Why this course?

Description

Do you know that Classification is the most commonly applied machine learning method and is used to develop machine learning models that can classify large amounts of data to predict the required outcome? This is also one of the most popular problems to be asked in the data analytics and data scientist job interviews.

In this course, we will try to predict the trade calls in the stock market. There are many machine learning techniques available to do classification. We will cover the classification using Support Vector Machine and Random Forest.

Who should opt for this course?

• Anyone who does not want to go deep into mathematics of the machine learning algorithm but wants to understand basic concepts and real-world application of SVM and Random Forest technique
• Anyone who wishes to develop a practical understanding of Support Vector Machine and Random Forest
• Any student or professional preparing for a job interview and wish to understand and model a classification problem using machine learning techniques
• Any professional willing to implement SVM or Random Forest at her work
• Any student willing to work on academic projects using SVM or Random Forest

Comprehensive Course Coverage

This course covers the implementation of Support Vector Machine and Random Forest for a classification problem – trade call prediction in the stock market, in great depth. It covers the following aspects:

• Basics of the stock market and trading
• Introduction to classification techniques and real-world application of classifications
• Basics of Support Vector Machine (SVM) and Random Forest
• In-depth understanding of the fundamentals of SVM and Random Forest
• Terminologies associated with SVM and Random Forest
• Building the SVM and Random Forest model in Python

In case of any query, please reach out to us at info@aimonks.com