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Machine Learning Algorithms

Supervised Learning Tip to Tail

This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.

To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).

This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

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What's inside

Syllabus

Classification using Decision Trees and k-NN

Welcome to Supervised Learning, Tip to Tail! This week we'll go over the basics of supervised learning, particularly classification, as well as teach you about two classification algorithms: decision trees and k-NN. You'll get started programming on the platform through Jupyter notebooks and start to familiarize yourself with all the issues that arise when using machine learning for classification.

Functions for Fun and Profit

Welcome to the second week of the course! In this week you'll learn all about regression algorithms, the other side of supervised learning. We'll introduce you to the idea of finding lines, optimization criteria, and all the associated issues. Through regression we'll see the interactions between model complexity and accuracy, and you'll get a first taste of how regression and classification might relate.

Regression for Classification: Support Vector Machines

This week we'll be diving straight in to using regression for classification. We'll describe all the fundamental pieces that make up the support vector machine algorithms, so that you can understand how many seemingly unrelated machine learning algorithms tie together. We'll introduce you to logistic regression, neural networks, and support vector machines, and show you how to implement two of those.

Contrasting Models

Now at the tail end of the course, we're going to go over how to know how well your model is actually performing and what you can do to get even better performance from it. We'll review assessment questions particular to regression and classification, and introduce some other tools that really help you analyze your model performance. The topics covered this week aim to give you confidence in your models, so you're ready to unlock the power of machine learning for your business goals.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Offers hands-on labs, interactive materials, and projects

Helps learners understand and implement supervised learning

Employs real case studies, enhancing practical relevance

Covers essential data preparation steps and tackles common ML issues

Suitable for beginners with basic programming and math knowledge

Introduces industry-standard techniques with a focus on practicality

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Reviews summary

Relevant supervised ml algorithms

Learners say that Machine Learning Algorithms: Supervised Learning Tip to Tail is an instructional course that provides a foundation in Supervised Machine Learning. The course briefly covers commonly used supervised learning models and processes. According to students, Anna Koop, the instructor, is knowledgeable and has a clear teaching style. Most reviewers mention that the course is high-level and lacks hands-on practice exercises, but some don't mind. Others note that the video lectures are fast-paced and include complex theory that can be difficult for beginners to grasp. Despite these issues, learners say this course is a great option to gain a general understanding of the different supervised machine learning algorithms before diving into more advanced topics.

Provides a broad understanding of supervised machine learning algorithms.

"Excellent course for an overview of different ML algorithms."

"This is an excellent course which goes into some depth on the different ML models and underlying complexity but it avoids getting bogged down into the details too much."

"The course is made from a perspective of giving insights in process and not too many mathematical details."

Video lectures are delivered quickly with complex theory.

"The content was good but the videos went too fast and too much theory was involved."

"Dr. Koop speaks very very fast though."

Lacks hands-on practice exercises.

"This course is a great overview of ML concepts."

"I did not give 5 stars because the labs need to be improved."

"I am aware of the vastness of the field, and maybe that's why they kept the course instructor centered, but I still expect courses to push learners more in trying out the methods themselves and learning by doing."

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 Algorithms: Supervised Learning Tip to Tail with these activities:

Review linear algebra fundamentals

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Linear algebra forms the mathematical foundation for many machine learning algorithms.

Browse courses on Linear Algebra

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Revisit basic linear algebra concepts (e.g., vectors, matrices, transformations)
Practice solving linear equations and matrix manipulations

Attend a workshop on applied machine learning

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Hands-on workshops offer an immersive environment for practical application and expert guidance.

Browse courses on Machine Learning

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Research and identify relevant workshops
Register for a workshop that aligns with your learning goals
Actively participate in the workshop activities and discussions

Practice decision trees from scratch

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Reinforce understanding of decision tree concepts by implementing them without external libraries.

Browse courses on Decision Trees

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Review the theory of decision trees
Write a function to calculate the entropy of a dataset
Write a function to split a dataset using a given attribute
Write a function to build a decision tree recursively

Five other activities

Expand to see all activities and additional details

Show all eight activities

Create a comprehensive study guide

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Organizing and reviewing course materials reinforces knowledge and facilitates effective recall.

Browse courses on Supervised Learning

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Gather all relevant course materials (e.g., notes, assignments, slides)
Identify key concepts and organize them logically
Summarize and condense the information into a concise and structured format

Join a study group for supervised learning

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Engaging with peers through discussions and shared problem-solving fosters deeper understanding.

