning

Google collaborated with Coursera and came up with a machine learning specialization with TensorFlow on Google cloud platform.

This is the second course of the 5 course specialization series and gives you an overview of ML basics and how to work on GCP.

Course Ratings: 4.5+ from 1,387+ students

Key Learning’s from the Course:

  • Introduction to machine learning terminologies
  • Intrduction to the machine learning types
  • How to optimise machine learning models
  • Understand generalization and sampling when dealing with datasets

Who is this course best suited?  Learn Google TensorFlow playground and understand how to tune neural networks for dataset classification

Skills Gained from the course: Tensorflow, Bigquery, Machine Learning and Data Cleansing

Deep Learning Coursera Reviews:


Do you recommend any other Coursera Deep Learning courses worth enrolling into? Let us know in the comments.

Happy Data Learning!