About this course:

To run predictive models, machine learning uses systems that gain from existing information so as to estimate future outcomes, behaviors, and trends.

With the help of this course of data science, you will be provided complete details of the theory of machine learning joined hands-on experience and practical scenarios validating, building, and deploying the models of machine learning. You will figure out how to create and get experiences from these models utilizing R, and Azure Notebooks.

Course Objective:

• Data preparation, exploration, and cleaning
• Supervised the techniques of machine learning
• Data Cleaning and Preparation.
• Unsupervised the techniques of machine learning
• Improvement of model performance
• Principles of Model Improvement
• Techniques for Improving Models
• Overview of Machine Learning
• High-Level Data Science Process
• Exploratory Data Analysis for Classification
• Exploratory Data Analysis for Regression
• Dimensionality Reduction

Audience:

• Programmers
• Data Analyst

Prerequisite:

• Some experience of programming – R is preferred.
• An elementary understanding of math
• A preparedness to learn through self-paced study.