Post Graduate Diploma in Artificial Intelligence

Module 1: Getting started with Python programming

• Overview
• Introductory Remarks about Python
• A Brief History of Python
• How python is differ from other languages
• Python Versions
• Installing Python and Environment Setup
• IDLE
• Getting Help
• How to execute Python program
• Writing your first Python program
• How to work on different Popular IDE’s
  [IDLE, Jupyter Notebook, Spyder etc.]

Module 2: Variables, Keywords and Operators

• Variables
• Memory mapping of variables
• Keywords in Python
• Comments in python
• Operators
• Arithmetic Operators
  1. Assignment Operators
  2. Comparison Operators
  3. Logical Operators
  4. Membership Operators
  5. Identity Operators
  6. Bitwise Operators
• >Basics I/O and Type casting
• Getting user input

Module 3: Data types in Python

• Numbers
• Strings
• Lists
• Tuples
• Dictionary
• Sets

Module 4: Numbers and Strings

• Introduction to Python ‘Number’ & ‘string’ data types
• Properties of a string
• String built-in functions
• Programming with strings
• String formatting

Module 5: Lists and Tuples

• Introduction to Python ‘list’ data type
• Properties of a list
• List built-in functions
• Programming with lists
• List comprehension
• Introduction to Python ‘tuple’ data type
• Tuples as Read only lists

Module 6: Dictionary and Sets

• Introduction to Python ‘dictionary’ data type
• Creating a dictionary
• Dictionary built-in functions
• Introduction to Python ‘set’ data type
• Set and set properties
• Set built-in functions

Module 7: Decision making & Loops

• Introduction of Decision Making
• Control Flow and Syntax
• The if Statement
• The if…else Statement
• The if…elif…else Statement
• Nested if…else Statement
• The while Loop
• break and continue Statement
• The for Loop
• Pass statement
• Exercise

Module 8: User defined Functions

• Introduction of functions
• Function definition and return
• Function call and reuse
• Function parameters
• Function recipe and docstring
• Scope of variables
• Recursive functions
• Lambda Functions / Anonymous Functions
• Map , Filter & Reduce functions
• Iterators
• Generators
• Zip function

Module 9: Modules and Packages

• Modules
• Importing module
• Standard Module – sys
• Standard Module – OS
• The dir Function
• Packages
• Exercise

Module 10: Exception Handling in Python

• Understanding exceptions
• Run Time Errors
• Handling I/O Exceptions
• try, except, else and finally statement
• raising exceptions with: raise, assert

Module 11: File Handling in Python

• Working with files
• File objects and Modes of file operations
• Reading, writing and use of ‘with’ keyword
• read(), readline(), readlines(), seek(), tell() methods
• Handling comma separated value files (CSV file handling)

Module 12: Regular expression

• Pattern matching
• Meta characters for making patterns
• re flags
• Use of match() , sub() , findall(), search(), split() methods

Module 13: Project Work

• Student Management System Project using List
• Bank Management System Project using Dictionary
• Hotel Management System Project using Function
• Employee Management System Project using File Handling

Part -2 Advance Excel:

Module 1: Introduction to MS Excel Environment

• Introduction to Excel Interface
• Features of MS Excel
• MS Excel functions
• Understanding of data calculations in Excel
• Formatting of data calculation
• Understanding about Sorting, Filtering & Validation MS
• Understanding of Data Tools Panel
• Excel Different Types of Charts Creation
• How to Create Pivot Table & Pivot Charts
• Basics of Macro Recording
• Implementation of Vlookup() & Hlookup() Functions

Module 2: Dashboard Designing in MS Excel

• Introduction to Dashboards
• Designing Sample Dashboard
• Step-by-step Excel dashboard tutorial
• Representation of Dashboard data
• Organizing data in Dashboard
• Tips and Tricks to enhance dashboard designing

Module 3: Project Work

• Dashboard Analysis Project in Excel
• Stock Management System Project in Excel

Part -3 SQL:

Module 1: Introduction to Database

• Overview of SQL
• Database Concepts
• What is Database Package
• Understanding Data Storage
• Relational Database (RDBMS) Concept

Module 2: SQL (Structured Query Language)

• SQL Basic
• SQL Commands
• DDL, TCL, DML & DQL
• DDL: create, alter, drop, rename
• SQL constraints: not null, unique, primary key etc.
• DML: insert, update, delete
• DQL: select
• TCL: rollback, commit
• Select distinct keyword
• SQL where
• SQL operators
• SQL like, not like
• SQL between, not between
• SQL order by
• SQL limit
• SQL aliases
• SQL joins: Inner join, Left Outer join, Right Outer join, Full join
• Mysql functions
• Numeric functions: max(), min(), avg(), sum(), count() etc.
• Date & Time functions: now(), today(), curdate(), curtime()
• SQL Subquery

