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Supervised Learning

The Supervised Learning course covers how supervised learning models work and how to quickly and efficiently code them using the Sklearn libraries in Python. The most popular and commonly used supervised learning models are taught including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Regression, Random Forest and Decision Trees. The course covers how each algorithm works and types of problems that the algorithm is good for solving. But students will not need to spend large amounts of time coding the algorithms, because Sklearn normally reduces the creation and training of the algorithm down to just a few lines of code. The course ends with a capstone project that allows students to demonstrate their knowledge

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What's inside

Learning objectives

Understand how the k-nearest neighbor algorithm works and how to code it in python
Understand how the support vector machine algorithm works and how to code it in python
Understand how different types of regression algorithm work and how to code them in python

Understand how the random forest algorithm works and how to code it in python
Understand how the decision trees algorithm works and how to code it in python
Evaluate different supervised learning algorithms for a given problem

Understand how the k-nearest neighbor algorithm works and how to code it in python
Understand how the support vector machine algorithm works and how to code it in python
Understand how different types of regression algorithm work and how to code them in python
Understand how the random forest algorithm works and how to code it in python
Understand how the decision trees algorithm works and how to code it in python
Evaluate different supervised learning algorithms for a given problem

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses Sklearn, a popular Python library, which simplifies the implementation of machine learning algorithms, making it accessible to learners

Includes a capstone project, providing an opportunity to apply learned concepts and demonstrate proficiency in supervised learning techniques

Explores KNN, SVM, Regression, Random Forest, and Decision Trees, which are fundamental algorithms in supervised learning

Requires coding in Python, so learners without prior experience may need to acquire basic Python skills beforehand

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Reviews summary

Practical supervised learning with sklearn

According to learners, this course provides a solid introduction to supervised learning using the Scikit-learn library in Python. Many find it highly practical and effective for quickly getting started with implementing common algorithms like KNN, SVM, and Random Forests. The hands-on coding examples and the capstone project are frequently highlighted as positive aspects that help solidify understanding and application. However, some students note that the course lacks sufficient theoretical or mathematical depth, and a recurring point, particularly in recent reviews, is the presence of potentially outdated library versions in the course materials which can lead to dependency issues.

Project helps apply learned skills.

"The capstone project was a really valuable experience that allowed me to consolidate everything I learned throughout the course."

"I enjoyed the final project; it was a practical way to test my understanding and ability to apply the different algorithms."

"Completing the capstone gave me confidence in my ability to use Sklearn for basic ML tasks."

"The hands-on project at the end was the best part for applying the concepts."

Accessible introduction for newcomers.

"As someone relatively new to machine learning, I found this course to be a very accessible starting point."

"The way the concepts are explained makes it easy for beginners to grasp the basics of supervised learning."

"It's a great first step if you have some Python background but are new to machine learning algorithms and libraries like Sklearn."

"Found it perfect for getting my feet wet in applied machine learning."

Teaches practical implementation using Sklearn.

"The course is very practical and focuses on teaching you how to use Sklearn for implementing ML models, which is exactly what I needed."

"I really appreciate the focus on using the Sklearn library; it makes applying these concepts much more straightforward."

"If you want to learn how to *code* supervised learning models using standard libraries quickly, this course is great."

"The examples provided using Scikit-learn were clear and easy to follow, demonstrating how to get things done fast."

Library versions in videos may be old.

"Many of the libraries, especially scikit-learn, used in the video demonstrations seem outdated, which caused dependency issues when I tried to run the code myself."

"Be prepared to look up current library documentation, as the versions used in the course might not match what's standard now."

"Running the provided code required some troubleshooting due to deprecated features in newer library versions. An update would be very helpful."

"Encountered some errors because the library versions referred to in the course material were quite old."

Mathematical/theoretical concepts are light.

"While the course shows you *how* to use the algorithms, it doesn't really dive deep into the underlying math or theory. You might need external resources for that."

"This course definitely gives you a good practical start, but it only scratches the surface of the theoretical underpinnings."

"I was hoping for more explanation on *why* the algorithms work, not just *how* to implement them with a library."

"If you are looking for a deep dive into the mathematics behind machine learning, this is not the course for you. It's more applied."

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 Supervised Learning with these activities:

Review Linear Algebra Fundamentals

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Solidify your understanding of linear algebra concepts, which are foundational for many supervised learning algorithms, especially Support Vector Machines and Regression.

Browse courses on Linear Algebra

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Review matrix operations (addition, multiplication, transpose).
Study vector spaces, linear independence, and basis vectors.
Practice solving systems of linear equations.

Brush Up on Python and Sklearn Basics

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Familiarize yourself with Python syntax and the Sklearn library, which are heavily used throughout the course for implementing supervised learning models.

