 What is a Data Science?
 Who is a Data Scientist?
 Who can become a Data Scientist?
 What is an Artificial Intelligence?
 What is a Machine Learning?
 What is a Deep Learning?
 Artificial Intelligence Vs Machine Learning Vs Deep Learning
    Real Time Process of Data Science
 Data Science Real Time Applications
 Technologies used in Data Science
 Prerequisites Knowledge to Learn Data Science

 What is a Machine Learning?
 Machine Learning Vs Statistics
 Traditional Programming Vs Machine Learning
 How Machine Will Learn like Human Learning
 Machine Learning Engineer Responsibilities
 Types of Machine Learning
 Supervised learning
 Un-Supervised learning
 Reinforcement Learning

PYTHON Programming Introduction
 History of Python
 Python is Derived from?
 Python Features
 Python Applications
 Why Python is Becoming Popular Now a Day?
 Existing Programming Vs Python Programming
 Writing Programs in Python Top Companies Using Python
 Python Programming Modes
   o Interactive Mode Programming
   o Scripting Mode Programming
 Flavors in Python, Python Versions
 Download & Install the Python in Windows & Linux
 How to set Python Environment in the System?
 Anaconda – Data Science Distributor
 Downloading and Installing Anaconda, Jupyter Notebook &
Spyder
 Python IDE – Jupyter Notebook Environment
 Python IDE – Spyder Environment
 Python Identifiers(Literals), Reserved Keywords
 Variables, Comments
 Lines and Indentations, Quotations
 Assigning Values to Variables
 Data Types in Python
 Mutable Vs Immutable
 Fundamental Data Types: int, float, complex, bool, str
 Number Data Types: Decimal, Binary, Octal, Hexa Decimal &
Number Conversions
 Inbuilt Functions in Python
 Data Type Conversions
 Priorities of Data Types in Python
 Python Operators

o Arithmetic Operators
o Comparison (Relational) Operators
o Assignment Operators
o Logical Operators
o Bitwise Operators
o Membership Operators
o Identity Operators

 Slicing & Indexing
o Forward Direction Slicing with +ve Step
o Backward Direction Slicing with -ve Step
 Decision Making Statements
o if Statement
o if-else Statement
o elif Statement
 Looping Statements
o Why we use Loops in python?
o Advantages of Loops
o for Loop

o Nested for Loop
o Using else Statement with for Loop
o while Loop
o Infinite while Loop
o Using else with Python while Loop
 Conditional Statements
o break Statement
o continue Statement
o Pass Statement

