Machine Learning with Python: A Practical Introduction from edX

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This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

Mostimportantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

What you'll learn

Explain the difference between the two main types of machine learning methods: supervised and unsupervised
Describe Supervised learning algorithms, including classification and regression
Describe Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
Explain how statistical modelling relates to machine learning and how to compare them
Discuss real-life examples of the different ways machine learning affects society
Build a prediction model using classification

What's inside

Learning objectives

Explain the difference between the two main types of machine learning methods: supervised and unsupervised
Describe supervised learning algorithms, including classification and regression
Describe unsupervised learning algorithms, including clustering and dimensionality reduction

Explain how statistical modelling relates to machine learning and how to compare them
Discuss real-life examples of the different ways machine learning affects society
Build a prediction model using classification

Explain the difference between the two main types of machine learning methods: supervised and unsupervised
Describe supervised learning algorithms, including classification and regression
Describe unsupervised learning algorithms, including clustering and dimensionality reduction
Explain how statistical modelling relates to machine learning and how to compare them
Discuss real-life examples of the different ways machine learning affects society
Build a prediction model using classification

Syllabus

Module 1 - Introduction to Machine Learning Applications of Machine Learning Supervised vs Unsupervised Learning Python libraries suitable for Machine Learning

Module 2 - Regression Linear Regression Non-linear Regression Model evaluation methods

Module 3 - Classification K-Nearest Neighbour Decision Trees Logistic Regression Support Vector Machines Model Evaluation

Module 4 - Unsupervised Learning K-Means Clustering Hierarchical Clustering Density-Based Clustering

Module 5 - Recommender Systems Content-based recommender systems Collaborative Filtering

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces learners to key machine learning concepts, including supervised vs. unsupervised learning, statistical modeling, and classification and regression

Provides theoretical knowledge and practical skills through hands-on labs, helping learners apply their theoretical knowledge

Covers a range of popular algorithms, from classification to clustering, exposing learners to various methodologies in machine learning

Suitable for learners interested in understanding the basics of machine learning and its applications

Requires learners to have some familiarity with Python programming

Does not delve deeply into advanced machine learning concepts, such as deep learning or natural language processing

Reviews summary

Python machine learning basics

According to students, this course introduces the basics of machine learning with Python. It is a good starting point for beginners who want to learn more about the subject.

Course is a good introduction to machine learning for beginners.

"This course is introducing the basics of ML but never touch on higher levels like cross-validation, newer ML algorithms etc. More for beginners."

Course only covers the basics of machine learning and doesn't go into more advanced topics.

"This course is introducing the basics of ML but never touch on higher levels like cross-validation, newer ML algorithms etc. More for beginners."

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 Machine Learning with Python: A Practical Introduction with these activities:

Complete the Data-Driven Decision Making with R course on Coursera

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This tutorial will help you learn how to apply machine learning techniques to solve business problems.

Browse courses on Data-Driven Decision Making

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Register for the course.
Complete the video lectures.
Read the course materials.
Complete the hands-on exercises.
Apply what you've learned to a real-world dataset.

Read 'Foundations of Machine Learning' by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar

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Reading this textbook will provide you with a strong theoretical foundation in machine learning and help you grasp the underlying algorithms.

View Foundations of Machine Learning, second edition on Amazon

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Read one section per week.
Complete the exercises at the end of each chapter.
Summarize your understanding of each chapter.

Join a machine learning study group

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Study groups provide a great way to learn from your peers and get support with your machine learning coursework.

Browse courses on Machine Learning

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Find a study group that meets your interests and level of experience.
Attend the study group meetings.
Participate in discussions.
Help other students with their questions.

Four other activities

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Practice Random Forest modeling using Python

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Machine learning models are used in many applications. This exercise will help you gain practical experience with a popular machine learning algorithm.

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Import relevant Python libraries.
Load a dataset.
Build a Random Forest model.
Tune the model parameters.
Evaluate the model.

