Unsupervised Learning with Python: Step-by-Step Tutorial!

Unsupervised Learning with Python: Step-by-Step Tutorial!, available at $19.99, has an average rating of 2.85, with 44 lectures, 2 quizzes, based on 10 reviews, and has 103 subscribers.

You will learn about Explore various Python libraries, including numpy, pandas, scikit-learn, matplotlib, seaborn and plotly Gain in-depth knowledge of Principle Component Analysis and use it to effectively manage noisy datasets. Discover the power of PCA and K-Means for discovering patterns and customer profiles by analyzing wholesale product data. Visualize, interpret, and evaluate the quality of the analysis done using Unsupervised Learning. Compare and evaluate the results of different data analyses to determine the quality of clusters, time, and memory usage. Compare T-SNE and UMAP with PCA and ICA, in the context of how different algorithms work and when to apply them. Evaluate the results of the analysis applied to various datasets using Unsupervised Learning. This course is ideal for individuals who are Analysts and Data Scientists who want to understand key applications of Unsupervised Learning from both a conceptual and practical point of view. It is particularly useful for Analysts and Data Scientists who want to understand key applications of Unsupervised Learning from both a conceptual and practical point of view.

Enroll now: Unsupervised Learning with Python: Step-by-Step Tutorial!

Summary

Title: Unsupervised Learning with Python: Step-by-Step Tutorial!

Price: $19.99

Average Rating: 2.85

Number of Lectures: 44

Number of Quizzes: 2

Number of Published Lectures: 44

Number of Published Quizzes: 2

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Explore various Python libraries, including numpy, pandas, scikit-learn, matplotlib, seaborn and plotly
Gain in-depth knowledge of Principle Component Analysis and use it to effectively manage noisy datasets.
Discover the power of PCA and K-Means for discovering patterns and customer profiles by analyzing wholesale product data.
Visualize, interpret, and evaluate the quality of the analysis done using Unsupervised Learning.
Compare and evaluate the results of different data analyses to determine the quality of clusters, time, and memory usage.
Compare T-SNE and UMAP with PCA and ICA, in the context of how different algorithms work and when to apply them.
Evaluate the results of the analysis applied to various datasets using Unsupervised Learning.

Who Should Attend

Analysts and Data Scientists who want to understand key applications of Unsupervised Learning from both a conceptual and practical point of view.

Target Audiences

Analysts and Data Scientists who want to understand key applications of Unsupervised Learning from both a conceptual and practical point of view.

Unlike supervised machine learning, unsupervised machine learning methods cannot be applied to a regression or a classification problem as you have no idea what the values for the output data might be, making it impossible for you to train the algorithm the way you normally would. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning is used for discovering the underlying structure of the data and encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. This course explains the most important Unsupervised Learning algorithms using real-world examples of business applications in Python code.�

This comprehensive 2-in-1 course is a friendly guide that takes you through the basics of Unsupervised Learning. It is packed with step-by-step instructions and working examples! Initially, you’ll select and apply key Unsupervised Learning methods to discover hidden structure in data, in particular: Conduct, interpret and visualize market basket analysis on transaction data. Implement, evaluate and visualize the results of cluster algorithms. Finally, solve any problem you might come across in Data Science or Machine Learning using Unsupervised Learning!

By the end of the course, you’ll apply clustering and dimensionality reduction in Machine Learning using Python as well as Master Unsupervised Learning to solve real-world problems!

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Hands-On Unsupervised Learning with Python, covers usage of Python to apply market basket analysis, PCA and dimensionality reduction, as well as cluster algorithms. This course explains the most important Unsupervised Learning algorithms using real-world examples of business applications in Python code. This course will allow you to utilize the Principal Component Analysis, and to visualize and interpret the results of your datasets such as the ones in the above description. You will also be able to apply hard and soft clustering methods (k-Means and Gaussian Mixture Models) to assign segment labels to customers categorized in your sample data sets. After watching this course, you will know how to apply the basic principles of Unsupervised Learning using Python.

The second course, Mastering Unsupervised Learning with Python, covers mastering advanced clustering, topic modeling, manifold learning, and autoencoders using Python. In this video course you will understand the assumptions, advantages, and disadvantages of various popular clustering algorithms, and then learn how to apply them to different datasets for analysis. You will apply the Latent Dirichlet Allocation algorithm to model topics, which you can use as an input for a recommendation engine just like the New York Times did. You will be using cutting-edge, nonlinear dimensionality techniques (also called manifold learning)—such as T-SNE and UMAP—and autoencoders (unsupervised deep learning) to assess and visualize the information contained in a higher dimension. You will be looking at K-Means, density-based clustering, and Gaussian mixture models. You will see hierarchical clustering through bottom-up and top-down strategies. You will go from preprocessing text to recommending interesting articles. Through this course, you will learn and apply concepts needed to ensure your mastery of unsupervised algorithms in Python. By the end of this course, you will have mastered the application of Unsupervised Learning techniques and will be able to utilize them in your Data Science workflow—for instance, to extract more informative features for Supervised Learning problems. You will be able not only to interpret results but also to enhance them.

