Post Graduate Diploma in Artificial Intelligence

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    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,
    Python institutes in delhi

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