Introduction to Machine Learning from Coursera

What's inside

Syllabus

Simple Introduction to Machine Learning

The focus of this module is to introduce the concepts of machine learning with as little mathematics as possible. We will introduce basic concepts in machine learning, including logistic regression, a simple but widely employed machine learning (ML) method. Also covered is multilayered perceptron (MLP), a fundamental neural network. The concept of deep learning is discussed, and also related to simpler models.

Basics of Model Learning

In this module we will be discussing the mathematical basis of learning deep networks. We’ll first work through how we define the issue of learning deep networks as a minimization problem of a mathematical function. After defining our mathematical goal, we will introduce validation methods to estimate real-world performance of the learned deep networks. We will then discuss how gradient descent, a classical technique in optimization, can be used to achieve this mathematical goal. Finally, we will discuss both why and how stochastic gradient descent is used in practice to learn deep networks.

Image Analysis with Convolutional Neural Networks

This week will cover model training, as well as transfer learning and fine-tuning. In addition to learning the fundamentals of a CNN and how it is applied, careful discussion is provided on the intuition of the CNN, with the goal of providing a conceptual understanding.

Recurrent Neural Networks for Natural Language Processing

This week will cover the application of neural networks to natural language processing (NLP), from simple neural models to the more complex. The fundamental concept of word embeddings is discussed, as well as how such methods are employed within model learning and usage for several NLP applications. A wide range of neural NLP models are also discussed, including recurrent neural networks, and specifically long short-term memory (LSTM) models.

The Transformer Network for Natural Language Processing

This week we'll cover an Introduction to the Transformer Network, a deep machine learning model designed to be more flexible and robust than Recurrent Neural Network (RNN). We'll start by reviewing several machine learning building blocks of a Transformer Network: the Inner products of word vectors, attention mechanisms, and sequence-to-sequence encoders and decoders. Then, we'll put all of these components together to explore the complete Transformer Network.

Introduction to Reinforcement Learning

This week will cover Reinforcement Learning, a fundamental concept in machine learning that is concerned with taking suitable actions to maximize rewards in a particular situation. After learning the initial steps of Reinforcement Learning, we'll move to Q Learning, as well as Deep Q Learning. We'll discuss the difference between the concepts of Exploration and Exploitation and why they are important.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores models utilized in numerous industries, enhancing learners' potential to solve complex problems

Taught by recognized experts in the field, providing learners with access to cutting-edge knowledge and industry insights

Emphasizes practical application through hands-on exercises, fostering learners' ability to implement machine learning algorithms

Covers a comprehensive range of machine learning models, from basic to advanced, ensuring a solid foundation for learners

Delves into natural language processing techniques, empowering learners to analyze and process textual data with proficiency

Reviews summary

Ml for newbies

learners say this course provides a strong foundation for beginners who want to understand machine learning. Even without a math or programming background, reviewers noted that they were able to grasp the concepts of machine learning. The course is largely positive, with reviewers praising the clear explanations, informative lectures, and helpful examples. According to students, one of the strengths of the course is its focus on intuitive explanations. Instructors use diagrams and real-world examples to make complex topics easier to understand. Reviewers also appreciated the balance between theory and practice, with lectures complemented by hands-on labs and assignments. While the course is generally well-received, some reviewers mentioned that the programming assignments could be improved. They suggested providing more guidance and support to help students complete the assignments successfully. Overall, learners say this course is an excellent resource for beginners who want to learn the fundamentals of machine learning. With its clear explanations, engaging assignments, and supportive instructors, this course is a great starting point for anyone interested in exploring the field.

The course uses diagrams and real-world examples to make complex topics easier to understand.

"Even without a math or programming background, I was able to grasp the concepts of machine learning."

"The course is largely positive, with reviewers praising the clear explanations, informative lectures, and helpful examples."

"Instructors use diagrams and real-world examples to make complex topics easier to understand."

"Reviewers also appreciated the balance between theory and practice, with lectures complemented by hands-on labs and assignments."

The course provides intuitive explanations that make complex topics easy to understand.

"The course is largely positive, with reviewers praising the clear explanations, informative lectures, and helpful examples."

"Instructors use diagrams and real-world examples to make complex topics easier to understand."

"Reviewers also appreciated the balance between theory and practice, with lectures complemented by hands-on labs and assignments."

The course combines theoretical explanations with hands-on labs and assignments.

"Reviewers also appreciated the balance between theory and practice, with lectures complemented by hands-on labs and assignments."

