This course is part of the IBM Machine Learning Professional Certificate

Mark J Grover +1 more instructor

Offered By

IBM Machine Learning Professional CertificateIBM

About this Course

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This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.
After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning.

By the end of this course you should be able to:
Explain the kinds of problems suitable for Unsupervised Learning approaches
Explain the curse of dimensionality, and how it makes clustering difficult with many features
Describe and use common clustering and dimensionality-reduction algorithms
Try clustering points where appropriate, compare the performance of per-cluster models
Understand metrics relevant for characterizing clusters

Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.
 
What skills should you have?
To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.

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Course 5 of 6 in the

IBM Machine Learning

Flexible deadlines

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Intermediate Level

Approx. 14 hours to complete

English

Subtitles: English

Skills you will gain

Deep LearningArtificial Neural NetworkMachine LearningReinforcement Learningkeras

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 5 of 6 in the

IBM Machine Learning

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 14 hours to complete

English

Subtitles: English

Instructors

Mark J Grover

Digital Content Delivery Lead

IBM Data & AI Learning

10,585 Learners

14 Courses

Miguel Maldonado

Machine Learning Curriculum Developer

Data and AI Learning

3,647 Learners

6 Courses

Offered by

IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.

Syllabus - What you will learn from this course

Week

Week 1

3 hours to complete

Introduction to Neural Networks

This module introduces Deep Learning, Neural Networks, and their applications. You will go through the theoretical background and characteristics that they share with other machine learning algorithms, as well as characteristics that makes them stand out as great modeling techniques for specific scenarios. You will also gain some hands-on practice on Neural Networks and key concepts that help these algorithms converge to robust solutions.

3 hours to complete

18 videos (Total 143 min), 4 readings, 3 quizzes

18 videos

Course Introduction1m

Introduction to Neural Networks - Part 12m

Introduction to Neural Networks - Part 27m

Introduction to Neural Networks - Part 35m

Introduction to Neural Networks - Part 411m

Optimization and Gradient Descent8m

Gradient Descent Notebook - Part 17m

Gradient Descent Notebook - Part 212m

Gradient Descent Notebook - Part 38m

Introduction to Neural Networks Notebook - Part 114m

Introduction to Neural Networks Notebook - Part 213m

Introduction to Backpropagation in Neural Networks - Part 14m

Backpropagation - Part 25m

Backpropagation Notebook - Part 16m

Backpropagation Notebook - Part 28m

Backpropagation Notebook - Part 36m

Other Activation Functions10m

Regularization Techniques for Deep Learning6m

4 readings

Introduction to Neural Networks Demo (Activity)2m

Backpropagation Demo (Activity)2m

Summary/Review10m

3 practice exercises

Check for Understanding5m

End of Module Quiz10m

Week

Week 2

2 hours to complete

Neural Network Optimizers and Keras

You can leverage several options to prioritize the training time or the accuracy of your neural network and deep learning models. In this module you learn about key concepts that intervene during model training, including optimizers and data shuffling. You will also gain hands-on practice using Keras, one of the go-to libraries for deep learning.

2 hours to complete

7 videos (Total 63 min), 2 readings, 3 quizzes

7 videos

Optimizers12m

Details of Training Neural Networks7m

Data Shuffling3m

Keras10m

Keras Notebook - Part 17m

Keras Notebook - Part 213m

Keras Notebook - Part 37m

2 readings

Keras Demo (Activity)2m

Summary/Review10m

3 practice exercises

Check for Understanding5m

End of Module Quiz10m

Week

Week 3

3 hours to complete

Convolutional Neural Networks

In this module you become familiar with convolutional neural networks, also known as space invariant artificial neural networks, a type of deep neural networks, frequently used in image AI applications. There are several CNN architectures, you will learn some of the most common ones to add to your toolkit of Deep Learning Techniques.

