Are you looking at improving and extending the capabilities of your machine learning systems? Or looking for a career in the field of machine learning? If yes, then this course is for you.

ML is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields, such as search engines, robotics, self-driving cars, and more. It is transforming the way businesses operate. Being able to understand the trends and patterns in complex data is critical to success. In a challenging marketplace, it is one of the key strategies for unlocking growth. 

The aim of the course is to teach you how to process various types of data, including how and when to apply different machine learning techniques. 

We cover a wide range of powerful machine learning algorithms, alongside expert guidance and tips on everything from sentiment analysis to neural networks. You’ll soon be able to answer some of the most important questions that you and your organization face.

Why should I choose this course?

This course is a blend of text, videos, code examples, quizzes, and coding challenges which together makes your learning journey all the more exciting and truly rewarding. It includes sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. This helps you learn a range of topics at your own speed and also move towards your goal of learning machine learning.

Testimonials:

The source content have been received well by the audience. Here are a couple of reviews:

"The author has communicated with clarity for the individual who would like to learn the practical aspects of implementing learning algorithms of today and for the future. Excellent work, up-to-date and very relevant for the applications of the day!"

- Anonymous Customer.

"Very helpful and objective."

- Fabiano Souza

"I would definitely recommend this to people who want to get started with machine learning in Python."

- Spoorthi V.

What is included?

Let’s dig into what this course covers. Since you already know the basics of Python, you are no stranger to the fact that it is an immensely powerful language. With the basics in place, this course takes a hands-on approach and demonstrates how you can perform various machine learning tasks on real-world data. 

The course starts by talking about various realms in machine learning followed by practical examples. It then moves on to discuss the more complex algorithms, such as Support Vector Machines, Extremely Random Forests, Hidden Markov Models, Sentiment Analysis, and Conditional Random Fields. You will learn how to make informed decisions about the types of algorithm that you need to use and how to implement these algorithms to get the best possible results.

After you are comfortable with machine learning, this course teaches you how to build real-world machine learning applications step by step. Further, we’ll explore deep learning with TensorFlow, which is currently the hottest topic in data science. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change the way you look at data. You will also learn how to train your machine to build new models that help make sense of deeper layers within your data.

By the end of this course, you should be able to solve real-world data analysis challenges using innovative and cutting-edge machine learning techniques. 

We have combined the best of the following Packt products:

• Python Machine Learning Cookbook and Python Machine Learning Solutions by Prateek Joshi
• Python Machine Learning Blueprints and Python Machine Learning Projects by Alexander T. Combs
• Deep Learning with TensorFlow by Dan Van Boxel
• Getting Started with TensorFlow by Giancarlo Zaccone
• Python Machine Learning by Sebastian Raschka
• Building Machine Learning Systems with Python - Second Edition by Luis Pedro Coelho and Willi Richert

Meet your expert instructors:

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 startup building an analytics platform for smart water management powered by deep learning. He has been an invited speaker at technology and entrepreneurship conferences including TEDx, AT&T Foundry, Silicon Valley Deep Learning, and Open Silicon Valley. 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.

Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is most well-known for "Dan Does Data", a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. 

Giancarlo Zaccone, a physicist, has been involved in scientific computing projects among firms and research institutions. He currently works in an IT company that designs software systems with high technological content. He currently works in an IT company that designs software systems with high technological content.

Sebastian Raschka has been ranked as the number one most influential data scientist on GitHub by Analytics Vidhya. He has many years of experience with coding in Python and conducted several seminars on the practical applications of data science and machine learning. He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle.

Luis Pedro Coelho is a computational biologist. He analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics—the application of machine learning techniques for the analysis of images of biological specimens. He has a PhD from Carnegie Mellon University, one of the leading universities in the world in the area of machine learning. He is the author of several scientific publications.

Willi Richert has a PhD in machine learning/robotics, where he used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Currently, he works for Microsoft in the Core Relevance Team of Bing, where he is involved in a variety of ML areas such as active learning, statistical machine translation, and growing decision trees.