Advanced Data Types: List, Tuple, Set, Frozenset, Dictionary,
Range, Bytes & Bytearray, None
 List Data Structure
o List indexing and splitting
o Updating List values
o List Operations
o Iterating a List
o Adding Elements to the List
o Removing Elements from the List
o List Built-in Functions
o List Built-in Methods
 Tuple Data Structure
o Tuple Indexing and Splitting
o Tuple Operations
o Tuple Inbuilt Functions
o Where use Tuple
o List Vs Tuple
o Nesting List and Tuple
 Set Data Structure
o Creating a Set
o Set Operations
o Adding Items to the Set
o Removing Items from the Set
o Difference Between discard() and remove()
o Union of Two Sets
o Intersection of Two Sets
o Difference of Two Sets
o Set Comparisons
 Frozenset Data Structure
 Dictionary Data Structure
o Creating the Dictionary
o Accessing the Dictionary Values              o Updating Dictionary Values
o Deleting Elements Using del Keyword
o Iterating Dictionary
o Properties of Dictionary Keys
o Built-in Dictionary Functions
o Built-in Dictionary Methods
 List Vs Tuple Vs Set Vs Frozenset Vs Dict
 Range, Bytes, Bytearray & None
 Python Functions
o Advantage of Functions in Python
o Creating a Function
o Function Calling
o Parameters in Function
o Call by Reference in Python
o Types of Arguments
 Required Arguments
 Keyword Arguments
 Default Arguments
 Variable-Length Arguments
 Scope of Variables
 Python Built-in Functions
 Python Lambda Functions
 String with Functions
o Strings Indexing and Splitting
o String Operators
o Python Formatting Operator
o Built-in String Functions
 Python File Handling
o Opening a File
o Reading the File
o Read Lines of the File
o Looping through the File
o Writing the File
o Creating a New File
o Using with Statement with Files
o File Pointer Positions
o Modifying File Pointer Position
o Renaming the File & Removing the File
o Writing Python Output to the Files
o File Related Methods
 Python Exceptions
o Common Exceptions
o Problem without Handling Exceptions        o except Statement with no Exception
o Declaring Multiple Exceptions
o Finally Block
o Raising Exceptions
o Custom Exception
 Python Packages
o Python Libraries
o Python Modules
 Collection Module
 Math Module
 OS Module
 Random Module
 Statistics Module
 Sys Module
 Date & Time Module
o Loading the Module in our Python Code
 import Statement
 from-import Statement
o Renaming a Module
 Regular Expressions
 Command Line Arguments
 Object Oriented Programming (OOPs)
o Object-oriented vs Procedure-oriented Programming
languages
o Object
o Class
o Method
o Inheritance
o Polymorphism
o Data Abstraction
o Encapsulation
 Python Class and Objects
o Creating Classes in Python
o Creating an Instance of the Class
 Python Constructor
o Creating the Constructor in Python
o Parameterized Constructor
o Non-Parameterized Constructor
o In-built Class Functions
o In-built Class Attributes
 Python Inheritance
o Python Multi-Level Inheritance
o Python Multiple Inheritance                          o Method Overriding
o Data Abstraction in Python
 Graphical User Interface (GUI) Programming
 Python TKinter
o Tkinter Geometry
 pack() Method
 grid() Method
 place() Method
o Tkinter Widgets 

 NumPy Introduction
o What is NumPy
o The Need of NumPy
 NumPy Environment Setup
 N-Dimensional Array (Ndarray)
o Creating a Ndarray Object
o Finding the Dimensions of the Array
o Finding the Size of Each Array Element
o Finding the Data Type of Each Array Item
o Finding the Shape and Size of the Array
o Reshaping the Array Objects
o Slicing in the Array
o Finding the Maximum, Minimum, and Sum of the Array
Elements
o NumPy Array Axis
o Finding Square Root and Standard Deviation
o Arithmetic Operations on the Array
o Array Concatenation
 NumPy Datatypes
o NumPy dtype
o Creating a Structured Data Type
 Numpy Array Creation
o Numpy.empty
o Numpy.Zeros
o NumPy.ones
 Numpy Array from Existing Data
o Numpy.asarray
 Numpy Arrays within the Numerical Range
o Numpy.arrange
o NumPy.linspace
o Numpy.logspace
 NumPy Broadcasting  o Broadcasting Rules
 NumPy Array Iteration
o Order of Iteration
 F-Style Order
 C-Style Order
o Array Values Modification
 NumPy String Functions
 NumPy Mathematical Functions
o Trigonometric Functions
o Rounding Functions
 NumPy Statistical functions
o Finding the Min and Max Elements from the Array
o Calculating Median, Mean, and Average of Array Items
 NumPy Sorting and Searching
 NumPy Copies and Views
 NumPy Matrix Library
 NumPy Linear Algebra
 NumPy Matrix Multiplication in Python