Attend a machine learning workshop

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Workshops provide an excellent opportunity to learn from experts and get hands-on experience with machine learning.

Browse courses on Machine Learning

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Find a workshop that meets your interests and level of experience.
Register for the workshop.
Attend the workshop.
Take notes and ask questions.

Contribute to the scikit-learn project

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This project will give you experience with one of the most popular machine learning libraries in Python.

Browse courses on Machine Learning

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Find an issue to work on.
Fork the project.
Make your changes.
Submit a pull request.

Write a blog post about machine learning

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Writing a blog post will help you solidify your understanding of machine learning and share your knowledge with others.

Browse courses on Machine Learning

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Choose a topic that you are familiar with.
Research your topic and gather information.
Write your blog post.
Edit and proofread your blog post.
Publish your blog post online.

Career center

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

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. They work closely with data scientists to identify the right problems to solve and the best algorithms to use. This course can help you build a strong foundation in machine learning, including supervised and unsupervised learning, feature engineering, and model evaluation.

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Data Scientist

A Data Scientist is responsible for using data to solve business problems. They work with data analysts to collect and clean data, and then use machine learning and other statistical techniques to analyze data and identify trends. This course can help you develop the skills you need to succeed as a Data Scientist, including data mining, statistical modeling, and machine learning.

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Data Analyst

A Data Analyst is responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use their findings to make recommendations that can help businesses make better decisions. This course can help you develop the skills you need to succeed as a Data Analyst, including data mining, statistical modeling, and machine learning.

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Statistician

A Statistician is responsible for collecting, analyzing, and interpreting data. They work with businesses, governments, and other organizations to help them make informed decisions. This course can help you develop the skills you need to succeed as a Statistician, including data analysis, statistical modeling, and probability.

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Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical techniques to analyze data and make investment decisions. They work with portfolio managers to develop and implement investment strategies. This course can help you develop the skills you need to succeed as a Quantitative Analyst, including statistical modeling, machine learning, and financial analysis.

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Data Engineer

A Data Engineer is responsible for designing, building, and maintaining data pipelines. They work with data scientists and other stakeholders to ensure that data is available and accessible for analysis. This course can help you develop the skills you need to succeed as a Data Engineer, including data warehousing, data integration, and data quality.

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Operations Research Analyst

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Business Analyst

A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. They work with stakeholders to understand their needs and then develop and implement solutions that meet those needs. This course can help you develop the skills you need to succeed as a Business Analyst, including data analysis, process improvement, and project management.

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Product Manager

A Product Manager is responsible for managing the development and launch of new products. They work with engineers, designers, and marketers to ensure that products meet the needs of customers. This course can help you develop the skills you need to succeed as a Product Manager, including product development, marketing, and customer research.

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Market Research Analyst

A Market Research Analyst is responsible for collecting, analyzing, and interpreting data about markets and customers. They use their findings to help businesses make better decisions about product development, marketing, and sales. This course can help you develop the skills you need to succeed as a Market Research Analyst, including data collection, data analysis, and market research.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. They work with users to understand their needs and then design and build software that meets those needs. This course can help you develop the skills you need to succeed as a Software Engineer, including programming, data structures, and algorithms.

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Project Manager

A Project Manager is responsible for planning, executing, and closing projects. They work with stakeholders to define project scope, develop project plans, and track project progress. This course can help you develop the skills you need to succeed as a Project Manager, including project planning, risk management, and communication.

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Management Consultant

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Financial Analyst

A Financial Analyst is responsible for analyzing financial data and making investment recommendations. They work with clients to help them make informed investment decisions. This course can help you develop the skills you need to succeed as a Financial Analyst, including financial modeling, valuation, and investment analysis.

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Actuary

An Actuary is responsible for assessing and managing risk. They work with insurance companies, pension funds, and other organizations to help them make sound financial decisions. This course can help you develop the skills you need to succeed as an Actuary, including probability, statistics, and financial modeling.

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