By the end of the course, you’ll apply clustering and dimensionality reduction in Deep Learning using Python as well as Master Unsupervised Learning to solve real-world problems!

About the Authors

Stefan Jansenis a data scientist with over 15 years of industry experience in fintech, investment, as well as an advisor to international organizations, Fortune 500 companies, and startups focusing on data strategy, predictive analytics, and machine & deep learning. As a partner in an international investment firm, he used supervised and unsupervised learning to develop investment strategies, manage risks, and evaluate performance. He has also applied a broad range of machine learning techniques to forecast demand, price products, and segment and target customers. He has also used natural language and deep learning for image recognition. He holds master degrees in quantitative economics and finance from Harvard University and Free University Berlin and is a CFA charter holder. He has been teaching Data Science at General Assembly (recently acquired for $420m by Adecco) for over two years, is a DataCamp instructor for Finance & Python with over 15,000 students, and is the author of ‘Hands-on Unsupervised Learning’ and ‘Mastering Unsupervised Learning’ by Packt.

Course Curriculum

Chapter 1: Hands-On Unsupervised Learning with Python

Lecture 1: The Course Overview

Lecture 2: Benefits of Unsupervised Learning

Lecture 3: How Market Basket Analysis Works

Lecture 4: How Market Basket Analysis Works (Continued)

Lecture 5: The Apriori Algorithm – Preparing the Data

Lecture 6: Understanding and Implementing the Apriori Algorithm

Lecture 7: Finding Association Rules

Lecture 8: Visualizing and Interpreting Association Rules

Lecture 9: Unsupervised Learning and the Curse of Dimensionality

Lecture 10: Approaches to Dimensionality Reduction

Lecture 11: The Key Ideas Behind PCA

Lecture 12: The Key Ideas Behind PCA (Continued)

Lecture 13: The Linear Algebra Behind PCA

Lecture 14: The Linear Algebra Behind PCA (Continued)

Lecture 15: PCA in Practice

Lecture 16: PCA in Practice (Continued)

Lecture 17: Clustering – Key Concepts

Lecture 18: Clustering Algorithm in Practice

Lecture 19: Evaluate Clustering Results

Lecture 20: Case Study – K-Means and Wholesale Data

Lecture 21: Case Study – K-Means and Wholesale Data (Continued)

Chapter 2: Mastering Unsupervised Learning with Python

Lecture 1: The Course Overview

Lecture 2: Alternatives to K-Means Clustering – Part 1

Lecture 3: Alternatives to K-Means Clustering – Part 2

Lecture 4: Agglomerative Clustering: Finding Natural Hierarchies – Part 1

Lecture 5: Agglomerative Clustering: Finding Natural Hierarchies –Part 2

Lecture 6: Density-Based Clustering: DBSCAN and HDBSCAN – Part 1

Lecture 7: Density-Based Clustering: DBSCAN and HDBSCAN – Part 2

Lecture 8: Gaussian Mixture Models

Lecture 9: Topic Modeling: Overview – Part 1

Lecture 10: Topic Modeling: Overview – Part 2

Lecture 11: Topic Modeling: Preparing Your Data – Part 1

Lecture 12: Topic Modeling: Preparing Your Data – Part 2

Lecture 13: Topic Modeling: Running the Models – Part 1

Lecture 14: Topic Modeling: Running the Models – Part 2

Lecture 15: Topic Modeling: Evaluating and Visualizing Results

Lecture 16: Manifold Learning: Introduction – Part 1

Lecture 17: Manifold Learning: Introduction – Part 2

Lecture 18: Manifold Learning in Practice – Part 1

Lecture 19: Manifold Learning in Practice – Part 2

Lecture 20: Visualize High-Dimensional Data: t-SNE and UMAP – Part 1

Lecture 21: Visualize High-Dimensional Data: t-SNE and UMAP – Part 2

Lecture 22: Deep Learning and Visualization: Autoencoders and t-SNE – Part 1

Lecture 23: Deep Learning and Visualization: Autoencoders and t-SNE – Part 2

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 4 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 4 votes
5 stars: 1 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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