 Pandas Introduction & Pandas Environment Setup
o Key Features of Pandas
o Benefits of Pandas
o Python Pandas Data Structure
 Series
 DataFrame
 Panel
 Pandas Series
o Creating a Series
 Create an Empty Series
 Create a Series using Inputs
o Accessing Data from Series with Position
o Series Object Attributes
o Retrieving Index Array and Data Array of a Series Object
o Retrieving Types (dtype) and Size of Type (itemsize)
o Retrieving Shape
o Retrieving Dimension, Size and Number of Bytes
o Checking Emptiness and Presence of NaNs
o Series Functions
 Pandas DataFrame
o Create a DataFrame
 Create an Empty DataFrame
 Create a DataFrame using Inputs                         Column Selection, Addition & Deletion
 Row Selection, Addition & Deletion
 DataFrame Functions
 Merging, Joining & Combining DataFrames
 Pandas Concatenation
 Pandas Time Series
o Datetime
o Time Offset
o Time Periods
o Convert String to Date
 Viewing/Inspecting Data (loc & iloc)
 Data Cleaning
 Filter, Sort, and Groupby
 Statistics on DataFrame
 Pandas Vs NumPy
 DataFrame Plotting
o Line: Line Plot (Default)
o Bar: Vertical Bar Plot
o Barh: Horizontal Bar Plot
o Hist: Histogram Plot
o Box: Box Plot
o Pie: Pie Chart
o Scatter: Scatter Plot

Introduction & Models of DBMS
 SQL & Sub Language of SQL
 Data Definition Language (DDL)
 Data Manipulation Language (DML)
 Data Query/Retrieval Language (DQL/DRL)
 Transaction Control Language (TCL)
 Data Control Language (DCL)
 Installation of MySQL & Database Normalization
 Sub Queries & Key Constraints
 Aggregative Functions, Clauses & Views

Data Extraction from CSV (pd.read_csv)
 Data Extraction from TEXT File (pd.read_table)
 Data Extraction from CLIPBOARD (pd.read_clipboard)
 Data Extraction from EXCEL (pd.read_excel)
 Data Extraction from URL (pd.read_html)
 Writing into CSV (df.to_cs                                   Writing into EXCEL (df.to_excel)
 Data Extraction from DATABASES
o Python MySQL Database Connection
 Import mysql.connector Module
 Create the Connection Object
 Create the Cursor Object
 Execute the Query

 Data Visualization Introduction
 Tasks of Data Visualization
 Benefit of Data Visualization
 Plots for Data Visualization
 Matplotlib Architecture
 General Concept of Matplotlib
 MatPlotLib Environment Setup
 Verify the MatPlotLib Installation
 Working with PyPlot
 Formatting the Style of the Plot
 Plotting with Categorical Variables
 Multi-Plots with Subplot Function
 Line Graph
 Bar Graph
 Histogram
 Scatter Plot
 Pie Plot
 3Dimensional – 3D Graph Plot
 mpl_toolkits
 Functions of MatPlotLib
 Contour Plot, Quiver Plot, Violin Plot
 3D Contour Plot
 3D Wireframe Plot
 3D Surface Plot
 Box Plot
o What is a Boxplot?
o Mean, Median, Quartiles, Outliers
o Inter Quartile Range (IQR), Whiskers
o Data Distribution Analysis
o Boxplot on a Normal Distribution
o Probability Density Function
o 68–95–99.7 Rule (Empirical rule)

 What is Machine Learning
 Importance of Machine Learning
 Need for Machine Learning
 Statistics Vs Machine Learning
 Traditional Programming Vs Machine Learning
 How Machine Learning like Human Learning
 How does Machine Learning Work?
 Machine Learning Engineer Responsibilities
 Life Cycle of Machine Learning
o Gathering Data
o Data preparation
o Data Wrangling
o Analyze Data
o Train the model
o Test the model
o Deployment
 Features of Machine Learning
 History of Machine Learning
 Applications of Machine Learning
 Types of Machine Learning
o Supervised Machine Learning
o Unsupervised Machine Learning
o Reinforcement Learning

How Supervised Learning Works?
 Steps Involved in Supervised Learning
 Types of supervised Machine Learning Algorithms
o Classification
o Regression
 Advantages of Supervised Learning
 Disadvantages of Supervised Learning

