Practical Supervised and Unsupervised Learning with Python

Are you looking forward to developing rich Python coding practices with Supervised and Unsupervised Learning? Then this is the perfect course for you!

Supervised Machine Learning is used in a wide range of industries across sectors such as finance, online advertising, and analytics, and it's here to stay. Supervised learning allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more. Unsupervised Learning is used to find a hidden structure in unlabeled and unstructured data. On the other hand, supervised learning is used for analyzing structured data making use of statistical techniques. Python makes this easier with its libraries that can be used for Machine Learning. This Course covers modern tools and algorithms to discover and extract hidden yet valuable structure in your data through real-world examples. This course explains the most important Unsupervised Learning algorithms using real-world examples of business applications in Python code.

This comprehensive 3-in-1 course follows a step-by-step approach to entering the world of Artificial Intelligence and developing Python coding practices while exploring Supervised Machine Learning. Initially, you’ll learn the goals of Unsupervised Learning and also build a Recommendation Engine. Moving further, you’ll work with model families like recommender systems, which are immediately applicable in domains such as e-commerce and marketing. Finally, you’ll understand the concept of clustering and how to use it to automatically segment data.

By the end of the course, you’ll develop rich Python coding practices with Supervised and Unsupervised Learning through real-world examples.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Hands-On Unsupervised Learning with Python, covers clustering and dimensionality reduction in Deep Learning using Python. This course will allow you to utilize 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.

The second course, Hands-on Supervised Machine Learning with Python, covers developing rich Python coding practices while exploring supervised machine learning. This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning. Next, we’ll explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you’ll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of the video course, you’ll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems.

The third course, Supervised and Unsupervised Learning with Python, covers an introduction to the world of Artificial Intelligence. Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers.

By the end of the course, you’ll develop rich Python coding practices with Supervised and Unsupervised Learning through real-world examples.

About the Authors

• Stefan Jansen is a data scientist with over 10 years of industry experience in fintech, investment, and as an advisor to Fortune 500 companies and startups, focusing on data strategy, predictive analytics, and machine and deep learning. He has used Unsupervised Learning extensively to segment large customer bases, detects anomalies, apply topic modeling to large volumes of legal documents to automate due diligence, and to facilitate image recognition. He holds master degrees from Harvard University and Free University Berlin, a CFA charter, and has been teaching data science and statistics for several years.

• Taylor Smith is a machine learning enthusiast with over five years of experience who loves to apply interesting computational solutions to challenging business problems. Currently working as Principal Data Scientist, Taylor is also an active open source contributor and staunch Pythonista.

• Prateek Joshi is an artificial intelligence researcher, published author of five books, and TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley start-up that builds analytics platforms for smart water management powered by deep learning. His work in this field has led to patents, tech demos, and research papers at major IEEE conferences. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open-Silicon Valley. Prateek has also been featured as a guest author in prominent tech magazines. His tech blog has received more than 1.2-million page views from 200 over countries and has over 6,600+ followers. He frequently writes on topics such as artificial intelligence, Python programming, and abstract mathematics. He is an avid coder and has won many hackathons utilizing a wide variety of technologies. He graduated from the University of Southern California with a master’s degree specializing in artificial intelligence. He has worked at companies such as Nvidia and Microsoft Research.