The course is suitable for beginners with no prior knowledge of machine learning.

"learners say this course provides a strong foundation for beginners who want to understand machine learning."

"Even without a math or programming background, reviewers noted that they were able to grasp the concepts of machine learning."

The programming assignments could be improved with more guidance and support.

"While the course is generally well-received, some reviewers mentioned that the programming assignments could be improved."

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 Introduction to Machine Learning with these activities:

Python Review

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Review the fundamentals of Python programming to strengthen your foundation for the course.

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Review basic data types and operators
Practice using control flow statements
Implement basic algorithms using Python syntax

Deep Learning with PyTorch

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Supplement your understanding of PyTorch and deep learning concepts by reviewing this comprehensive guide.

View Deep Learning with PyTorch: Build, train, and... on Amazon

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Read the introductory chapters to grasp the fundamentals
Review specific sections relevant to the course material

Study Group Discussions

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Engage in regular discussions with classmates to clarify concepts, share insights, and support each other's learning.

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Identify challenging topics and prepare questions
Form a study group with peers
Meet regularly to discuss course material and share perspectives

Five other activities

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Show all eight activities

Machine Learning Algorithm Implementation

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Practice implementing various machine learning algorithms in Python to enhance your understanding of their practical applications.

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Implement a linear regression model using PyTorch
Implement a logistic regression model using PyTorch
Implement a simple convolutional neural network using PyTorch

Natural Language Processing with Transformers

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Explore advanced concepts in natural language processing by following tutorials on using transformers for text-related tasks.

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Follow a tutorial on using Hugging Face's Transformers library
Apply transformers to a text classification task
Investigate the inner workings of transformer models

Contribute to Open Source Machine Learning Projects

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Deepen your understanding of machine learning and connect with the community by contributing to open source projects.

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Identify open source projects related to the course topics
Choose an issue or feature to work on
Submit a pull request with your contribution

Machine Learning Model Deployment

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Gain practical experience by deploying your trained machine learning model in a real-world environment.

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Select a suitable deployment platform
Prepare the model for deployment
Monitor and evaluate the deployed model

Machine Learning Project

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深化 your understanding of machine learning by applying it to a real-world problem through a comprehensive project.

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Define the problem statement and gather data
Explore different machine learning models and select the most appropriate one
Build, train, and evaluate the machine learning model
Deploy the model and analyze its performance

Career center

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

Machine Learning Engineer

Machine Learning Engineers design, develop, deploy, and manage machine learning models and applications. They work on a variety of projects, from building self-driving cars to improving healthcare diagnostics. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use machine learning and other data analysis techniques to extract insights from data. They work in a variety of industries, including healthcare, finance, and零售业. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They work in a variety of industries, including banking, hedge funds, and asset management. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

See salaries and explore the career path for Quantitative Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including healthcare, finance, and零售业. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

See salaries and explore the career path for Software Engineer

Business Analyst

Business Analysts use data analysis and modeling techniques to identify and solve business problems. They work in a variety of industries, including healthcare, finance, and零售业. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve complex problems in business and industry. They work in a variety of industries, including manufacturing, transportation, and logistics. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

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Researcher

Researchers conduct original research in a variety of fields, including science, engineering, and the social sciences. They work in a variety of settings, including universities, research institutes, and government agencies. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

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Consultant

Consultants provide advice and expertise to businesses and organizations on a variety of topics, including management, strategy, and finance. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

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Teacher

Teachers develop and deliver lesson plans and teach students in a variety of subjects. They work in a variety of settings, including schools, colleges, and universities. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

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Financial Analyst

Financial Analysts analyze financial data and make investment recommendations. They work in a variety of industries, including banking, hedge funds, and asset management. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

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Economist

Economists study the production, distribution, and consumption of goods and services. They work in a variety of industries, including government, academia, and the private sector. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

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Product Manager

Product Managers develop and manage products for a variety of companies. They work in a variety of industries, including technology, healthcare, and manufacturing. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

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Statistician

Statisticians collect, analyze, and interpret data. They work in a variety of industries, including healthcare, finance, and manufacturing. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

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Market Researcher

Market Researchers conduct research to identify and understand consumer needs. They work in a variety of industries, including marketing, advertising, and product development. This course provides a strong foundation in the mathematical and algorithmic principles of machine learning, and it will help you develop the skills you need to succeed in this challenging and rewarding field.

See salaries and explore the career path for Market Researcher