 How Unsupervised Learning Works?
 Why use Unsupervised Learning?
 Types of Unsupervised Learning Algorithm
o Clustering
o Association
 Advantages of Unsupervised Learning
 Disadvantages of Unsupervised Learning
 Supervised Vs Unsupervised Learning     Reinforcement Machine Learning
 How to get Datasets for Machine Learning?
o What is a Dataset?
o Types of Data in Datasets
o Popular Sources for Machine Learning Datasets 

Why do we need Data Preprocessing?
o Getting the Dataset
o Importing Libraries
o Importing Datasets
o Finding Missing Data
 By Deleting the Particular Row
 By Calculating the Mean
o Encoding Categorical Data
 LableEncoder
 OneHotEncoder
o Splitting Dataset into Training and Test Set
o Feature Scaling
 Standardization
 Normalization

What is the Classification Algorithm?
 Types of Classifications
o Binary Classifier
o Multi-class Classifier
 Learners in Classification Problems
o Lazy Learners
o Eager Learners
 Types of ML Classification Algorithms
o Linear Models
 Logistic Regression
 Support Vector Machines
o Non-linear Models
 K-Nearest Neighbors
 Naïve Bayes
 Decision Tree Classification
 Random Forest Classification
 Kernel SVM
 Evaluating a Classification Model
o Confusion Matrix
 What is a Confusion Matrix?                                 True Positive
 True Negative
 False Positive – Type 1 Error
 False Negative – Type 2 Error
 Why need a Confusion matrix?
 Precision
 Recall
 Precision vs Recall
 F1-score
 Confusion Matrix in Scikit-Learn
 Confusion Matrix for Multi-Class Classification
o Log Loss or Cross-Entropy Loss
o AUC-ROC curve
 Use cases of Classification Algorithms

 Why do we Need a K-NN Algorithm?
 How does K-NN work?
o What is Euclidean Distance
o How it Calculates the Distance
 How to Select the Value of K in the K-NN Algorithm?
 Advantages of KNN Algorithm
 Disadvantages of KNN Algorithm
 Python Implementation of the KNN Algorithm
 Analysis on Social Network Ads Dataset
 Steps to Implement the K-NN Algorithm
o Data Pre-processing Step
o Fitting the K-NN algorithm to the Training Set
o Predicting the Test Result
o Test Accuracy of the Result (Creation of Confusion Matrix)
o Visualizing the Test Set Result.
o Improve the Performance of the K-NN Model

Why is it Called Naïve Bayes?
o Naïve Means?
o Bayes Means?
 Bayes’ Theorem
o Posterior Probability
o Likelihood Probability
o Prior Probability
o Marginal Probability
 Working of Naïve Bayes’ Classifier                 Advantages of Naïve Bayes Classifier
 Disadvantages of Naïve Bayes Classifier
 Applications of Naïve Bayes Classifier
 Types of Naïve Bayes Model
o Gaussian Naïve Bayes Classifier
o Multinomial Naïve Bayes Classifier
o Bernoulli Naïve Bayes Classifier
 Python Implementation of the Naïve Bayes Algorithm
 Steps to Implement the Naïve Bayes Algorithm
o Data Pre-processing Step
o Fitting Naive Bayes to the Training set
o Predicting the Test Result
o Test Accuracy of the Result (Creation of Confusion matrix)
o Visualizing the Test Set Result
o Improve the Performance of the Naïve Bayes Model

Why use Decision Trees?
 Types of Decision Trees
o Categorical Variable Decision Tree
o Continuous Variable Decision Tree
 Decision Tree Terminologies
 How does the Decision Tree Algorithm Work?
 Attribute Selection Measures
o Entropy
o Information Gain
o Gini index
o Gain Ratio
 Algorithms used in Decision Trees
o ID3 Algorithm → (Extension of D3)
o C4.5 Algorithm→ (Successor of ID3)
o CART Algorithm → (Classification & Regression Tree)
 How to Avoid/Counter Overfitting in Decision Trees?
o Pruning Decision Trees
o Random Forest
 Pruning: Getting an Optimal Decision tree
 Advantages of the Decision Tree
 Disadvantages of the Decision Tree
 Python Implementation of Decision Tree
 Steps to Implement the Decision Tree Algorithm
o Data Pre-processing Step
o Fitting a Decision-Tree Algorithm to the Training Set
o Predicting the Test Result                                    Test Accuracy of the Result (Creation of Confusion matrix)
o Visualizing the Test Set Result
o Improve the Performance of the Decision Tree Model

