MACHINE LEARNING

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.
THE CURRICULUM
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms
ELIGIBILITY CRITERIA
B.Tech (Mechanical), ITI/Diploma
SYLLABUS
Python For Machine Learning

Introduction To Statistics	Machine Learning Applications & Landscap
Linear Algebra Recap	Building End-to-End Machine Learning Project
Supervised Learning: Training Models	Ensemble Learning and Random Forests
Support Vector Machines	Unsupervised Learning
Decision Trees	Neural Network
Extended Application	Supervised Learning: Classifications