Python Machine Learning with Real Use Cases

This is a high-level list of topics covered in this course. Please see the detailed Agenda below • Use predictive modeling and apply it to real-world problems • Explore data visualization techniques to interact with your data • Learn how to build a recommendation engine • Understand how to interact with text data and build models to analyze it • Work with speech data and recognize spoken words using Hidden Markov Models • Get well versed with reinforcement learning, automated ML, and transfer learning • Work with image data and build systems for image recognition and biometric face recognition • Use deep neural networks to build an optical character recognition system

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Hours :

Hands on Labs

Requirements

  • This course is designed for developers wants to Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Pre-Requisites: Students should have familiar with • Basics of Python • Knowledge of Python is assumed.

Description

This eagerly anticipated edition of the popular Python Machine Learning with Real Use Cases will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The course will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the course will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding lessons, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this course, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. Working in a hands-on learning environment, led by our Python Machine Learning Cookbook expert instructor, students will learn about and explore: • Learn and implement machine learning algorithms in a variety of real-life scenarios • Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques • Find easy-to-follow code solutions for tackling common and not-so-common challenges

Course Content

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About the Instructor

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About the Instructor

Ernesto Lee is an impassioned blockchain entrepreneur and technologist from the Miami/Fort Lauderdale Area. For over 25 years Ernesto has been asking hard questions and pursuing tough answers. Ernesto was an original founding member, co-owner and the CTO of Blockchain Training Alliance and former Chief Solutions Architect at TechBlue.com. Presently, Ernesto is the CEO at Ernesto.Net and Engineer at Kaiser Permanente.  Ernesto’s career illustrates a lifelong commitment to pushing the envelope on innovation and growing opportunities for all around him.

As a graduate of Old Dominion University (BS, Physics), Virginia Tech (MS, Software Engineering), and Harvard Extension School (Graduate Certificate, Business Communication), Ernesto has always had a passion for technology and teaching. It has been a cornerstone of Ernesto’s career.