Working of the Random Forest Algorithm
 Assumptions for Random Forest
 Why use Random Forest?
 How does Random Forest Algorithm Work?
o Ensemble Techniques
o Bagging (Bootstrap Aggregation)
 Applications of Random Forest
 Disadvantages of Random Forest
 Python Implementation of Random Forest Algorithm
 Steps to Implement the Random Forest Algorithm:
o Data Pre-processing Step
o Fitting the Random Forest Algorithm to the Training Set
o Predicting the Test Result
o Test Accuracy of the Result (Creation of Confusion Matrix)
o Visualizing the Test Set Result
o Improving the Performance of the Random Forest Model

Logistic Function (Sigmoid Function)
 Assumptions for Logistic Regression
 Logistic Regression Equation
 Type of Logistic Regression
o Binomial Logistic Regression
o Multinomial Logistic Regression
o Ordinal Logistic Regression
 Python Implementation of Logistic Regression (Binomial)
 Steps to Implement the Logistic Regression:
o Data Pre-processing Step
o Fitting Logistic Regression to the Training Set
o Predicting the Test Result
o Test Accuracy of the Result (Creation of Confusion Matrix)
o Visualizing the Test Set Result
o Improve the Performance of the Logistic Regression Model

Types of Support Vector Machines
o Linear Support Vector Machine
o Non-Linear Support Vector Machine                        Hyperplane in the SVM Algorithm
 Support Vectors in the SVM Algorithm
 How does SVM Works?
o How does Linear SVM Works?
o How does Non-Linear SVM Works?
 Python Implementation of Support Vector Machine
 Steps to Implement the Support Vector Machine:
o Data Pre-processing Step
o Fitting Support Vector Machine to the Training Set
o Predicting the Test Result
o Test Accuracy of the Result (Creation of Confusion Matrix)
o Visualizing the Test Set Result
o Improve the Performance of the Support Vector Machine
Model 

 Terminologies Related to the Regression Analysis
o Dependent Variable
o Independent Variable
o Outliers
o Multi-collinearity
o Under fitting and Overfitting
 Why do we use Regression Analysis?
 Types of Regression
o Linear Regression
o Logistic Regression
o Polynomial Regression
o Support Vector Regression
o Decision Tree Regression
o Random Forest Regression
o Ridge Regression
o Lasso Regression

Types of Linear Regression
o Simple Linear Regression
o Multiple Linear Regression
 Linear Regression Line
o Positive Linear Relationship
o Negative Linear Relationship
 Finding the Best Fit Line
o Cost Function
o Gradient Descent            o Model Performance
o R-Squared Method
 Assumptions of Linear Regression

SLR Model
 Implementation of Simple Linear Regression Algorithm using
Python
o Data Pre-processing Step
o Fitting Simple Linear Regression to the Training Set
o Predicting the Test Result
o Test Accuracy of the
o Visualizing the Test Set Result.
o Try to Improve the Performance of the Model

 MLR Equation
 Assumptions for Multiple Linear Regression
 Implementation of Multiple Linear Regression model using Python
o Data Pre-processing Step
o Fitting Multiple Linear Regression to the Training Set
o Predicting the Test Result
o Test Accuracy of the
o Visualizing the Test Set Result.
o Try to Improve the Performance of the Model

 What is Backward Elimination?
 Steps of Backward Elimination
 Need for Backward Elimination: An optimal Multiple Linear
Regression model
 Implement the Steps for Backward Elimination method

