Machine Learning Algorithms: Basics to Advanced

Are you really keen to learn some cool Machine Learning algorithms along with mastering advanced data analysis using financial examples in Pandas? Then this Course is for you!

To address the complex nature of various real–world data problems, specialized Machine Learning algorithms have been developed that solve these problems perfectly. On the other hand, the Ensemble is a powerful way to upgrade your model as it combines models and doesn’t assume a single model is the most accurate.

This well thought out sequential course takes a practical approach to Mastering Python Data Analysis with Pandas helping you exploring various Machine Learning algorithms to develop your own Ensemble Learning models and methods to use them efficiently. Then, you will learn how to pre–cluster your data to optimize and classify it for large datasets. Along with this, you will also focus on algorithms such as k–Nearest Neighbors, Naive Bayes, Decision Trees, Random Forest, k–Means, and much more. Finally, you will combine various models to achieve higher accuracy than base models can and develop robust models using the bagging technique.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Mastering Python Data Analysis with Pandas, you will learn how to apply Pandas to important but simple financial tasks such as modeling portfolios, calculating optimal portfolios based upon risk, and more. This video not only teaches you why Pandas is a great tool for solving real–world problems in quantitative finance, it also takes you meticulously through every step of the way, with practical, real–world examples, especially from the financial domain where Pandas is a popular choice. By the end of this video, you will be an expert in using the Pandas library for any data analysis problem, especially related to finance.