Part -4 Data Analysis:

MODULE 1: GETTING STARTED WITH PYTHON LIBRARIES

• What is data analysis?
• Why python for data analysis?
• Essential Python Libraries Installation and setup
• Ipython
• Jupyter Notebook

MODULE 2: NUMPY ARRAYS

• Introduction to Numpy
• Numpy Arrays
• Numpy Data types
• Numpy Array Indexing
• Numpy Mathematical Operations
• Indexing and slicing
• Manipulating array shapes
• Stacking arrays
• Sorting arrays
• Creating array views and copies
• I/O with NumPy
• Numpy Statistics related Functions
• Numpy Exercises

MODULE 3: WORKING WITH PANDAS

• Introduction to Pandas
• Data structure of pandas
• Pandas Series
• Pandas dataframes
• Data aggregation with Pandas
• DataFrames Concatenating and appending
• DataFrames Joining
• DataFrames Handling missing data
• Data Indexing and Selection
• Operating on data in pandas
• loc and iloc
• map,apply,apply_map
• group_by
• string methods
• Querying data in pandas
• Dealing with dates
• Reading and Writing to CSV files with pandas
• Reading and Writing to Excel with pandas
• Reading and Writing to SQL with pandas
• Reading and Writing to HTML files with pandas
• Pandas Exercises

Part -5 Data Visualization:

MODULE 1: Matplotlib

• Introduction of Matplotlib
• Basic matplotlib plots
• Line Plots
• Bar Plots
• Pie Plots
• Scatter plots
• Histogram Plots
• Subplot
• Saving plots to file
• Plotting functions in matplotlib
• Matplotlib Exercises

MODULE 2: Seaborn

• Introduction of Seaborn
• Categorical Plots
• Bar Plots
• Box Plots
• Heatmaps Plots
• Pair Plots
• Regression Plots
• Style and Color
• Seaborn Exercise

MODULE 3: Plotly – Python Plotting

• Introduction to Plotly – Python Plotting
• Plotly

MODULE 4: Geographical Plotting

• Introduction to Geographical Plotting
• Choropleth Maps – Part 1
• Choropleth Maps – Part 2
• Choropleth Exercises
• Projects using Analysis and Visualisation

Part -6 Statistics, Probability & Business Analytics:

MODULE 1: Introduction to Basic Statistics

• Overview of Statistics
• Data types and their measures
• Measures of Central Tendency
• Arithmetic mean
• Harmonic mean
• Geometric mean
• Median
• Mode
• Variance
• Standard deviation
• Quartile: First quartile, Second quartile, Third quartile, IQR
• Correlation & Covariance Matrix

MODULE 2: Probability Distributions

• Introductio of probability
• Conditiona probability
• Norma distribution
• Unifor distribution
• Frequenc distribution
• Centra limit theorem

MODULE 3: Hypothesis Testing

• Concept of Hypothesis Testing
• Statistical Methods
• Z-Test
• T-Test
• Chi-Square Test
• One Way Anova Test
• Two Way Anova Test

Part -7 Machine Learning:

MODULE 1: Introduction to Machine Learning

• What is Machine learning?
• Overview about scikit-learn package
• Types of ML
• Basic steps of ML
• ML algorithms
• Machine learning examples

MODULE 2: Data Preprocessing / Data Cleaning

• Dealing with missing data
• Identifying missing values
• Imputing missing values
• Drop samples with missing values
• Handling with categorical data
• Nominal and Ordinal features
• Encoding class labels
• One hot encoding
• Split data into training and testing sets
• Feature scaling
• Feature Selection
• How to Handle Outliers & Removal
• Underfitting and Overfitting

MODULE 3: Supervised Learning

• Classification
• Regression

MODULE 4: Unsupervised Learning

• Clustering

MODULE 5: KNN Classifiers

• K-Nearest Neighbours (KNN)
• KNN Theory
• KNN implementation
• KNN Project Overview and Project Solutions

MODULE 6: Regression Based Learning

• Linear Regression Theory
• Dependent and independent Variables
• Linear Regression with Python implementation
• Linear Regression Project on Predicting House Price
• Multiple linear regression
• Polynomial regression
• Regularization

MODULE 7: Logistic Regression for Classification

• Logistic Regression Theory
• Binary and multiclass classification
• Implementing titanic dataset
• Implementing iris dataset
• Sigmoid and softmax functions

