Intro to Machine Learning from Udacity

What's inside

Syllabus

Meet with Sebastian and Katie to discuss machine learning.

Learn about classification, training and testing, and run a naive Bayes classifier using Scikit Learn.

Build an intuition about how support vector machines (SVMs) work and implement one using scikit-learn.

Learn about how the decision tree algorithm works, including the concepts of entropy and information gain.

In this mini project, you will extend your toolbox of algorithms by choosing your own algorithm to classify terrain data, including k-nearest neighbors, AdaBoost, and random forests.

Find out about the Enron data set used in the next lessons and mini-projects.

See how we can model continuous data using linear regression.

Sebastian discusses outlier detection and removal.

Learn about what unsupervised learning is and find out how to use scikit-learn's k-means algorithm.

Learn about feature rescaling and find out which algorithms require feature rescaling before use.

Katie discusses when and why to use feature selection, and provides some methods for doing this.

Find out how to use text data in your machine learning algorithm.

Learn about data dimensionality and reducing the number of dimensions with principal component analysis (PCA).

Learn more about testing, training, cross validation, and parameter grid searches in this lesson.

How do we know if our classifier is performing well? Katie discusses different evaluation metrics for classifiers in this lesson.

Spend some time reflecting on the course material with Sebastian and Katie!

Final Project

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops core foundational knowledge in machine learning and is taught by experienced instructors

Provides hands-on practice with supervised and unsupervised learning algorithms, preparing learners for practical applications in the field

Suitable for beginners seeking to enter the field of machine learning and develop foundational skills

Covers a wide range of topics, from supervised to unsupervised learning, feature engineering, model evaluation, and more

Leverages industry-standard tools and libraries such as Scikit-Learn, providing learners with practical experience in real-world scenarios

Course prerequisites may be required for learners with no prior background in machine learning or programming

Reviews summary

Intro to ml using python

This course is a recommended way to get started with Machine Learning, especially if you have little to no experience in the field. It features lots of hands-on exercises and practical applications, but lacks in mathematical depth.

Delivers easy to understand content

"Some core concepts are explained in an easy way."

Focuses on practical use of Python and Scikit Learn

"Udacity's Intro to Machine Learning is an introduction to data analysis using Python and the sklearn package."

Beginner-friendly course

"Nice for a beginner who just wants an intro to machine learning and not delve deeper into the implementation and mathematics behind the algorithms."

Quizzes have missing information

"I hated how the quiz questions weren't clearly written out (some missing information was said instead of shown visually)."

Could go deeper into mathematical concepts

"The math is sloppy and confusing."

"The mathematical level is broken down to high school level, which is good for the intuitive understanding, but in my opinion the level is far too low to learn anything serious..."

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 Intro to Machine Learning with these activities:

Organize notes

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Help you refresh your foundational understanding of machine learning and algorithms

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Review your notes from previous courses or tutorials on machine learning.
Identify sections in your notes that need more attention.
Organize your notes by topic and subtopic.
Summarize key concepts and algorithms in your own words.
Test your understanding by attempting practice problems or quizzes online.

Explore Machine Learning Libraries

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Help you become familiar with popular machine learning libraries and their functionalities

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Choose a machine learning library such as scikit-learn or TensorFlow.
Follow online tutorials or documentation to learn about the library's basic functions.
Experiment with different algorithms and data sets using the library.
Explore advanced features of the library, such as hyperparameter tuning or data visualization.

Classification Drills

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Help you strengthen your understanding of different classification algorithms and their applications

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Solve practice problems involving classification tasks.
Implement classification algorithms from scratch using Python or another programming language.
Analyze the results of your algorithms and identify areas for improvement.
Participate in online coding challenges or competitions related to classification.

Four other activities

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Show all seven activities

Machine Learning Study Group

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Help you reinforce your learning, clarify concepts, and exchange ideas with peers

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Form a study group with classmates.
Meet regularly to discuss course material, solve problems together.
Share resources, such as notes, practice questions, or online tutorials.
Provide each other with feedback and support.

Mini Machine Learning Project

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Help you apply your learning by building a small-scale machine learning project

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Identify a real-world problem that can be solved using machine learning.
Collect and prepare a data set for your project.
Choose and implement a machine learning algorithm to solve your problem.
Evaluate the performance of your algorithm and make necessary adjustments.
Present your project to classmates or colleagues for feedback.

Attend a Machine Learning Workshop

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Help you gain practical experience and learn from experts in the field

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Search for upcoming machine learning workshops in your area or online.
Select a workshop that aligns with your interests and learning goals.
Attend the workshop and actively participate in discussions and activities.
Network with other attendees and instructors.
Follow up on any resources or materials provided at the workshop.

Contribute to an Open-Source Machine Learning Project

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Help you gain hands-on experience, build your portfolio, and contribute to the machine learning community

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Identify an open-source machine learning project that interests you.
Read the project documentation and understand its goals and architecture.
Find a small task or issue to work on and submit a pull request.
Collaborate with other contributors and the project maintainers.
Document your contributions and share your experience with others.

