Deep Learning and Reinforcement Learning by IBM via Coursera: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

Deep Learning and Reinforcement Learning Certification by Coursera introduces its learners to two of the most sought-after avenues in Machine Learning-Reinforcement Learning and Deep Learning. The latter is abundantly used to power most of the AI applications that are used on a daily basis. Deep Learning and Reinforcement Learning Certification Syllabus exhaustively cover the theory of neural networks that form the basis of Deep Learning as well as modern architectures concerned with Deep Learning.

As an intermediate-level course, it serves as the perfect second step for aspiring data scientists interested in getting hands-on experience with both the domains in Machine Learning to learn at their own pace and earn a certification. Delivered by IBM professionals, the course will focus on Reinforcement Learning once learners have developed a few Deep Learning models. Although Reinforcement Learning has only a few practical applications at present, it is coming up as a promising area of research in AI that is expected to become relevant in the coming future. At the end of the course, data scientists will find them to be well acquainted with the learnings provided in the course. They can easily implement them in their workforce and deliver timely solutions.

Course and certificate fees

Deep Learning and Reinforcement Learning Certification Fee structure is as follows-

Candidates can take up this course for free if they audit for the free course.

Fee Details for Deep Learning and Reinforcement Learning

Particulars	Amount in INR
Deep Learning and Reinforcement Learning (Audit)	Free
Deep Learning and Reinforcement Learning - 1 month	Rs. 3,275 /-
Deep Learning and Reinforcement Learning - 3 months	Rs. 6,550 /-
Deep Learning and Reinforcement Learning - 6 months	Rs. 9,825 /-

certificate availability

Yes

certificate providing authority

Coursera

Who it is for

Data scientist

Deep Learning and Reinforcement Learning Certification Programme is an intermediate course on the two essential concepts of Machine Learning and will this benefit the following groups-

Data scientists who wish to learn about Reinforcement and Deep Learning.

Eligibility criteria

Work experience

Deep Learning and Reinforcement Learning Certification Training prescribe a prior experience with Python development environment programming as a work requisite.

Education

Course participants of the Deep Learning and Reinforcement Learning Certification Programme must have a fundamental understanding of exploratory data analysis, data cleaning, unsupervised learning, calculus, supervised learning, linear algebra, statistics and probability.

Certification qualifying details

All learners who pay for the certification after their free trial can receive Deep Learning and Reinforcement Learning Certification provided they fulfil the additional requirements of obtaining the passing grade in all the graded assignments after verification of their name or ID on Coursera.

What you will learn

Knowledge of deep learning

Deep Learning and Reinforcement Learning Certification Training prepare data scientists with preliminary knowledge about programming with Python and other basic concepts to enhance their knowledge and incline it towards a machine learning paradigm. After completion, course participants will learn the following-

Explaining the curse of dimensionality and how it makes clustering difficult with many features
Clustering points where appropriate, compare the performance of per-cluster models
Describing and using common clustering and dimensionality-reduction algorithms
Key concepts in this domain that intervene during model training
Most common CNN architectures in the current domains
Understanding metrics relevant for characterising clusters
Theoretical background of neural networks and characteristics that they share with other machine learning algorithms
Explaining the kinds of problems suitable for unsupervised learning approaches
Knowing about Reinforcement Learning through training algorithms by using rewards instead of a method to minimise error
Describing the use of trained autoencoders for image applications

The syllabus

Module 1: Introduction to Neural Networks

Videos

Course Introduction
Introduction to Neural Networks
Basics of Neurons
Neural Networks with Sklearn
Neuron in Action
Neural Networks with SKlearn
Forward Propagation
Matrix Representation of Forward Propagation
Main Types of Deep Neural Network
(Optional) Introduction to Neural Networks Notebook - Part 1
(Optional) Introduction to Neural Networks Notebook - Part 2
Gradient Descent Basics
Compare Different Gradient Descent Methods
(Optional) Gradient Descent Notebook - Part 1
(Optional) Gradient Descent Notebook - Part 2
(Optional) Gradient Descent Notebook - Part 3

Reading

Summary/Review

Quizzes

Practice: Introduction to Neural Networks
Practice: Optimization and Gradient Descent
End of Module Quiz

App Items

Neural Networks with Sklearn
Introduction to Neural Networks Demo (Activity)
Gradient Descent Demo (Activity)

