Introduction to Deep Learning & Neural Networks with Keras (Coursera)

Offered by IBM,
Introduction to Deep Learning & Neural Networks with Keras (Coursera)

Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

After completing this course, learners will be able to:
• describe what a neural network is, what a deep learning model is, and the difference between them.
• demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.
• demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.
• build deep learning models and networks using the Keras library.

Course 3 of 6 in the IBM AI Engineering Professional Certificate

Syllabus

WEEK 1
Introduction to Neural Networks and Deep Learning
In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Finally, you will learn about how neural networks feed data forward through the network.

WEEK 2
Artificial Neural Networks
In this module, you will learn about the gradient descent algorithm and how variables are optimized with respect to a defined function. You will also learn about backpropagation and how neural networks learn and update their weights and biases. Futhermore, you will learn about the vanishing gradient problem. Finally, you will learn about activation functions.

WEEK 3
Keras and Deep Learning Libraries
In this module, you will learn about the diifferent deep learning libraries namely, Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.

WEEK 4
Deep Learning Models
In this module, you will learn about the difference between the shallow and deep neural networks. You will also learn about convolutional networks and how to build them using the Keras library. Finally, you will also learn about recurrent neural networks and autoencoders.

WEEK 5
Course Project
In this module, you will conclude the course by working on a final assignment where you will use the Keras library to build a regression model and experiment with the depth and the width of the model.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Machine Learning: Regression (Coursera) Coursera
University of Washington

Machine Learning: Regression (Coursera)

Case Study - Predicting Housing Prices. In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.

Jun 15th 2026
5-12 Weeks
Sample-based Learning Methods (Coursera) Coursera
University of Alberta,Alberta Machine Intelligence Institute

Sample-based Learning Methods (Coursera)

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning.

Jun 15th 2026
4 Weeks
Machine Learning for Data Analysis (Coursera) Coursera
Wesleyan University

Machine Learning for Data Analysis (Coursera)

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.

Jun 15th 2026
4 Weeks
Generative AI Essentials: Overview and Impact (Coursera) Coursera
University of Michigan

Generative AI Essentials: Overview and Impact (Coursera)

With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like.

Jun 19th 2026
1 Week
Advanced Algorithms and Complexity (Coursera) Coursera
University of California, San Diego,Higher School of Economics - HSE University

Advanced Algorithms and Complexity (Coursera)

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more typical applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision.

Jun 15th 2026
5-12 Weeks
Business Implications of AI: A Nano-course (Coursera) Coursera
EIT Digital

Business Implications of AI: A Nano-course (Coursera)

In this course you will learn what Artificial Intelligence is, from a leaders point of view. How shall we, as leaders, understand it from a corporate strategy point of view? What is it and how can it be used? What are the crucial strategic decisions we have to make, and how to make them? What consequences can we expect if we decide on doing AI-projects and what kind of competences do we need? Where shall we start, and what could be a good second as well as third step? What implications for the organization can we expect? These are the questions answered in this course.

Jun 15th 2026
4 Weeks
Business Implications of AI: Full course (Coursera) Coursera
EIT Digital

Business Implications of AI: Full course (Coursera)

In this course you will learn what Artificial Intelligence is, from a leaders point of view. How shall we, as leaders, understand it from a corporate strategy point of view? What is it and how can it be used? What are the crucial strategic decisions we have to make, and how to make them? What consequences can we expect if we decide on doing AI-projects and what kind of competences do we need? Where shall we start, and what could be a good second as well as third step? What implications for the organization can we expect? These are the questions answered in this course.

Jun 15th 2026
4 Weeks
Introduction to Computer Vision with Watson and OpenCV (Coursera) Coursera
IBM

Introduction to Computer Vision with Watson and OpenCV (Coursera)

Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries such as self-driving cars, robotics, augmented reality, face detection in law enforcement agencies. In this beginner-friendly course you will understand about computer vision, and will learn about its various applications across many industries.

Jun 15th 2026
4 Weeks
Practical Machine Learning (Coursera) Coursera
Johns Hopkins University

Practical Machine Learning (Coursera)

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates.

Jun 15th 2026
4 Weeks
The Economics of AI (Coursera) Coursera
University of Virginia

The Economics of AI (Coursera)

The course introduces you to cutting-edge research in the economics of AI and the implications for economic growth and labor markets. We start by analyzing the nature of intelligence and information theory. Then we connect our analysis to modeling production and technological change in economics, and how these processes are affected by AI. Next we turn to how technological change drives aggregate economic growth, covering a range of scenarios including a potential growth singularity.

Jun 16th 2026
5-12 Weeks
Mathematics for Machine Learning: Linear Algebra (Coursera) Coursera
Imperial College London

Mathematics for Machine Learning: Linear Algebra (Coursera)

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

Jun 15th 2026
5-12 Weeks