Need for Polynomial Regression
 Equation of the Polynomial Regression Model
 Implementation of Polynomial Regression using Python
 Steps for Polynomial Regression:
o Data Pre-processing
o Build a Linear Regression Model
o Build a Polynomial Regression Model
o Visualize the Result for Linear Regression Model
o Visualize the Result for Polynomial Regression Model                                  o Predicting the Final Result with the Linear Regression Model
o Predicting the Final Result with the Polynomial Regression
Model
 Support Vector Regression (SVR)
 Decision Tree Regression
 Random Forest Regression
 Ridge Regression
 Lasso Regression
 Linear Regression Vs Logistic Regression
 Classification vs Regression 

 Types of Clustering Methods
o Partitioning Clustering
o Density-Based Clustering
o Distribution Model-Based Clustering
o Hierarchical Clustering
o Fuzzy Clustering
 Clustering Algorithms
o K-Means Algorithm
o Mean-shift Algorithm
o DBSCAN Algorithm
o Expectation-Maximization Clustering using GMM
o Agglomerative Hierarchical Algorithm
o Affinity Propagation
 Applications of Clustering

Hierarchical Clustering Technique Approaches
 Why Hierarchical Clustering?
 Agglomerative Hierarchical Clustering
 How the Agglomerative Hierarchical Clustering Work?
 Measure for the Distance between two Clusters
o Single Linkage
o Complete Linkage
o Average Linkage
o Centroid Linkage
 Working of Dendrogram in Hierarchical Clustering
 Hierarchical Clustering Example with Scratch Data
 Python Implementation of Agglomerative Hierarchical Clustering
 Steps for Implementation of Agglomerative Hierarchical
Clustering using Python
o Data Pre-processing      o Finding the Optimal Number of Clusters using the
Dendrogram
o Training the Hierarchical Clustering Model
o Visualizing the Clusters 

Toggle Content

What is K-Means Algorithm?
 How does the K-Means Algorithm Work?
 How to Choose the Value of “K Number of Clusters” in K-Means
Clustering?
o Elbow Method
o Within Cluster Sum of Squares (WCSS)
 K-Means Clustering Example with Scratch Data
 Python Implementation of K-means Clustering Algorithm
 Steps to Implement of K-means Clustering Algorithm
o Data Pre-processing
o Finding the Optimal Number of Clusters using the Elbow
Method
o Training the K-means Algorithm on the Training Dataset
o Visualizing the Cluster

Apriori Algorithm
 How does Apriori Algorithm Works?
 Apriori Algorithm Example
 Implementation of Apriori Algorithm using Python
 Limitations of Apriori Algorithm

Mean, Median and Mode
 Data Variability, Range, Quartiles
 IQR, Calculating Percentiles
 Variance, Standard Deviation, Statistical Summaries
 Types of Distributions – Normal, Binomial, Poisson
 Probability Distributions & Skewness
 Data Distribution, 68–95–99.7 rule (Empirical rule)
 Descriptive Statistics and Inferential Statistics
 Statistics Terms and Definitions, Types of Data
 Data Measurement Scales, Normalization, Standardization
 Measure of Distance, Euclidean Distance
 Probability Calculation – Independent & Dependent
 Entropy, Information Gain
 Regression

 Natural Language Processing Introduction
o What is NLP?
o History of NLP
o Advantages of NLP
o Disadvantages of NLP
 Components of NLP
o Natural Language Understanding (NLU)
o Natural Language Generation (NLG)
o Difference between NLU and NLG
 Applications of NLP
 How to build an NLP Pipeline?
 Phases of NLP
o Lexical Analysis and Morphological
o Syntactic Analysis (Parsing)
o Semantic Analysis
o Discourse Integration
o Pragmatic Analysis
 Why NLP is Difficult?
 NLP APIs
 NLP Libraries
 Natural Language Vs Computer Language

