Supervised Machine Learning Principles and Practices-Python
What you'll learn
- Understand the mathematics behine Machine Learning
- Supervised Machine Learning Models such as Decision Tree, Support Vector Machine, k-Nearest Neighbor, Linear Regression etc.
- Python Code for Supervised learning models
- Creating a ML model and solving for a given set of data.
Requirements
- Basic Mathematics, Programming foundations
Description
In this course, we present the concept of machine learning and the classification of different methods of learning such as Supervised and Unsupervised Learning. We also present reinforcement learning. We offer popular techniques and implement them in Python. We begin with the Decision Tree method. We present this simply with all the required mathematical tools such as entropy. We implement them in Python and explain how the accuracy can be improved. We offer the classification problem with a suitable real-life scenario. Linear Regression is taught using simple real-life examples. We present the L2 Error estimation and explain how we can minimize the error using gradient optimization. This is implemented using the Python library. We also offer the Logistic Regression method with an example and implement in Python. The Nearest Neighbourhood approach is explained with examples and implemented in Python. Support Vector Machines (SVM) are a popular supervised learning model that you can use for classification or regression. This approach works well with high-dimensional spaces (many features in the feature vector) and can be used with small data sets effectively. When trained on a data set, the algorithm can easily classify new observations efficiently. We also present a few more methods. The Bayesian model of classification is used for large finite datasets. It is a method of assigning class labels using a direct acyclic graph. The graph comprises one parent node and multiple children nodes. And each child node is assumed to be independent and separate from the parent. As the model for supervised learning in ML helps construct the classifiers in a simple and straightforward way, it works great with very small data sets. This model draws on common data assumptions, such as each attribute is independent. Yet having such simplification, this algorithm can easily be implemented on complex problems.
Who this course is for:
- Bachelor and Master Degree students
- Machine Learning Programmers
Instructor
Dr. Xavier Chelladurai is a Professor, Computer Science and Engineering, Christ University. He is one of the pioneers of Computer Science in India, serving the academia and industry for the past 37+ years. Author of 23 Computer Science books, most of them prescribed in the syllabus of various Indian and foreign universities, more than 10 research papers, 18 educational videos published in YouTube and several blogs on technical topics.
He is a Java practitioner for 25 years and his Java book published by McGraw Hill India Education celebrated its Jubilee recently. Artificial Intelligence and Machine Learning practitioner with 19 years of industry experience (HCL 14 years, Tech M – 2 years and Capgemini 3 years) and 17 years of education and research experience. He was member of IT Task force for Govt of Tamilnadu for three years in 1998- 2001.