Machine Learning with Python

Name: Machine Learning with Python
Rating: 4.0 (3 reviews)

Machine learning

(3 ratings)

1,218 students

Created by Ram Reddy

Last updated 3/2020

English

What you'll learn

Make predictions using linear regression, polynomial regression, and multivariate regression
Master Machine Learning on Python
Make robust Machine Learning models
Have a great intuition of many Machine Learning models

Requirements

Python programming Language

Description

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

What is Machine learning

Features of Machine Learning

Difference between regular program and machine learning program

Applications of Machine Learning

Types of Machine Learning

What is Supervised Learning

What is Reinforcement Learning

What is Neighbours algorithm

K Nearest Neighbours classification

K Nearest Neighbours Regression

Detailed Supervised Learning

Supervised Learning Algorithms

Linear Regression

Use Case(with Demo)

Model Fitting

Need for Logistic Regression

What is Logistic Regression?

Ridge and lasso regression

Support vector Machines

Pre process of Machine learning data

ML Pipeline

What is Unsupervised Learning

What is Clustering

Types of Clustering

Tree Based Modeles

What is Decision Tree

What is Random Forest

What is Adaboost

What is Gradient boosting

stochastic gradient boostinng

What is Naïve Bayes

Who this course is for:

Any students in college who want to start a career in Data Science.
Any data analysts who want to level up in Machine Learning.
Any people who want to create added value to their business by using powerful Machine Learning tools

Ram Reddy

Data Scientist

4.3 Instructor Rating
984 Reviews
48,815 Students
7 Courses

Data scientist and founder of RRITEC, a company dedicated to helping scientists better understand and visualize their data. Ram Has hands-on exposure to a wide variety of datasets has informed him of the many problems scientists face when trying to visualize their data.

Some of the main roles are

Develop and improve robust predictive algorithms that are the core of the product

Combine an understanding of business goals with data analysis and machine learning

Investigate new data sources; acquire, analyze, clean and structure data

Utilize state of the art machine learning techniques to improve and expand existing models