Python Machine Learning: Tips, Tricks, and Techniques, published by Packt
This is the code repository for Python Machine Learning Tips, Tricks, and Techniques [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
achine learning allows us to interpret data structures and fit that data into models to identify patterns and make predictions. Python makes this easier with its huge set of libraries that can be easily used for machine learning. In this course, you will learn from a top Kaggle master to upgrade your Python skills with the latest advancements in Python. It is essential to keep upgrading your machine learning skills as there are immense advancements taking place every day. In this course, you will get hands-on experience of solving real problems by implementing cutting-edge techniques to significantly boost your Python Machine Learning skills and, as a consequence, achieve optimized results in almost any project you are working on. Each technique we cover is itself enough to improve your results. However; combining them together is where the real magic is. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your machine learning toolbox. By the end of this course, you will know various tips, tricks, and techniques to upgrade your machine learning algorithms to reduce common problems, all the while building efficient machine learning models.
- Tips and tricks to speed up your modeling process and obtain better results
- Make predictions using advanced regression analysis with Python
- Modern techniques for solving supervised learning problems
- Various ways to use ensemble learning with Python to derive optimum results
- Build your own recommendation engine and perform collaborative filtering
- Give your production machine learning system improved reliability
To fully benefit from the coverage included in this course, you will need:
This course is for aspiring data science professionals and Machine Learning practitioners who are familiar with basic Python programming and machine learning libraries.
This course has the following software requirements:
Python