3 hours to complete

13 videos (Total 118 min), 3 readings, 4 quizzes

13 videos

Important Transformations8m

Introduction to Convolutional Neural Networks - Part 17m

Introduction to Convolutional Neural Networks - Part 27m

Convolutional Settings - Padding and Stride6m

Convolutional Settings - Depth and Pooling7m

Demo CNN Notebook - Part 116m

Demo CNN notebook - Part 25m

Transfer Learning - Part 15m

Transfer Learning - Part 23m

Transfer Learning Notebook17m

Convolutional Neural Network Architectures - Part 18m

Convolutional Neural Network Architectures - Part 211m

Convolutional Neural Network Architectures - Part 310m

3 readings

Convolutional Neural Networks Demo (Activity)2m

Transfer Learning Demo (Activity)2m

Summary/Review10m

4 practice exercises

Check for Understanding5m

End of Module Quiz10m

Week

Week 4

2 hours to complete

Recurrent Neural Networks and Long-Short Term Memory Networks

In this module you become familiar with Recursive Neural Networks (RNNs) and Long-Short Term Memory Networks (LSTM), a type of RNN considered the breakthrough for speech to text recongintion. RNNs are frequently used in most AI applications today, and can also be used for supervised learning.

2 hours to complete

7 videos (Total 62 min), 2 readings, 3 quizzes

7 videos

Recurrent Neural Networks (RNNs) - Part 19m

Recurrent Neural Networks (RNNs) - Part 26m

Recurrent Neural Networks Notebook - Part 113m

Recurrent Neural Networks Notebook - Part 25m

Long-Short Term Memory (LSTM) Networks11m

Gated Recurrent Unit - Part 15m

Gated Recurrent Unit - Part 28m

2 readings

Recurrent Neural Networks Demo (Activity)2m

Summary/Review10m

3 practice exercises

Check for Understanding5m

End of Module Quiz15m

Week

Week 5

2 hours to complete

Deep Learning with Autoencoders

In this module you become familiar with Autoencoders, an useful application of Deep Learning for Unsupervised Learning. Autoencoders are a neural network architecture that forces the learning of a lower dimensional representation of data, commonly images. In this module you will learn some Deep learning-based techniques for data representation, how autoencoders work, and to describe the use of trained autoencoders for image applications

2 hours to complete

9 videos (Total 76 min), 2 readings, 3 quizzes

9 videos

Autoencoders - Part 16m

Autoencoders - Part 26m

Variational Autoencoders - Part 14m

Variational Autoencoders - Part 25m

Autoencoders Notebook - Part 114m

Autoencoders Notebook - Part 210m

Autoencoders Notebook - Part 37m

Autoencoders Notebook - Part 49m

Autoencoders Notebook - Part 510m

2 readings

Autoencoders Demo (Activity)2m

Summary/Review10m

3 practice exercises

Check for Understanding5m

End of Module Quiz10m

Week

Week 6

3 hours to complete

Deep Learning Applications and Reinforcement Learning

In this module you become familiar with other novel applications of Neural Networks. You will learn about Generative Adversarial Networks, frequently referred to as GANs, which are an application of Neural Networks to generate new data. Finally, you learn about Reinforcement Learning, one of the big promises for A.I., based on training algorithms by using rewards, instead of using a method to minimize error, which is what we have been using throughout the course.

3 hours to complete

8 videos (Total 72 min), 2 readings, 4 quizzes

8 videos

Generative Adversarial Networks - Part 16m

Generative Adversarial Networks - Part 210m

Additional Topics in Deep Learning7m

Reinforcement Learning (RL)8m

Reinforcement Learning Notebook - Part 111m

Reinforcement Learning Notebook - Part 210m

Reinforcement Learning Notebook - Part 37m

Reinforcement Learning Notebook - Part 49m

2 readings

Reinforcement Learning Demo (Activity)2m

Summary/Review10m

3 practice exercises

Check for Understanding5m

Module 6 Quiz10m

About the IBM Machine Learning Professional Certificate

Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis.

This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries. Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

More questions? Visit the Learner Help Center.

Deep Learning and Reinforcement Learning