Course curriculum
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1
DS- 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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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 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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7
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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8
Chapter 8 . Mathematics
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8.1 Data
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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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9
Chapter 8 . Mathematics
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8.1 Data
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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. Intro to Probability & DV
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9.1 Introduction to Probability, Statistics & SQL
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9.2 Data Visualization with Tableau
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9.3 LSTM
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11
Chapter 10. Machine Learning Models
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10.1 Clustering
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10.2 Evaluation Metrics
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10.3 Logistic Regression
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10.4 Simple Linear regression
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10.5 Multiple Linear regression
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12
IBM - Phase -II Confirmation
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Registration Instruction For IBM
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Steps to register- IBM Education
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13
Live Session meeting Links
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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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Internship Orientation Meeting 1- 2022
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DS -Internship Project- ChatBot- Oct 13, 2022 07:00 PM
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14
Live Sessions 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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Orientation Meeting
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Internship Project Live Session
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15
ChatBot - Internship Project
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ChatBot with DialogFlow - Theory
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ChatBot with DialogFlow - Code
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ChatBot - Code
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Test.qa
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Train.qa
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16
INTERNSHIP PROJECT SUBMISSION INSTRUCTIONS
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Internship Project Submissions
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