o Open the Text File for Processing
o Import Required Libraries
o Sentence Tokenizing
o Word Tokenizing
o Find the Frequency Distribution
o Plot the Frequency Graph
o Remove Punctuation Marks
o Plotting Graph without Punctuation Marks
o List of Stopwords
o Removing Stopwords
o Final Frequency Distribution
 Word Cloud
o Word Cloud Properties
o Python Code Implementation of the Word Cloud
o Word Cloud with the Circle Shape
o Word Cloud Advantages
o Word Cloud Disadvantages
 Stemming
o Stemmer Examples
o Stemming Algorithms
 Porter’s Stemmer
 Lovin’s Stemmer
 Dawson’s Stemmer
 Krovetz Stemmer
 Xerox Stemmer
 Snowball Stemmer
 Lemmatization
o Difference between Stemmer and Lemmatizer
o Demonstrating how a lemmatizer works
o Lemmatizer with default PoS value
o Demonstrating the power of lemmatizer
o Lemmatizer with different POS values
 Part-of-Speech (PoS) Tagging
o Why do we need Part of Speech (POS)?
o Part of Speech (PoS) Tags
 Chunking
o Categories of Phrases
o Phrase Structure Rules
 Chinking
 Named Entity Recognition (NER)
o Use-Cases
o Commonly used Types of Named Entity                 WordNet
 Bag of Words
o What is the Bag-of-Words method?
o Creating a basic Structure on Sentences
o Words with Frequencies
o Combining all the Words
o Final Model of our Bag of Words
o Applications & Limitations
 TF-IDF
o Term Frequency
o Inverse Document Frequency
o Term Frequency – Inverse Document Frequency

What is Model Deployment?
 What is Flask?
 Installing Flask on your Machine
 Understanding the Problem Statement
 Build our Machine Learning Model
 Create the Webpage
 Connect the Webpage with the Model
 Working of the Deployed Mode

What is Deep Learning?
 Deep learning Process
 Types of Deep Learning Networks
o Deep Neural Networks
o Artificial Neural Networks
o Convolutional Neural Networks
o Recurrent Neural Networks
 TensorFlow
o History of TensorFlow
o Components of TensorFlow
o Use Cases/Applications of TensorFlow
o Features of TensorFlow
 Installation of TensorFlow through pip & conda
 Advantage and Disadvantage of TensorFlow
 TensorFlow Playground
 Introduction to Keras, OpenCV & Theano
 Implementation of Deep Learning

What is Artificial Intelligence?
o Why Artificial Intelligence?
o Goals of Artificial Intelligence
o What Comprises to Artificial Intelligence?
o Advantages of Artificial Intelligence
o Disadvantages of Artificial Intelligence
 Applications of Artificial Intelligence
 History of Artificial Intelligence
 Types of Artificial Intelligence
 Types of AI Agents
o Simple Reflex Agent
o Model-Based Reflex Agent
o Goal-Based Agents
o Utility-Based Agent
o Learning Agent
 Search Algorithms in Artificial Intelligence
o Search Algorithm Terminologies
o Properties of Search Algorithms
o Types of Search Algorithms
 Subsets of Artificial Intelligence
 Implementation of Artificial Intelligence

