Deep Learning and Reinforcement Learning from Coursera

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.

What's inside

Syllabus

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 make 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.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Course covers Neural Networks, a topic that is standard in multiple industries

Taught by instructors recognized in Advanced Computing and Neural Networks

Provides experience with Keras, a go-to library for Deep Learning

Teaches skills that are highly relevant to industry

Suitable for experienced learners with foundational knowledge

Advises to take other courses as pre-requisites

Reviews summary

Solid deep learning, introductory reinforcement learning

Learners say this course offers a fantastic deep dive into Deep Learning, providing a strong foundation from basic neural networks to advanced architectures like CNNs and RNNs. The lectures are clear and concise, making complex topics digestible, and hands-on labs with Keras offer invaluable practical experience. While the Deep Learning modules are consistently praised, the Reinforcement Learning section is frequently noted as a brief introduction, often requiring additional resources for comprehensive understanding. It's highly recommended for aspiring data scientists with a solid existing ML and mathematical background, as some found the pace steep otherwise.

A strong background in math and machine learning is crucial.

"The course also assumes a very strong mathematical background, which can be a hurdle even with the listed prerequisites."

"I struggled a lot with this course. Despite having some ML background, the jump into Deep Learning felt too steep at times..."

"Highly recommended for those looking to build a strong DL foundation."

Well-designed hands-on labs using Keras provide practical experience.

"The hands-on labs were well-designed and provided practical experience with Keras."

"I really enjoyed working through the practical coding assignments, which used relevant datasets and Keras."

"The practical aspect with Keras and real-world examples really made the Deep Learning concepts stick."

"I found the Keras exercises useful for hands-on application."

Provides a comprehensive and strong foundation in Deep Learning.

"This course was a fantastic deep dive into Deep Learning, covering all the essentials from basic neural nets to advanced architectures..."

"I appreciated the comprehensive coverage of Deep Learning techniques. The transfer learning module was particularly insightful..."

"An excellent course that demystifies Deep Learning. The explanation of backpropagation and optimizers was very clear."

"I learned a lot about CNNs and transfer learning. A strong recommendation for anyone serious about DL."

Some learners encountered issues with lab setup and dependencies.

"The labs sometimes had outdated dependencies or environment issues which added to the frustration."

The Reinforcement Learning module is generally brief and high-level.

"The reinforcement learning part was more of an introduction but still valuable."

"The Reinforcement Learning section felt a bit too high-level; I had to seek out additional resources for a more thorough understanding."

"Reinforcement Learning content was underwhelming. It felt rushed and didn't provide the practical depth I expected."

"The RL section was negligible."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deep Learning and Reinforcement Learning with these activities:

Volunteer at a local organization focused on using Artificial Intelligence for social good

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Gain practical experience and contribute to the broader social impact of Artificial Intelligence by volunteering with an organization dedicated to its ethical and beneficial use.

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Identify local organizations working in the field of Artificial Intelligence for social good.
Reach out to the organization and inquire about volunteer opportunities.
Attend volunteer training and orientation.
Participate in projects and initiatives that align with your interests and skills.
Reflect on your experience and its impact on your understanding of Artificial Intelligence.

Review the basics of calculus and linear algebra

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Refresh your understanding of calculus and linear algebra, which are essential mathematical foundations for Deep Learning and Reinforcement Learning.

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Review key concepts in calculus, such as derivatives, integrals, and limits.
Review key concepts in linear algebra, such as matrices, vectors, and eigenvalues.
Practice solving problems involving calculus and linear algebra.
Apply your refreshed knowledge to understand the mathematical concepts underlying Deep Learning and Reinforcement Learning.

Practice backpropagation algorithm with Keras

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Practice the Back Propagation algorithm using Keras to solidify understanding of neural network training.

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Set up your Python development environment with Keras.
Create a simple neural network model with one hidden layer.
Implement the Back Propagation algorithm in Python using Keras.
Train your neural network model on a dataset.
Evaluate the performance of your neural network model.

Four other activities

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Follow a tutorial on how to build a Deep Learning model for text classification using RNNs

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Gain practical experience in building and training a Deep Learning model for text classification using Recurrent Neural Networks.

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Choose a suitable tutorial on text classification with RNNs.
Set up your Python development environment with necessary libraries.
Follow the tutorial step-by-step to build and train your RNN model.
Fine-tune and evaluate your model's performance on a text dataset.

Classify images using pre-trained CNNs using transfer learning

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Gain hands-on experience with transfer learning using pre-trained CNNs for image classification tasks.

