Master Machine Learning

Understand the core concepts of machine learning (supervised, unsupervised, reinforcement). Apply popular machine learning algorithms (e.g., linear regression, decision trees). Evaluate and improve the performance of machine learning models. Prepare and clean data for machine learning projects. Implement machine learning solutions using a programming language (e.g., Python).

Instructors:

SJ Sushmitha J

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15 Lessons

Explore the fascinating field of machine learning in this comprehensive course. Gain a solid foundation in fundamental concepts and algorithms used in machine learning, including supervised and unsupervised learning, neural networks, and deep learning. Learn to apply these techniques to analyze and extract insights from data, and discover how to evaluate model performance and make informed decisions. This self-paced course provides the knowledge and tools to excel in this rapidly growing field.

Course Content

Module 1,Master Machine Learning

Intro

Note

Data_Colab Intro

Features

Classification-Regression

Training Model

Module 2,Master Machine Learning

Preparing Data

K-Nearest Neighbors and Naive Baye

Logistic Regression

Support Vector Machine

Module 3,Master Machine Learning

Neural Networks

Tensorflow

Linear Regression

K-Means Clustering

Principal Component Analysis

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