Browse courses on Sklearn

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Review basic Python syntax and data structures.
Practice using Sklearn for data splitting and model fitting.
Familiarize yourself with common Sklearn functions.

Implement KNN from Scratch

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Reinforce your understanding of the K-Nearest Neighbor algorithm by implementing it from scratch without using Sklearn. This will help you understand the underlying mechanics.

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Write a function to calculate the distance between two data points.
Implement the KNN algorithm to predict the class of a new data point.
Test your implementation on a sample dataset.

Four other activities

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Follow a Regression Tutorial with Real-World Data

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Deepen your understanding of regression techniques by following a tutorial that uses a real-world dataset. This will expose you to practical challenges and data preprocessing steps.

Browse courses on Regression

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Find a tutorial that uses a dataset relevant to your interests.
Follow the tutorial step-by-step, paying attention to data cleaning and feature engineering.
Experiment with different regression models and evaluate their performance.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Expand your knowledge of machine learning and Sklearn by reading this popular book. It provides practical examples and in-depth explanations of the algorithms covered in the course.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters related to the supervised learning algorithms covered in the course.
Work through the code examples and experiment with different parameters.
Try to apply the techniques learned to your own projects.

Build a Classification Model for Image Recognition

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Apply your knowledge of supervised learning to a real-world problem by building a classification model for image recognition. This project will solidify your understanding of model selection, training, and evaluation.

Browse courses on Classification

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Choose an image dataset (e.g., MNIST, CIFAR-10).
Preprocess the data and split it into training and testing sets.
Train a supervised learning model (e.g., SVM, Random Forest) on the training data.
Evaluate the model's performance on the testing data.

Create a Blog Post Explaining SVM Kernels

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Solidify your understanding of Support Vector Machine kernels by writing a blog post that explains them in a clear and concise manner. This will force you to think critically about the concepts and communicate them effectively.

Browse courses on Support Vector Machines

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Research different types of SVM kernels (linear, polynomial, RBF).
Explain how each kernel works and its advantages and disadvantages.
Provide examples of when to use each kernel.
Write a clear and concise blog post with visuals.

Career center

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

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models, and this course helps build a foundation for that work. The course's focus on supervised learning algorithms like K-Nearest Neighbor, Support Vector Machines, and Random Forests provides a practical understanding of common model types. This knowledge is very helpful in training models and evaluating their performance, a central task for a Machine Learning Engineer. This course, with its emphasis on rapidly coding models using Python's Sklearn libraries, directly applies to the workflow of a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Many data scientists use machine learning models to extract insights from data, and this course is valuable to data scientists pursuing such work. The course covers essential supervised learning algorithms, from K-Nearest Neighbor to Support Vector Machines, along with regression and tree-based methods. The ability to quickly implement these models using Sklearn, as taught in this course, helps accelerate the data scientist's workflow. The capstone project also allows a data scientist to demonstrate knowledge of common machine learning models. A data scientist will be able to better select the correct model for a given problem after taking this course.

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Artificial Intelligence Specialist

An Artificial Intelligence Specialist develops and implements AI solutions, and this course may be helpful in this endeavor. The course introduces a variety of supervised learning models, including K-Nearest Neighbor, Support Vector Machines, and regression techniques. The focus on practical coding using Sklearn libraries provides a hands-on approach. This course may be useful for an Artificial Intelligence Specialist when selecting the proper model to use, a skill taught in the course. A capstone project allows the Artificial Intelligence Specialist to gain experience with deploying models.

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Data Analyst

A data analyst uses data to generate insights and recommendations, and this course may be useful for this work. While not exclusively focused on data analysis, the course's overview of different machine learning models such as K-Nearest Neighbor, Support Vector Machines, and regression provides a helpful toolkit for a data analyst. The course also teaches practical coding skills using Python's Sklearn libraries. A data analyst may find that this course will be beneficial when trying to explore more advanced modeling in their work when their current knowledge is lacking.

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Research Scientist

A Research Scientist often explores new algorithms and methods in machine learning, and this course may be beneficial as a component of that. This course introduces several common algorithms like K-Nearest Neighbor, Support Vector Machines, and decision trees. The emphasis on understanding the inner workings of each algorithm, alongside practical coding skills using Sklearn, provides a helpful foundation for a Research Scientist. The capstone project allows a research scientist to demonstrate knowledge of common machine learning models. A research scientist should have broad knowledge of models, and this course will help provide that.

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Business Intelligence Analyst

A Business Intelligence Analyst gathers and analyzes data to inform business decisions, and this course may be helpful for that work. The course introduces a variety of supervised learning techniques which include K-Nearest Neighbor, Support Vector Machines and tree based models. While not the core focus of business intelligence, understanding these algorithms and how to use them with Python's Sklearn library helps add to the analyst's toolkit. The capstone project allows a Business Intelligence Analyst to test a model's performance. This course may assist a Business Intelligence Analyst in generating unique insights.