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Set up your Python development environment with Keras and TensorFlow.
Load a pre-trained CNN model, such as VGG16 or ResNet50.
Create a new dataset for your image classification task.
Preprocess your images for use with the pre-trained CNN.
Fine-tune the pre-trained CNN model on your new dataset.
Evaluate the performance of your fine-tuned CNN model.

Create a presentation on the applications of Deep Learning in the healthcare industry

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Explore and present real-world applications of Deep Learning in the healthcare industry to enhance comprehension and practical relevance.

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Research and identify applications of Deep Learning in healthcare, such as medical image analysis, drug discovery, and personalized medicine.
Gather data and examples to support your presentation.
Design engaging slides with clear explanations and visuals.
Rehearse your presentation and incorporate feedback.
Present your findings to a target audience.

Participate in a Kaggle competition on image segmentation using Deep Learning

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Engage in a hands-on challenge to apply your Deep Learning skills in image segmentation by participating in a Kaggle competition.

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Familiarize yourself with the Kaggle competition rules and dataset.
Choose an appropriate Deep Learning model for image segmentation.
Train and optimize your model using Kaggle's platform.
Evaluate your model's performance and submit your results.
Analyze your results and learn from the competition.

Career center

Learners who complete Deep Learning and Reinforcement Learning will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers bridge the gap between traditional software engineering and data science. Machine Learning Engineers are capable of collecting raw data, cleaning it for analysis, building machine learning models, and deploying those models to production. This course will help students become Machine Learning Engineers by introducing them to the essential concepts of Deep Learning and Reinforcement Learning, two critical areas of Machine Learning. This course in particular will provide a foundation in the core concepts of Deep Learning and Reinforcement Learning as well as valuable hands-on experience implementing Deep Learning using Keras.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists collect, clean, and analyze data, then use it to make recommendations and predictions. It is typically recommended that Data Scientists possess a strong foundation in statistics, math, and computer science. This course will introduce students to Deep Learning and Reinforcement Learning, two of the most in-demand and powerful tools in a Data Scientist's toolbox.

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Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software systems. Software Engineers may also be involved in the research and development of new technologies. This course will help Software Engineers expand their skillset and become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning. These technologies are becoming increasingly important in a variety of industries, and Software Engineers who are proficient in them will be in high demand.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They are employed by a variety of organizations, including investment banks, hedge funds, and insurance companies. This course will help Quantitative Analysts stay ahead of the curve by introducing them to Deep Learning and Reinforcement Learning, two cutting-edge technologies that are revolutionizing the field of quantitative analysis.

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Research Scientist

Research Scientists conduct research in a variety of fields, including computer science, biology, and physics. This course will help Research Scientists stay at the forefront of their fields by introducing them to Deep Learning and Reinforcement Learning, two of the most promising and rapidly developing areas of research.

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Business Analyst

Business Analysts use data to identify and solve business problems. They work with stakeholders to gather requirements, analyze data, and develop solutions. This course will help Business Analysts become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to solve a variety of business problems.

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Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They use this information to make recommendations and predictions. This course will help Data Analysts become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze data more efficiently and accurately.

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Statistician

Statisticians collect, analyze, and interpret data. They use this information to make predictions and draw conclusions. This course will help Statisticians become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze data more efficiently and accurately.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. They work with organizations to improve efficiency and productivity. This course will help Operations Research Analysts become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to solve a variety of business problems.

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Financial Analyst

Financial Analysts use data to make investment recommendations. They work with clients to assess risk and return, and to develop investment strategies. This course will help Financial Analysts become more effective in their roles by introducing them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze data more efficiently and accurately.

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Actuary

Actuaries use mathematical and statistical models to assess risk. They work with insurance companies and other organizations to develop insurance policies and pricing strategies. This course may be useful for Actuaries as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to assess risk more efficiently and accurately.

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Risk Analyst

Risk Analysts use data to identify and assess risk. They work with organizations to develop risk management strategies. This course may be useful for Risk Analysts as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to identify and assess risk more efficiently and accurately.

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Economist

Economists use data to analyze economic trends and make predictions. They work with governments, businesses, and other organizations to develop economic policies. This course may be useful for Economists as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze economic data more efficiently and accurately.

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Market Researcher

Market Researchers use data to understand consumer behavior. They work with businesses to develop marketing strategies and products. This course may be useful for Market Researchers as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze consumer data more efficiently and accurately.

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UX Researcher

UX Researchers use data to understand user experience. They work with product teams to design and improve user interfaces. This course may be useful for UX Researchers as it will introduce them to Deep Learning and Reinforcement Learning, two powerful technologies that can be used to analyze user data more efficiently and accurately.

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