This document provides an overview of topics related to artificial intelligence, machine learning, and data analytics. It introduces brief histories of machine learning and AI, describes common machine learning techniques like supervised and unsupervised learning, and discusses data types and dealing with missing/outlier values. The document also outlines applications of AI in analytics, text analytics methods like tokenization and sentiment analysis, time series analysis techniques, clustering algorithms like K-means, regression, classification algorithms, and statistical concepts like measures of central tendency, hypothesis testing, and representing data through graphs and charts. The content is intended to provide a non-technical introduction to these key AI and data science concepts.

1. Artificial intelligence and Machine Learning
Introduction
For Non Technical Audience

2. Training Content
• Brief history Machine Learning
• What is Machine Learning
• What is
• Statistics
• Data Science
• Data Engineering
• Data Analysis
• Real world scenarios to understand the
perspective
• Types of Machine Learning
• Unsupervised Learning
• Supervised Learning
• Reinforced learning
• What are open source technologies
• Examine data types – quantitative, qualitative,
continuous, discrete, ordinal, nominal etc.
• What is Missing and Outliers values 2
• Definition of Artificial intelligence
• PAST of AI
• History
• The Original 7 Aspects of AI (1955)
• What is Intelligence
• Types of AI
• PRESENT of AI
• How to Teach Machines?
• Maturity Level
• Applications of AI in terms of Analytics
(six)
• Miscellaneous related to AI in terms of
Analytics
• Future of AI
• Next Generation AI
• Next Generation Applications (< 5 years)
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3. Training Content cont
Text Analytics
•Text analysis steps
•Tokenization
•Part of speech tagging
•Lemmatization
•Named entity recognition
•Sentiment analysis
Time Series Analysis
•What is Time Series Analysis
•Periodic models: monthly,
weekly, and daily averages
•Anomaly detection
•Predictions
•Trend Analysis
Clustering/
Grouping/Segmentation
•Principle Component Analysis
•What is clustering and use cases
•Proximity Matrices – find
dissimilarity between two
observation
•Choice of attributes
•Units of measure of attributes
•Determining number of clusters
•K-means clustering
•Practical Issues with K-means
clustering
•Measure performance of clustering
•Hierarchical clustering
•Practical Issues in clustering
3
Regression and Classifications
•What is Regression
•What can be predicted
•How to make sure given result is
good enough
•What are classification problems
and types of classification
algorithms
•Decision Tree Classification
•Performance metrics –
confusion matrix
•Receiver Operating
Characteristic AUC (ROC AUC)
and Precision Recall AUC (PR
AUC)
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4. Training Content cont
• Introduction to statistics
• What is Statistics
• Population and Sample
• Descriptive and Inferential
Statistics
• Parameters and Statistics
• Measures of Central Tendency:
Mean, median, mode
• Measures of Dispersion: Range,
quartile deviation, mean deviation ,
standard deviation
• Measure of Shape: Skewness,
Kurtosis
• Sampling Procedure: Probability &
Non-Probability
• Normal Distribution
• The Histograms
• Hypothesis Testing
• The Testing Process
• Sample Averages
• Confidence Intervals
• Hypothesis Tests
• The Null Hypothesis
• The p-Value
• Interpreting the Test
Results
• One & Two sided Tests
• Type I & II Errors and
Power
• Deciding on the Sample
Size 4
• Representing Data -
Graphical /Tabular
• XY Graphs
• Scatter Graphs
• Correlation
• Box Plots
• Box Plots for
Comparison
• Grouped Data
• Cumulative Frequency
• Percentiles
• Pareto Charts
• Stem and Leaf Plots
• Multi variance Charts
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