Module 2: Back Propagation Training and Keras

Videos

How to Train a Neural Network
Backpropagation
(Optional) Backpropagation Notebook - Part 1
(Optional) Backpropagation Notebook - Part 2
The Sigmoid Activation Function
Other Popular Activation Functions
(Optional) Backpropagation Notebook - Part 3
Popular Deep Learning Library
A Typical Keras Workflow
Implementing an Example Neural Network in Keras
(Optional) Keras Notebook - Part 1
(Optional) Keras Notebook - Part 2
(Optional) Keras Notebook - Part 3

Reading

Summary/Review

Quizzes

Practice: Back Propagation, Activation Functions
Practice: Keras Library
End of Module Quiz

App Items

Backpropagation Demo (Activity)
Keras Demo (Activity)
Regression with Keras
(Optional) Loading Images with Keras

Module 3: Neural Network Optimizers

Videos

Optimizers and Momentum
Popular Optimizers
Details of Training Neural Networks
Data Shuffling
Transforms

Reading

Summary/Review

Quizzes

Practice: Optimizers and Data Shuffling
End of Module Quiz

App Items

Optimizers
Grid Search with Keras

Plugin

Learning Rate Scheduler Reading

Module 4: Convolutional Neural Networks

Videos

Categorical Cross Entropy
Introduction to Convolutional Neural Networks (CNN)
Images Dataset
Kernels
Convolution for Color Images
Convolutional Settings - Padding and Stride
Convolutional Settings - Depth and Pooling
(Optional) Demo CNN Notebook - Part 1
(Optional) Demo CNN Notebook - Part 2

Reading

Summary/Review

Quizzes

Practice: Convolutional Neural Networks
End of Module Quiz

App Items

Categorical Cross Entropy
Images Convolution
Padding, Pooling, and Stride
Channels and Flattening
Training the Network
Convolutional Neural Networks Demo (Activity)

Module 5: Transfer Learning

Videos

Introduction to Transfer Learning
Transfer Learning and Fine Tuning
(Optional) Transfer Learning Notebook
Convolutional Neural Network Architectures - LeNet
Convolutional Neural Network Architectures - AlexNet
Convolutional Neural Network Architectures - Inception
Convolutional Neural Network Architectures - ResNet
Regularization Techniques for Deep Learning

Reading

Summary/Review

Quizzes

Practice: Transfer Learning
Practice: Convolutional Neural Network Architectures
Practice: Regularization
End of Module Quiz

App Items

Transfer Learning Demo (Activity)
Types of Model APIs in Keras
Transfer Learning Examples with Existing Architectures
Regularization Techniques

Plugin

Regularization Reading

Module 6: Recurrent Neural Networks and Long-Short Term Memory Networks

Videos

Recurrent Neural Networks (RNNs)
State and Recurrent Neural Networks
Details Recurrent Neural Networks
(Optional) Recurrent Neural Networks Notebook - Part 1
(Optional) Recurrent Neural Networks Notebook - Part 2
Long-Short Term Memory (LSTM) Networks
LSTM Explanation
Gated Recurrent Unit Gated
Recurrent Unit Details

Reading

Summary/Review

Quizzes

Practice: Recurrent Neural Networks
Practice: LSTM and GRU
End of Module Quiz

App Items

(Optional) Introduction to Sequential Data
Existing Recurrent Neural Networks
Word Embeddings
Recurrent Neural Networks Demo (Activity)
LSTM and GRU Demo (Activity)

Module 7: Autoencoders

Videos

Introduction to Autoencoders
Autoencoders
(Optional) Autoencoders Notebook - Part 1
(Optional) Autoencoders Notebook - Part 2
(Optional) Autoencoders Notebook - Part 3
(Optional) Autoencoders Notebook - Part 4
(Optional) Autoencoders Notebook - Part 5

Reading

Summary/Review

Quizzes

Practice: Autoencoders
End of Module Quiz

App Items

Autoencoders
Autoencoders Demo (Activity)

Plugin

Transposed Convolution Reading

Module 8: Generative Models and Applications of Deep

Videos

What is a Variational Autoencoder
How Variational Autoencoders Work
Introduction to GANs
How GANS Work
Issues with Training GANS
Additional Topics in Deep Learning
Model Agnostic Explainable AI

Reading

Summary/Review

Quizzes

Practice: Variational Autoencoders
Practice: Generative Adversarial Networks
End of Module Quiz

