Course curriculum
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1
E- Learning Content
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1.1-Python Crash course Introduction
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1.2 Python Demo n install
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1.3 Python Intro and Installation
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1.4 Basic python and datatype
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1.5 Basic,Number,string
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1.6 Data types
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2
Chapter 2 - Control flow
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2.1 If else conditions
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2.2 While & for loop conditions
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3
Chapter 3 - Exception Handling
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3.1 Exception Handling
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4
Chapter 4 -Functions
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4.1 Functions
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5
Chapter 5 - OOPS
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5.1 CLASSES
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5.2 OOP
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6
Chapter 5 - OOPS
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5.1 CLASSES
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5.2 OOP
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7
Chapter -6 Deep Learning
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6.1 Logistic Regression vs DL
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6.2 TesorFlow and Keras
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8
Chapter -7 Libraries
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7.1 Introduction to Libraries
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7.2 Library Introduction
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7.3 Matplolib
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7.4 Numpy
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7.5 Pandas
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9
Chapter 8 - Mathematics
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8.1 Data
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8.2 Linear Algebra
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8.2 Linear Algebra
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8.3 Statistics
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8.4 Stats - Probs
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10
Chapter 9 - Machine Learning Models
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9.1 Clustering PPT
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9.2 Clustering
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9.3 Evaluation Metrics
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9.4 Logistic Regression - Feature Regression
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9.5 Logistic Regression
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9.6 Simple Linear regression
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9.7 Multiple Linear regression
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11
Zoom Sessions Links
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1st Live Session- Jul 12, 2022 07:00 PM
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2nd Live Session- Jul 14, 2022 07:00 PM
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3rd Live Session- Jul 19, 2022 07:00 PM
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4th Live Session- Jul 25, 2022 07:00 PM
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5th Live Session- Jul 28, 2022 07:00 PM
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6th Live Session- Aug 3, 2022 07:00 PM
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7th Session- Aug 6, 2022 06:00 PM
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8th Session- Aug 10, 2022 07:00 PM
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Internship orientation
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ML- Internship Project - Self Driving Car
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12
live Session Recordings
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SESSION- 1
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SESSION-2
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SESSION-3
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SESSION-4
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Session-5
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SESSION-6
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SESSION -7
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SESSION-8
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Internship orientation meeting recordings
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SELF DRIVING CAR-INTERNSHIP PROJECT LIVE SESSION
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13
Registration Instruction For IBM
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Registration Instruction For IBM
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Steps to register- IBM Education
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14
Self Driving Car - Internship Project
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Self Driving Car - 1
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Self Driving Car - 2
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15
Internship project Submisions
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Internship Project Submissions details
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