Learning Path: Python: Machine And Deep Learning With Python

Use the power of Python to explore the future of data science and uncover the hidden layers of data!

Do you want to explore the various arenas of machine learning and deep learning by creating insightful and interesting projects? If yes, then this Learning Path is ideal for you!

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Machine learning and deep learning gives you unimaginably powerful insights into data. Both of these fields are increasingly pervasive in the modern data-driven world.

This Learning Path begins with covering the basic-to-advanced-level concepts of Python. Then, you’ll explore a range of real-life scenarios where machine learning can be used. Throughout the Learning Path, you will use Python to implement a wide range of machine learning algorithms that solve real-world problems. You’ll also learn a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. There are six different independent projects that will help you master machine learning in Python. Finally, you’ll learn to build intelligent systems using deep learning with Python.

By the end of this Learning Path, you should be able to build your own machine learning and deep learning models.

Meet Your Experts:

We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:

Daniel Arbuckle got his Ph.D. In Computer Science from the University of Southern California. He has published numerous papers, along with several books and video courses, and is both a teacher of computer science and a professional programmer.

Prateek Joshiis an artificial intelligence researcher, published author of five books, and TEDx speaker. He is the founder of Pluto AI, a venture-funded Silicon Valley startup building an analytics platform for smart water management powered by deep learning. His tech blog has received more than 1.2 million page views from 200 over countries and has over 6,600+ followers.

Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling.

Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks.

This Learning Path is for Python programmers, developers, and data scientists who are looking to use machine-learning algorithms to create real-world applications

Basic knowledge of Python

Some understanding of statistical concepts would be beneficial

Learn the fundamentals to advanced techniques of Python

Explore and use Python's impressive machine learning ecosystem

Understand which machine learning algorithms to use in a given context

Learn how to evaluate a problem and apply optimal data models to solve it

Deploy machine learning models using third-party APIs

Get to grips with each and every aspect of deep learning