MODULE 8: Decision Tree Classification

• Introduction to decision trees
• Entropy and Information gain
• Introduction to bagging algorithm
• Implementation with iris dataset
• Visualizing Decision Tree
• Ensemble Learning
• Random forest
• Bagging and boosting
• Voting classifier
• Project on Decision Tree models

MODULE 9: Support Vector Machines (SVM)

• Introduction to SVM
• Working of SVM and its uses
• Working with High Dimensional Data
• Kernel, gamma, margin
• Breast Cancer Prediction Project using SVM

MODULE 10: Naive Bayes Algorithm

• Conditional Probability
• Overview of Naïve Bayes Algorithm
• Feature extraction
• CountVectorizer
• TfidfVectorizer
• Email Spam Filtering Project using naïve Bayes classifier

MODULE 11 Model Selection Techniques

• Cross Validation via K-Fold
• Grid and random search for hyper parameter tuning

MODULE 12: Clustering Based Learning

• K-means Clustering Algorithm
• Elbow technique
• Silhouette coefficient
• K Means Clustering Project Overview
• K Means Clustering Project Solutions

MODULE 13: Recommendation System

• Content based technique
• Collaborative filtering technique
• Evaluating similarity based on correlation
• Classification-based recommendations
• Movie Recommendation System Project

MODULE 14: Principal Component Analysis

• Need for dimensionality reduction
• Principal Component Analysis (PCA)
• PCA Project with Python on cancer Dataset

MODULE 15: Natural Language Processing (NLP)

• Install nltk
• Tokenize words
• Tokenizing sentences
• Stop words with NLTK
• Stemming & Lemmatization words with NLTK
• Sentiment Analysis
• Twitter Sentiment analysis Project

MODULE 16: Working with OpenCV (Computer Vision)

• Basic of Computer Vision & OpenCV
• Reading and writing images
• Resizing image
• Applying image filters
• Writing text on images
• Image Manipulations
• Image Segmentation
• Understanding haar classifiers
• Object Detection
• People ,Car, Bike, Bus Detection
• Face, eyes detection
• How to use webcam in OpenCV
• Building image dataset
• Capturing video
• Face Recognition based Attendance System Project

Part -8 Deep Learning:

MODULE 1: Introduction to Deep Learning

• What is Deep Learning?
• Deep Learning Packages
• Deep Learning Applications
• Building deep learning environment
• Installing tensor flow locally
• Understanding Google Colab

MODULE 2: Tensor Flow Basics

• What is tensorflow?
• Tensorflow 1.x v/s tensorflow 2.x
• Variables, Constants, Placeholder
• Scalar, vector, matrix
• Operations using tensorflow
• Difference between tensorflow and numpy operations
• Tensor Flow Computational graph

MODULE 3: Introduction to Artificial Neural Network

• What is Artificial Neural Network (ANN)?
• How neural network works?
• Perceptron
• Multilayer Perceptron
• Feedforward
• Back propagation

MODULE 4: Activation Functions

• What does Activation Functions do?
• Linear Activation Function
• Sigmoid function
• Hyperbolic tangent function (tanh)
• ReLU –rectified linear unit
• Softmax function

MODULE 5: Optimizers

• What does optimizers do?
• Gradient descent
• Stochastic gradient descent
• Learning rate , epoch

MODULE 6: Building Artificial Neural Network

• Using scikit implementation
• Using tensorflow
• Understanding mnist dataset
• Initializing weights and biases
• Defining loss/cost function
• Train the neural network
• Minimizing the loss by adjusting weights and biases

MODULE 7: Modern Deep Learning Optimizers

• SGD with momentum
• RMSprop
• AdaGrad
• Adam
• Dropout layers and regularization

MODULE 8: Building Deep Neural Network Using Keras

• What is keras?
• Keras fundamental for deep learning
• Keras sequential model and functional api
• Solve a linear regression and classification problem with example
• Saving and loading a keras model

MODULE 9: Convolutional Neural Network (CNN)

• Introduction to CNN?
• CNN architecture
• Convolutional operations
• Pooling, stride and padding operations
• Data augmentation
• Building, training and evaluating first CNN model
• Auto encoders for CNN

MODULE 10: Recurrent Neural Network (RNN)

• Introduction to RNN?
• RNN architecture
• Implementing basic RNN in tensorflow
• Need for LSTM and GRU
• Text classification using LSTM

MODULE 11: Speech Recognition APIs

• Text to speech
• Speech to Text
• Automate task using voice
• Voice search on web

MODULE 12: Projects

• Bike Detection & Counting the no. of Bikes passing Yellow Line
• Stock Price Prediction Using LSTM
• Attendance System Using based on Face Recognition
• Gender Prediction Project
• Face Mask Detection Project using keras & openCV
• Email spam filtering Project
• Hand Written Digits & Letters Prediction
• Movie Recommendation System
• Chat Bot Project using Tensorflow with Keras
• Virtual Voice Assistant Project

