Level 2 - Machine Learning With Python - Innozant MIS & Data Analytics & Data Science

Description

Intro to Data Science & Machine Learning.

This course will introduce you to the world of data science and cover all the major aspects of deriving insights from data sets. In this course, we will show you how to acquire data, clean it for easier analysis, explore and derive insights, convert it into specific features, and model it using machine learning algorithms.

Machine Learning is a step into the direction of artificial intelligence (AI). Machine Learning is a program that analyses data and learns to predict the outcome. This course will be covered in Python as it is one of the primary tools used by data scientists today.

What You will Learn:

Python Basics for Data Science
Analyzing Data with Python
Visualizing Data with Python
Machine Learning with Python
Demonstrate your skills for working with Python and Data
Various types of Machine Learning algorithms
Build and validate a predictive machine learning model using Python
Create and share actionable insights to real life data problems

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Topics for this course

106 Lessons

Overview of Data Science

Machine Learning Overview

Module 1: Python Basics

Python: Introduction and Installation.00:00:00

Working with python00:00:00

Basic syntax and first program00:00:00

Variable : Naming convention , Scope00:00:00

Data Types & Type Casting00:00:00

Strings00:00:00

Conditional Statements: If, If-else, Elif, Nested if-else00:00:00

Loops and conditional00:00:00

List00:00:00

Tuples00:00:00

Dictionaries00:00:00

Set , set operators00:00:00

Arithmetic operators : Logical, comparison00:00:00

Operations (append, extend, sort, pop, insert, remove, count, max, min, count, length,index)00:00:00

Module 2. Introduction to Machine Learning

Module 3: Scikit Learn

Module 4: Machine Learning – Optimization Techniques

Optimization00:00:00

Maxima and Minima00:00:00

Steps to find Maxima and minima00:00:00

Saddle point00:00:00

Cost function (Loss Function)00:00:00

Optimization Strategies00:00:00

Learning Rate and Its Importance00:00:00

Optimization Strategies – Gradient Descent00:00:00

Step to Calculate Gradient Descents00:00:00

Identifying Gradient Descents performance00:00:00

Gradient Descents for Machine Learning00:00:00

Procedure for Batch Gradient Descent00:00:00

Procedure for Stochastic Gradient Descent00:00:00

Difference Between Batch and Stochastic Gradient Descent00:00:00

Disadvantage of Gradient and Descent00:00:00

Momentum00:00:00

Nesterov Accelerated Gradient00:00:00

Adaptive Gradient Procedure – Adagrad00:00:00

Advantage and Disadvantage of Adagrad00:00:00

Root Mean Squared Propagation – RMSprop00:00:00

Adaptive Moment Estimation Procedure – ADAM00:00:00

Module 5: MACHINE LEARNING ALGORITHM

Machine Learning Algorithm- Supervised Learning

Machine Learning – Unsupervised Learning

Ensemble Techniques

Neural Networks – Basics and Example

Projects

₹18,999.00

Course level: Intermediate