Module 1: Introduction to Artificial Intelligence and Machine Learning

Module 2: Techniques of Machine Learning

Module 3: Mathematics & Statistics Refresher

Module 4: Accessing/Importing and Exporting Data

Module 5: Introduction to NumPy, Pandas

Module 6: Data Cleaning- Manipulation

Basic Functionalities of a data object
Merging of Data objects
Concatenation of data objects
Types of Joins on data objects
Exploring a Dataset
Analysing a dataset
Data Manipulation steps (sorting, filtering, duplicates, merging, appending, derived variables, sampling, Data type, conversions, renaming, formatting etc)
Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Normalizing data
Formatting data

Module 7: Data Analysts-Visualization

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis (Distributions & Relationships, Graphical Analysis}
Creating Graphs-
- Bar plot
- Pie plot
- Count plot
- Line chart
- Histogram
- Boxplot
- Scatter
- Density
- Violine Plot
- Swarm plot
- Distplot
- Pair plot
- Heatmap
Important Packages for Data Visualisations
- Matplotllb
- Seaborn
- Plotly
- Cufflinks

SUPERVISED LEARNING:
Module 8: Linear Regression

Module 9: Logistic Regression

Module 10: Time Series Forecasting

Introduction -Applications
Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques
Basic Techniques- Averages, Smoothening, etc
Advanced Techniques- AR Models, ARIMA, etc

Module 11: Decision Trees

Decision Trees - Introduction-Applications
Construction of Decision Trees through Simplified Examples
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical variables; other
Measures of Randomness
Pruning a Decision Tree; Cost as a consideration
Decision Trees – Validation
Over fitting- Best Practices to avoid

Module 12: Ensemble Learning

Module 13: Naive Bayes

Module 14: Model Evaluation, Improvements & Performance Metrics

Module 15: Kernel Learning

Module 16: Support Vector Machines

Module 17: Unsupervised Learning: Segmentation

Module 18: Natural Language Processing

What is NLP & How to solve NLP problems
NLP Feature Engineering & Modelling
How to process any raw data file
- Tokenizing, remove stopwords, speech tagging
- Stemming, Lemmatizing, CountVectorizer, Wordcloud
Build models for solving practical read world problems.

Module 19: Deep Learning - Artificial Neural Networks (ANN)

Motivation for Neural Networks and Its Applications
Perceptron and Single Layer Neural Network, and Hand Calculations
Learning In a Multi Layered Neural Net: Back-Propagation and Conjugant Gradient Techniques
Introducing & Using Tensorflow
Neural Networks for Regression
Neural Networks for Classification
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating ANN models

Module 20: End to End ML Implementation and Use Case specific discussions

PTN-107: Artificial Intelligence, Data Science and Machine Learning with Python