Part -9 R Language:

Module 1: Introduction to R

• What is R
• History of R
• Features of R
• Obtaining and managing R
• Installing R
• Perform basic operations in R using command line
• Packages
• Input/output
• R interfaces
• R Library
• Working with RStudio

Module 2: Data Types and Objects

• Data Types
• Variables in R
• Scalars
• Vectors
• Factors
• Numbers
• Attributes
• Entering Inputs
• Evaluation
• Printing
• Missing Objects
• Data Frames
• R Objects
• Matrices
• Using c, Cbind and Rbind
• attach and detach functions in R
• Lists
• Missing Values
• Names

Module 3: Data Management

• Reading Data
• Writing data
• Reading data files with tables
• Files connection
• Reading lines of Text files
• Sorting Data
• Merging Data
• Aggregating Data
• Reshaping Data

Module 4: Import and Export Data in R

• Importing data in to R
• CSV File
• Excel File
• Import data from text table

Module 5: Control Structures and Functions

• Control statements [if,if..else,next,return]
• Loop statements [while, for, repeat]
• Functions
• Function arguments & options
• Scoping rules of R
• Loop Functions [Lapply,Tapply,Mapply,Sapply,Apply etc.]

Module 6: Database connectivity with R

• How to install RMysql Package
• How to connect R to Mysql Database
• Operation on Mysql Queries on R

Module 7: Date and Time in R

• Dates in R
• Times in R
• Operation on Dates and Time on R

Module 8: Regular Expression & Random Numbers

• Creating Random Numbers
• Generating Random Numbers
• Random Sampling
• Pattern matching
• Meta characters for making patterns
• Regular Expression functions

Part -10 Data Analysis using R:

MODULE 1: GETTING STARTED WITH R LIBRARIES

• What is data analysis?
• Why R for data analysis?
• Essential R Libraries Installation and setup
• Rstudio Overview

MODULE 2: MANAGING DATA FRAMES WITH THE DPLYR PACKAGE

• The dplyr Package
• Installing the dplyr package
• select()
• filter()
• arrange()
• rename()
• slice()
• join()
• distinct()
• mutate()
• group_by()
• summarise()
• sample_n()
• sample_frac()
• %>% [Pipe Operator]

MODULE 3: DATA CLEANING WITH THE TIDYR PACKAGE

• The tidyr Package
• Installing the tidyr package
• Installing the data.table package
• Cleaning data
• Data Sorting
• Merging data
• Find & Remove duplicate record
• gather()
• spread()
• separate()
• unite()

Note :

• In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data
  set, which is ready for any analysis.
• Thus using and exploring the popular functions required to clean data in R.

Part -11 Data Visualization using R:

MODULE 1: GRAPHICS AND PLOTTING USING R

• Basic Plotting
• Basic plots in R
• Line Plots
• Bar Plots
• Pie Plots
• Box Plots
• Scatter plots
• Histogram Plots
• Saving plots to file
• Plotting functions in R
• Plotting Exercises

MODULE 2: ADVANCE PLOTTING
GGPLOT 2 Visualization

• Introduction of ggplot2 visualization package
• Layers of ggplot2 package
• How to install ggplot2 package
• Introduction of ggplot2 cheat sheet
• Histogram Plots
• Scatter plots
• Bar Plots
• Box Plots
• Line Plots
• Pie Plots
• QQ Plots
• Style and Color
• ggplot2 Package Exercise

MODULE 3: Interactive Visualizations with Plotly

• Overview of Plotly and Interactive Visualizations
• How to install Plotly Interactive Visualization Package
• Plotly Visualizations

Part -12 Core Tableau for Data Analysis:

MODULE 1: Working with Tableau Desktop

• Introduction to Tableau
• What is Tableau?
• Kinds of tableau
• Tableau architecture
• Overview to different versions
• Installation of Tableau Desktop
• Understanding tableau user interface
• Connect Tableau to Datasets
• Data analysis with tableau
• Bar Charts
• Area Charts
• Scatter Charts
• Pie Charts
• Creating maps and setting map options
• Creating Dashboards
• Interactive Dashboards
• Storylines
• Joins
• Data Blending
• Table Calculations
• Parameters
• Dual Axis Charts
• Calculated Fields
• Time series Data Analysis
• Data Extracts
• Aggregation, Granularity, and Level of Detail
• Filters and Quick Filters
• Data Hierarchies
• Assigning Geographical Roles to Data Elements
• Assignments and Projects

For a complete breakdown of the modules in this Course,

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