Why R Programming is Important?
 Why Learn R?
 History of Python
 Features of R
 Applications of R
 Comparison between R and Python
 Which is Better to Choose
 Pros and Cons of R
 Companies using R
 R Packages
 Downloading and Installing R
 What is CRAN?
 Setting R Environment:
o Search Packages in R Environment
o Search Packages in Machine with inbuilt function and
manual searching
o Attach Packages to R Environment
o Install Add-on Packages from CRAN        o Detach Packages from R Environment
o Functions and Packages Help
 R Programming IDE
o RStudio
o Downloading and Installing RStudio
 Variable Assignment
o Displaying Variables
o Deleting Variables
 Comments
o Single Line
o Multi Line Comments
 Data Types
o Logical
o Integer
o Double
o Complex
o Character
 Operators                        Naming List Elements
o Accessing List Elements
o Manipulating List Elements
o Merging Lists
o Converting List to Vector
 Matrix
o Creating a Matrix
o Accessing Elements of a Matrix
o Matrix Manipulations
o Dimensions of Matrix
o Transpose of Matrix
 Data Frames
o Create Data Frame
o Vector to Data Frame
o Character Data Converting into Factors: StringsAsFactors
o Convert the columns of a data frame to characters
o Extract Data from Data Frame
o Expand Data Frame, Column Bind and Row Bind
 Merging / Joining Data Frames
o Inner Join
o Outer Join
o Cross Join
 Arrays
o Create Array with Multiple Dimensions
o Naming Columns and Rows
o Accessing Array Elements
o Manipulating Array Elements
o Calculations across Array Elements
 Factors
o Factors in Data Frame
o Changing the Order of Levels
o Generating Factor Levels
o Deleting Factor Levels
o Arithmetic Operators
o Relational Operators
o Logical Operators
o Assignment Operators
o R as Calculator
o Performing different Calculations
 Functions
o Inbuilt Functions
o User Defined Functions
 STRUCTURES
o Vector
o List
o Matrix
o Data frame
o Array
o Factors
 Inbuilt Constants & Functions
 Vectors
o Vector Creation
o Single Element Vector
o Multiple Element Vector
o Vector Manipulation
o Sub setting & Accessing the Data in Vector
 Lists
o Creating a List

Data Extraction from CSV
o Getting and Setting the Working Directory
o Input as CSV File, Reading a CSV File
o Analyzing the CSV File, Writing into a CSV File
 Data Extraction from URL
 Data Extraction from CLIPBOARD
 Data Extraction from EXCEL                                o Install “xlsx” Package
o Verify and Load the “xlsx” Package, Input as “xlsx” File
o Reading the Excel File, Writing the Excel File
 Data Extraction from DATABASES
o RMySQL Package, Connecting to MySql
o Querying the Tables, Query with Filter Clause
o Updating Rows in the Tables, Inserting Data into the Tables
o Creating Tables in MySql, Dropping Tables in MySql
o Using dplyr and tidyr package 

Data Pre-processing
 Classification Algorithms
o K Nearest Neighbors Classification
o Naive Bayes Classification
o Decision Tree Classification
o Random Forest Classification
o Support Vector Machine Classification
o Logistic Regression
o Kernel SVM
 Regression Algorithms
o Simple Linear Regression
o Multiple Linear Regression
o Polynomial Regression
o Support Vector Regression
o Decision Tree Regression
o Random Forest Regression
 Clustering Algorithms
o K-Means Clustering
o Hierarchical Clustering
 Association Rule Algorithms
o Apriori
o Eclat
 Dimensionality-Reduction
o Principal Component Analysis
o Linear Discriminant Analysis
o Kernal PCA
 Model Selection & Boosting
o Grid Search
o K Fold Cross Validation
o XGBoost
 Natural Language Processing
 Deep Learning – Artificial Neural Networks

Explore Weka Machine Learning Toolkit
o Installation of WEKA
o Features of WEKA Toolkit
o Explore & Load data sets in Weka
 Perform Data Preprocessing Tasks
o Apply Filters on Data Sets
 Performing Classification on Data Sets
o J48 Classification Algorithm
o Decision Trees Algorithm
o K-NN Classification Algorithm
o Naive-Bayes Classification Algorithm
o Comparing Classification Results
 Performing Regression on Data Sets
o Simple Linear Regression Model
o Multi Linear Regression Model
o Logistic Regression Model
o Cross-Validation and Percentage Split
 Performing Clustering on Data Sets
o Clustering Techniques in Weka
o Simple K-means Clustering Algorithm
o Association Rule Mining on Data Sets
o Apriori Association Rule Algorithm
o Discretization in the Rule Generation Process
 Graphical Visualization in Weka
o Visualization Features in Weka
o Visualize the data in various dimensions
o Plot Histogram, Derive Interesting Insights