Career center

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

Machine Learning Engineer

A Machine Learning Engineer designs and builds machine learning models that can be used to solve real-world problems. This course can help you develop the skills you need to be successful in this role by teaching you about the fundamentals of machine learning, including supervised learning, unsupervised learning, and deep learning. You will also learn how to use software tools such as Python and R to implement machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist uses data to solve business problems. This course can help you develop the skills you need to be successful in this role by teaching you about machine learning algorithms, data visualization techniques, and statistical methods. You will also learn how to use software tools such as Python and R to analyze data and communicate your findings to non-technical audiences.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst is responsible for collecting, cleaning, and analyzing data to find trends and patterns. This course can help you develop the skills you need to be successful in this role by teaching you about machine learning algorithms, data visualization techniques, and statistical methods. You will also learn how to use software tools such as Python and R to analyze data.

See salaries and explore the career path for Data Analyst

Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course can help you develop the skills you need to be successful in this role by teaching you about the fundamentals of computer science, including data structures, algorithms, and software design. You will also learn how to use software tools such as Python and Java to develop software applications.

See salaries and explore the career path for Software Engineer

Product Manager

A Product Manager is responsible for the development and marketing of a product. This course can help you develop the skills you need to be successful in this role by teaching you about the product development process, including market research, product design, and product launch. You will also learn how to use software tools such as Jira and Confluence to manage product development.

See salaries and explore the career path for Product Manager

Business Analyst

A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. This course can help you develop the skills you need to be successful in this role by teaching you about business analysis techniques, including process mapping, data analysis, and financial modeling. You will also learn how to use software tools such as Visio and Excel to create business analysis reports.

See salaries and explore the career path for Business Analyst

Financial Analyst

A Financial Analyst is responsible for analyzing financial data and making investment recommendations. This course can help you develop the skills you need to be successful in this role by teaching you about financial analysis techniques, including ratio analysis, discounted cash flow analysis, and regression analysis. You will also learn how to use software tools such as Excel and Bloomberg to analyze financial data.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

A Marketing Analyst is responsible for analyzing marketing data and making marketing recommendations. This course can help you develop the skills you need to be successful in this role by teaching you about marketing analysis techniques, including market research, customer segmentation, and campaign analysis. You will also learn how to use software tools such as Google Analytics and Adobe Analytics to analyze marketing data.

See salaries and explore the career path for Marketing Analyst

Operations Analyst

An Operations Analyst is responsible for analyzing business operations and identifying opportunities for improvement. This course can help you develop the skills you need to be successful in this role by teaching you about operations analysis techniques, including process mapping, data analysis, and simulation modeling. You will also learn how to use software tools such as Visio and Excel to create operations analysis reports.

See salaries and explore the career path for Operations Analyst

Risk Analyst

A Risk Analyst is responsible for identifying and assessing risks to an organization. This course can help you develop the skills you need to be successful in this role by teaching you about risk analysis techniques, including risk identification, risk assessment, and risk mitigation. You will also learn how to use software tools such as Risk Manager and Palisade DecisionTools to analyze risks.

See salaries and explore the career path for Risk Analyst

Statistician

A Statistician is responsible for collecting, analyzing, and interpreting data. This course can help you develop the skills you need to be successful in this role by teaching you about statistical methods, including data collection, data analysis, and statistical modeling. You will also learn how to use software tools such as R and SAS to analyze data.

See salaries and explore the career path for Statistician

Actuary

An Actuary is responsible for assessing and managing financial risks. This course may be useful in helping you develop the skills you need to be successful in this role by teaching you about financial analysis techniques, including ratio analysis, discounted cash flow analysis, and regression analysis. You will also learn how to use software tools such as Excel and Bloomberg to analyze financial data.

See salaries and explore the career path for Actuary

Economist

An Economist is responsible for studying the economy and making economic forecasts. This course may be useful in helping you develop the skills you need to be successful in this role by teaching you about economic principles, including microeconomics, macroeconomics, and econometrics. You will also learn how to use software tools such as R and EViews to analyze economic data.

See salaries and explore the career path for Economist

Financial Planner

A Financial Planner is responsible for helping clients achieve their financial goals. This course may be useful in helping you develop the skills you need to be successful in this role by teaching you about financial planning techniques, including investment planning, retirement planning, and estate planning. You will also learn how to use software tools such as MoneyGuidePro and eMoney Advisor to create financial plans.

See salaries and explore the career path for Financial Planner

Insurance Agent

An Insurance Agent is responsible for selling and servicing insurance policies. This course may be useful in helping you develop the skills you need to be successful in this role by teaching you about insurance products, including life insurance, health insurance, and property insurance. You will also learn how to use software tools such as InsureMe and AgentPro to manage insurance policies.

See salaries and explore the career path for Insurance Agent