App Items

Variational Autoencoder
GANS Lab 1
GANS Lab 2
GPU with Keras

Module 9: Reinforcement Learning

Videos

Reinforcement Learning (RL)
(Optional) Reinforcement Learning Notebook - Part 1
(Optional) Reinforcement Learning Notebook - Part 2
(Optional) Reinforcement Learning Notebook - Part 3
(Optional) Reinforcement Learning Notebook - Part 4

Reading

Summary/Review

Quizzes

Practice: Reinforcement Learning
End of Module Quiz

Peer Review

Final Project

App Items

Reinforcement Learning Demo (Activity)

Admission details

Deep Learning and Reinforcement Learning Certification Online Course can be registered for through the course URL depending upon the type of learning the learner opts for. The registration procedure has been mentioned below-

Step 1: Click on the link- https://www.coursera.org/learn/deep-learning-reinforcement-learning and log in through your account or register on Coursera.

Step 2: Select “Enroll for Free” option.

Step 3: You can choose to begin with your free trial after which you will have to pay for the certification or audit the course for free.

Step 4: On choosing a free trial option, you can begin with the course and pursue for seven days.

Step 5: Auditing the course will allow you to pursue the course for free totally without accessing graded assignments.

Scholarship Details

Scholarship in the form of Coursera financial aid is extended to those individuals whose applications for the same are approved. Interested applicants may apply through the course page by clicking on “Financial Aid available.” Continuing to their application will take them to a declaration after which they need to input their personal information and fill a 150-word application by answering the questions displayed on the screen. Every applicant shall get an email intimation regarding the acceptance and rejection of their application within 15 days.

How it helps

Deep Learning is an important branch of machine learning that boosts the capacity of computers to recognise and process speech and images. Reinforcement learning, on the other hand, allows machines and software agents and to automatically determine their ideal behaviour within a specific context. This maximises its performance. Both these concepts are being used extensively by data scientists to revamp and reshape the software industry.

Deep Learning and Reinforcement Learning Certification Course ensures learners ace in their professional setup by a strong practical experience of these two domains. The course includes practising labs on CNN and RNN which will help candidates encounter and tackle real-world problems through the learning outcomes. The subject matter is quite extensive to enumerate machine learning fundamentals.

Machine Learning has proven to be one of the most sought after skills for vacancies in modern AI applications, a field that has grown manifold throughout the years. Deep Learning and Reinforcement Learning Certification benefits learners with a professional certification from IBM and polishes candidates to pursue a career in Machine Learning. Learners can also pursue other IBM certifications to earn a digital Badge from IBM in recognition of their proficiency in Machine Learning.

Instructors

Mr Mark J Grover
Digital Content Delivery Lead
IBM

Mr Miguel Maldonado
Machine Learning Curriculum Developer
IBM

Mr Joseph Santarcangelo
Data Scientist
IBM

Ph.D

Ms Kopal Garg
Data Scientist
IBM

Other Masters

Ms Xintong Li
Data Scientist
IBM

FAQs

Can assignment deadlines be added to the Google calendar?

Yes, this can be done by activating calendar sync under the settings on the course dashboard. Any change in deadline will be reflected on the calendar as well.

What kind of quizzes are there in Deep Learning and Reinforcement Learning Certification Online Course?

Quizzes have multiple-choice, short answer and single choice questions. If the options for a question are round, it is a single-choice question. If the options for a quiz are square, it is a multiple-choice question.

Where can doubts regarding course material be addressed?

Issues with the course material can be brought up in the discussion forum available on the course dashboard.

Why is a learner not able to access his certificate?

The most likely reason for this is that the learners might not have completed the course completely. He/she must ensure that all certification qualifying requirements have been met.

Can a learner who has audited for free course receive a certification?

Such learners can receive a certification if they purchase the same during or after the course. They will have to perform the additional coursework.

Do all programming assignments come with a grade?

No, some carry a grade while some don't. The former contribute towards the certification while the latter is just for practice.

Can learners unenroll from Deep Learning and Reinforcement Learning Certification?

Learners who have paid for the professional certification but have not claimed a refund within the deadline cannot unenroll from this course.

What is the deadline for submission of assignments?

The deadline for completing assignments will be displayed on the course dashboard once the assignment activates or is unlocked.

Can course lectures be accessed offline?

They can be accessed offline only if they are downloaded, which can be done by selecting "lecture video" under downloads section on the course dashboard.