AI, Basic Statistics, Basic Python, Basic R, ML (Overview)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Overview of this course

Lecture 2: Knowledge Sharing Flow

Chapter 2: Artificial intelligence

Lecture 1: Definition of Artificial intelligence

Lecture 2: History of AI

Lecture 3: The Original 7 Aspects of AI (1955)

Lecture 4: What is Intelligence

Lecture 5: Types of AI

Lecture 6: Maturity Level and Why AI

Lecture 7: How to Teach Machines

Lecture 8: Applications of AI

Lecture 9: AI in Telecom

Lecture 10: AI in Finance

Lecture 11: AI in Image Analytics

Lecture 12: AI in Medical Science

Lecture 13: AI in IT operations Management

Lecture 14: AI in News

Lecture 15: Open Source Technologies

Lecture 16: What is Cognitive Computing

Lecture 17: Analytics Methodology CRISP DM

Lecture 18: Next Generation AI

Lecture 19: Next Generation’s AI Applications ( 5 years)

Lecture 20: IOT Analytics

Lecture 21: How Artificial Intelligence and Natural Intelligence can co-exist

Chapter 3: Basic Statistics

Lecture 1: Introduction

Lecture 2: What is Statistics

Lecture 3: Population and Sample

Lecture 4: Measures of Central Tendency (Mean, Median, Mode)

Lecture 5: Measures of Dispersion

Lecture 6: How to add Data Analysis tab

Lecture 7: Descriptive and Inferential Statistics

Lecture 8: Type of Data and Variables

Lecture 9: Normal Distribution

Lecture 10: Binomial and Poisson distribution

Lecture 11: Hypothesis testing

Lecture 12: How to Find Value of z for one tail at 95% confidence

Lecture 13: Confusion matrix & Types of Error Alpha and Beta

Lecture 14: Example – Standard Normal Distribution

Lecture 15: Student's t Distribution

Lecture 16: Chi-square Distributions

Lecture 17: Various Distributions

Lecture 18: Sampling Procedure

Lecture 19: Central Limit Theorem – CLT

Lecture 20: How to determine Sample size

Lecture 21: ANOVA – Analysis of variance

Lecture 22: Various Distributions – Choices

Lecture 23: Graphs types

Lecture 24: Chart Types and Scatter plots

Lecture 25: Correlation

Lecture 26: Box Plots

Lecture 27: Grouped Data – Pareto Charts – Multi variance Charts

Chapter 4: Basic Python

Lecture 1: Introduction

Lecture 2: Strings

Lecture 3: Tuples

Lecture 4: Operators

Lecture 5: Regular Expression

Lecture 6: Date Time

Lecture 7: Panda data frame

Lecture 8: Panda statistics

Lecture 9: Various Join and Tables

Lecture 10: Various graphs

Lecture 11: Numpy – Introduction

Lecture 12: Numpy – Slicing

Lecture 13: Various Join and Stacking

Lecture 14: Handle Missing Data – part 1

Lecture 15: Handle Missing Data – part 2

Lecture 16: Encode Categorical features

Lecture 17: Statistical and Probability Functions

Lecture 18: Sampling, Logging and Exception handling

Chapter 5: Basic R

Lecture 1: Basic R – Introduction and History

Lecture 2: Vectors – List – Matrix

Lecture 3: Data Frame

Lecture 4: Apply functions

Lecture 5: Handle Missing Data

Lecture 6: Encode Categorical features and Various Table

Lecture 7: Graphs

Lecture 8: Cbind and Rbind

Lecture 9: Data sorting and Date Time

Lecture 10: Reading -Saving data objects & Mathematical Functions

Lecture 11: Statistical & Probability Functions

Lecture 12: Measures of Central Tendency

Lecture 13: Logging the various types of messages

Chapter 6: Machine Learning (Overview)

Lecture 1: What is Machine Learning

Lecture 2: Executive Summary – Types of Machine Learning

Lecture 3: Unsupervised Machine Learning

Lecture 4: Association (Also known as Market Basket Analysis)

Lecture 5: Supervised Machine Learning

Lecture 6: Random Forest

Lecture 7: XGBoost

Lecture 8: Summary of supervised and unsupervised algorithms

Lecture 9: Reinforced learning

Lecture 10: Applications_Regression_Classification_Clustering

Lecture 11: How much Data is Enough

Lecture 12: What next