Browse courses on Supervised Learning

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Find or create a study group with like-minded individuals
Establish regular meeting times and create a study schedule
Collaborate on solving problems, discussing concepts, and sharing resources

Develop a machine learning model using k-NN

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Practical experience in building a k-NN model will solidify your understanding and prepare you for real-world applications.

Browse courses on K-Nearest Neighbors

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Choose a dataset and define the problem statement
Implement the k-NN algorithm
Evaluate the model's performance using metrics
Tune the hyperparameters of the model (e.g., k)

Tutorial on SVM hyperparameter tuning

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SVM hyperparameter tuning can be complex but it's crucial for optimal performance. This tutorial will break it down.

Browse courses on Support Vector Machines

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Understand the different SVM hyperparameters
Learn about cross-validation and grid search
Implement hyperparameter tuning using a library or framework

Contribute to an open-source machine learning library

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Practical experience in contributing to open-source projects enhances your understanding of real-world machine learning development.

Browse courses on Machine Learning

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Identify an open-source machine learning library that aligns with your interests
Read the documentation and familiarize yourself with the codebase
Identify a feature or improvement that you can contribute
Implement your contribution and submit a pull request

Career center

Learners who complete Machine Learning Algorithms: Supervised Learning Tip to Tail will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists implement and build machine learning algorithms to solve complex business problems. This course may be useful, as it provides a foundation in supervised learning algorithms, including decision trees, k-NN, and support vector machines. These algorithms are commonly used in data science for classification and regression tasks.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use data to make informed decisions and solve business problems. This course may be useful, as it provides a foundation in supervised learning algorithms, which are commonly used in data analysis for tasks such as classification and prediction.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers design, build, and maintain machine learning models. This course may be useful, as it provides a foundation in supervised learning algorithms, which are commonly used in machine learning for tasks such as classification and prediction.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians use data to make informed decisions and solve problems. This course may be useful, as it provides a foundation in supervised learning algorithms, which are commonly used in statistics for tasks such as classification and prediction.

See salaries and explore the career path for Statistician

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the performance of software systems.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers build and maintain data pipelines to make data available for analysis. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the efficiency of data pipelines.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to make investment decisions. This course may be useful, as it provides a foundation in supervised learning algorithms, which are commonly used in quantitative analysis for tasks such as risk assessment and portfolio optimization.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of risk assessments.

See salaries and explore the career path for Actuary

Market Research Analyst

Market Research Analysts conduct research to identify and understand consumer needs. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of market research surveys.

See salaries and explore the career path for Market Research Analyst

Human Factors Engineer

Human Factors Engineers design products and systems to be safe and使いやすい. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of human factors studies.

See salaries and explore the career path for Human Factors Engineer

User Experience Researcher

User Experience Researchers design and conduct research to improve the user experience of products and services. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of user research studies.

See salaries and explore the career path for User Experience Researcher

Business Analyst

Business Analysts identify and solve business problems. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the efficiency of business processes.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve business problems. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the efficiency of operations.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of financial forecasts.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts identify and assess risks to an organization. This course may be useful, as it provides a foundation in supervised learning algorithms, which can be used to improve the accuracy of risk assessments.

See salaries and explore the career path for Risk Analyst

Reading list

We've selected 17 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning Algorithms: Supervised Learning Tip to Tail.

The Elements of Statistical Learning

Save

Provides a comprehensive overview of statistical learning, covering a wide range of topics such as supervised learning, unsupervised learning, and ensemble methods. It valuable resource for anyone looking to gain a deep understanding of the theoretical foundations of statistical learning.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

The Elements of Statistical Learning: Data Mining,...

Kindle Edition

$$$

Deep Learning

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Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics such as supervised learning, unsupervised learning, and Bayesian inference. It valuable resource for anyone looking to gain a deep understanding of the theoretical foundations of machine learning.

Deep Learning: Foundations and Concepts

Hardcover

$$$

Deep Learning

Save

Provides a comprehensive overview of deep learning, covering a wide range of topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone looking to gain a deep understanding of deep learning.

Machine Learning Algorithms

Supervised Learning Tip to Tail

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Relevant supervised ml algorithms

Activities

Career center

Reading list

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