Machine Learning(ML) Training

Created By: Team InventModel

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Requirements

Machine Learning(ML) Training

Course Description
Module A: Introduction

 

A.1 Introduction to python 

A.2 History of python

A.3 Features of python

A.4 Comparison to other languages

A.5 IDLE setup

A.6 Your very first python implementation


Module B: Tokens 

 

B.1 Tokens & its types

B.2 keywords

B.3 Identifiers & it’s constraints

B.4 Literals

B.5 Operators 

B.6 Delimiters

B.7 Comments

 

Module C: Variables

C.1 Python working 

C.2 Variables and data types (integers, floats, strings, Booleans)

C.3 Difference between identifiers and variables

C.4 Basic operations

Exercise


Module D: Data Types in Detail

 

 D.1 Numbers

 D.2 Strings

 D.3 List

            D.4 Tuples

            D.5 Dictionary

            Exercise

Module E: Decision Making


 E.1 Introduction

            E.2 Syntax (Keywords and Identifiers, Statements, Expressions)

            E.3 Decision making (if, else, else if)

            Exercises

 

Module F: Loops

 

F.1 Loops (for, while)

F.2 Control flow statements (break, pass, and continue)

Exercises

 

Module G: Functions

 

G.1 Introduction

G.2 Calling a function

           G.3 Arguments

           G.4 Types of functions (built in, user defined)

           G.5 Scope of variable 

           G.6 Higher order functions (passing function to function)

G.7 Single line function (lambda function)

           Exercises

 

Module H: Packages & Modules

 

            H.1 Modules and Packages 

            H.2 Importing modules

            H.3 user defined modules 

            H.4 dir() function 

            Exercises

Module I: Exceptional Handling 

 

I.1 Errors and its types

I.2 Handling errors

I.3 try, except handling 

I.4 raise & assert (difference)

Exercises

 

Module J: File Handling 

 

J.1 Introduction

J.2 Writing/Reading/appending data to a file

J.3 Additional file methods

J.4 Dictionary (about pickle)

Exercises

 

Module K: OOPS

 

            K.1 Introduction

            K.2 Creating classes, methods, and instance

            K.3 Constructor or Destructor method

            K.4 Inheritance

            K.5 Polymorphism

            K.6 Data Abstraction 

            K.7 Special methods

            Exercises

Course Content

Level Advanced • 17 Lectures • 00 Minutes

Day1_ Basic to Machine Learning- Introduction of Machine Learning

Day2_ Basic Machine Learning Training - Introduction to Regression

Day3_ Basic Machine Learning Training - Classification, KNN, Decision Trees

Day4_ Basic Machine LearningTraining- Clustering, K-Mean, Hierarchical Clustering

Day5_ Basic Machine Learning Training - Linear Algebra

Day6_ Basic Machine Learning Training -Linear Algebra and Introduction to Calculus

Day7_ Basic Machine Learning Training - Diffrentiation

Day8_ Basic Machine Learning Training - MINI PROJECT _Sentiment Analysis_pt. 1

Day9 _ Basic Machine Learning Training - MINI PROJECT _Sentiment Analysis_ pt. 2

Day10_ Basic Machine Learning Training - Partial Diffrentiation

Day11_ Basic Machine Learning Training - Probability and Statistic

Day12_ Basic Machine Learning - Conditional Probability

Day13_ Basic Machine Learning- MINI PROJECT _Customer Spending Analysis_

Day14_ Basic Machine Learning Training - MINI PROJECT - Customer Spending Analysis

Day15_ Basic Machine Learning Training- MINI PROJECT - Customer Segmentation Analysis pt3

Day16_ Basic Machine Learning Training - Statistics pt 1

Day18_ Basic Machine Learning Training - Introduction of Neural Network

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₹1,900.00
This Course Includes:

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