Course curriculum

  • 1

    E- Learning Content

    • 1.1-Python Crash course Introduction

    • 1.2 Python Demo n install

    • 1.3 Python Intro and Installation

    • 1.4 Basic python and datatype

    • 1.5 Basic,Number,string

    • 1.6 Data types
  • 2

    Chapter 2 - Control flow

    • 2.1 If else conditions

    • 2.2 While & for loop conditions

  • 3

    Chapter 3 - Exception Handling

    • 3.1 Exception Handling
  • 4

    Chapter 4 -Functions

    • 4.1 Functions
  • 5

    Chapter 5 - OOPS

    • 5.1 CLASSES

    • 5.2 OOP
  • 6

    Chapter -6 Deep Learning

    • 6.1 Logistic Regression vs DL

    • 6.2 TesorFlow and Keras
  • 7

    Chapter -7 Libraries

    • 7.1 Introduction to Libraries

    • 7.2 Library Introduction

    • 7.3 Matplolib

    • 7.4 Numpy

    • 7.5 Pandas
  • 8

    Chapter 8 - Mathematics

    • 8.1 Data

    • 8.2 Linear Algebra

    • 8.2 Linear Algebra

    • 8.3 Statistics

    • 8.4 Stats - Probs
  • 9

    Chapter 9 - Machine Learning Models

    • 9.1 Clustering PPT

    • 9.2 Clustering

    • 9.3 Evaluation Metrics

    • 9.4 Logistic Regression - Feature Regression

    • 9.5 Logistic Regression

    • 9.6 Simple Linear regression

    • 9.7 Multiple Linear regression
  • 10

    Live Sessions meeting Links

    • Session- 1

    • Session- 2

    • Session-3

    • Session-4

    • Session-5
  • 11

    Live Sessions Recordings

    • Session-1

    • Session-2

    • Session- 3

    • Session-4

    • Session- 5
  • 12

    Self Driving Car - Internship Project

    • Self Driving Car - 1

    • Self Driving Car - 2

    • Self Driving Car - Internship Project- Live Sessions Recordings