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Step-By-Step Machine Learning With Python

Step-By-Step Machine Learning With Python

Put your Python skills to the test and enter the big world of data science to learn the most effective machine learning tools and techniques with this interesting guide.


Put your Python skills to the test and enter the big world of data science to learn the most effective machine learning tools and techniques with this interesting guide.

Data science and machine learning are some of the top buzzwords in the technical world today. The resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This video is your entry point to machine learning. It starts with an introduction to machine learning and the Python language and shows you how to complete the necessary setup. Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. With the help of the various projects included, you will acquire the mechanics of several important machine learning algorithms, which will no longer seem obscure. Also, you will be guided step-by-step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and master best practices for applying machine learning techniques. Throughout this course, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple Python language. Interesting and easy-to-follow examples—including news topic classification, spam email detection, online ad click-through prediction, and stock prices forecasts—will keep you glued to the screen till you reach your goal.

About the Author

Yuxi (Hayden) Liu is currently a data scientist working on messaging app optimization at a multinational online media corporation in Toronto. He focuses on social graph mining, user demographics, interest prediction, spam detection, and recommendation systems. He has worked for several years as a data scientist in real-time bidding programmatic advertising, where he applied his machine learning expertise in ad optimization, click-through rate and conversion prediction, and click fraud detection. Hayden earned his degree from the University of Toronto, and published five IEEE transactions and conference papers during his master's research. He is also a machine learning education enthusiast, and has authored the Python Machine Learning By Example book.

Course Information

Basic knowledge of Python is assumed

Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
Use Python to visualize data spread across multiple dimensions and extract useful features
Delve into the world of analytics to predict situations correctly
Implement machine learning classification and regression algorithms from scratch in Python
Be amazed to see algorithms in action
Evaluate the performance of a machine learning model and optimize it
Solve interesting, real-world problems using machine learning and Python as the journey unfolds

This course is for you if you want to learn how to train your machine just like a kid
This course is for you if you don’t know anything about machine learning but want to know it well enough so that you can implement projects based on Artificial Intelligence

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Course Specifications

Our great AI and Machine Learning courses include Artificial Intelligence, Coding and Programming. 

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Adult education is the non-credential activity of gaining skills and improved education. 

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Online education is electronically supported learning that relies on the Internet for teacher/student interaction. 

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A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

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Course duration is 24 hours.

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