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Quantitative Analyst

A Quantitative Analyst, sometimes called a quant, develops mathematical and statistical models. This course may be useful in such a role. The course covers common supervised learning methods such as K-Nearest Neighbor, Support Vector Machines, regression, and tree-based models. A quant may find it useful to learn how to use these types of machine learning models. The practical coding approach with Python's Sklearn libraries is valuable for a quant. The course may help a quant better decide which model to use in a particular situation.

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Software Developer

A software developer often incorporates machine learning models into applications, making this course helpful for some software developers. The course introduces supervised learning algorithms like K-Nearest Neighbor, Support Vector Machines, and regression techniques. The practical coding in Python with Sklearn is directly relevant to a Software Developer who wants to add machine learning to software they are building. The course may assist a Software Developer who wants to build software that is more intelligent. A Software Developer will also gain a broader knowledge of ML after taking the course.

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Statistician

A statistician analyzes data to draw conclusions and create models, and this course may be useful to that end. The course covers various supervised learning models, including K-Nearest Neighbor, Support Vector Machines, and regression. A statistician may find the practical coding with Python's Sklearn library useful when testing or implementing models. The course may help a statistician understand the power and scope of different types of machine learning models. A statistician may benefit from the real world application of models that this course allows.

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Bioinformatician

A bioinformatician uses computational tools to analyze biological data, and this course may be useful to a bioinformatician. The course covers supervised learning algorithms like K-Nearest Neighbor, Support Vector Machines, and tree-based methods. The focus on practical coding using Sklearn in Python allows a bioinformatician to add machine learning to the tools they might use to explore large biological datasets. A bioinformatician may benefit from applying the models they learned to biological data. The capstone project may give a Bioinformatician a way to test newly learned skills.

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Financial Analyst

A financial analyst often uses data and models to make financial recommendations, and this course may be useful for that purpose. The course covers several supervised learning methods, including K-Nearest Neighbor, regression, and tree based-models. A financial analyst may find the coding exercises in Python using Sklearn helpful when trying to explore new approaches to problems. The course may be helpful when trying to find patterns in datasets. A financial analyst will learn how to select the proper model for the problem at hand.

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Operations Research Analyst

An Operations Research Analyst uses analytical methods to improve efficiency and effectiveness, and this course may be useful for some of these analysts. The course introduces supervised learning models including K-Nearest Neighbor, Support Vector Machines, and regression techniques. The focus on implementation using Python Sklearn libraries is helpful when trying to apply models to real world problems. An Operations Research Analyst may find it useful when their current knowledge is not sufficient to address machine learning problems. This course may benefit the workflow of an Operations Research Analyst.

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Marketing Analyst

A marketing analyst uses data to understand customer behavior and optimize marketing campaigns, and this course may be useful for that purpose. The course covers supervised learning techniques such as K-Nearest Neighbor, Support Vector Machines, and decision trees. The practical coding with Python's Sklearn library may be useful when a marketing analyst wants to better target specific customer segments or generate more accurate predictions for marketing outcomes. A Marketing Analyst may benefit from the capstone project, which allows them to demonstrate the skills they learned.

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Economist

An economist studies economic systems, and models. This course may be useful for an economist to improve their understanding of how models work. The course covers several supervised learning methods, including K-Nearest Neighbor, Support Vector Machines, and regression. An economist might find the coding exercises in Python using Sklearn helpful to understand the practical implementation of models. This course may allow an economist to explore alternative ways to model complex systems. An economist may find it helpful to be able to apply practical models.

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Actuary

An actuary assesses risk and uncertainty using statistical and mathematical methods, and this course may be beneficial for some actuaries. The course covers several supervised learning techniques, including K-Nearest Neighbor, Support Vector Machines, and tree based methods. An actuary might find the hands-on coding experience with Python's Sklearn helpful. The course may provide an actuary with additional tools that they can use to assess risk. An actuary may find it helpful to be able to work with advanced models.

See salaries and explore the career path for Actuary

Reading list

We've selected one 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 Supervised Learning.

Hands-On Machine Learning with Scikit-Learn, Keras,...

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Provides a comprehensive overview of machine learning concepts and techniques, with a strong focus on practical implementation using Sklearn, Keras, and TensorFlow. It covers all the supervised learning algorithms taught in the course in detail, with code examples and real-world case studies. This book is valuable as both a reference and for additional reading to deepen your understanding of the material. It is commonly used as a textbook in machine learning courses.

Hands-On Machine Learning with Scikit-Learn, Keras,...

Paperback

Hands-On Machine Learning with Scikit-Learn, Keras,...

Paperback

$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

Kindle Edition

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