Python Machine Learning (Part 2)
In Part 2 of the Python Machine Learning guided lab from Next.Tech, you will build your knowledge in the basic and advanced techniques used to improve the performance of machine learning models. More specifically, we will cover data preprocessing, dimensionality reduction, ensemble learning, and machine learning algorithim optimization.
Unlock modern machine learning techniques with Python by using the latest cutting-edge open source Python libraries.
This is Part 2 of the Python Machine Learning series. In this course, we provide a practical approach to key frameworks in data science and machine learning. Using scikit-learn to implement machine learning, get to know the best practices to improve and optimize your machine learning systems and algorithms.
Objectives
Master machine learning techniques using challenging real-world data, Understand how to preprocess data for particular models,Delve into dimensionality reduction techniques, Learn how to optimize machine learning algorithms by evaluating the models and tuning hyperparameters,and Understand ensemble learning
Prerequisites
Basic Python programming and familiarity with the NumPy library, Understanding of basic linear algebra concepts
Completion of Python Machine Learning Part 1 is highly recommended
Complete this entire course to earn a Python Machine Learning (Part 2) Certificate of Completion
