INTRODUCTION TO DATA SCIENCE

• 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

INTRODUCTION TO MACHINE LEARINING

• 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

CORE PYTHON PROGRAMMING

• 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
• Interactive Mode Programming
• 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
• Arithmetic Operators
• Comparison (Relational) Operators
• Assignment Operators
• Logical Operators
• Bitwise Operators
• Membership Operators
• Identity Operators
Slicing & Indexing
• Forward Direction Slicing with +ve
Step
• Backward Direction Slicing with -ve
Step
Decision Making Statements
• if Statement
• if-else Statement
• elif Statement
Looping Statements
• Why we use Loops in python?
• Advantages of Loops
• for Loop
• Nested for Loop
• Using else Statement with for Loop
• while Loop
• Infinite while Loop
• Using else with Python while Loop
Conditional Statements
• break Statement
• continue Statement
• Pass Statement

ADVANCED PYTHON PROGRAMMING
Advanced Data Types: List, Tuple,
Set, Frozenset, Dictionary,Range,
Bytes & Bytearray, None
List Data Structure
• List indexing and splitting
• Updating List values
• List Operations
• Iterating a List
• Adding Elements to the List
• Removing Elements from the List
• List Built-in Functions
• List Built-in Methods
Tuple Data Structure
• Tuple Indexing and Splitting
• Tuple Operations
• Tuple Inbuilt Functions
• Where use Tuple
• List Vs Tuple
• Nesting List and Tuple
Set Data Structure
• Creating a Set
• Set Operations
• Adding Items to the Set
• Removing Items from the Set
• Difference Between discard() and remove()
• Union of Two Sets
• Intersection of Two Sets
• Difference of Two Sets
• Set Comparisons
Frozenset Data Structure
Dictionary Data Structure
• Creating the Dictionary
• Accessing the Dictionary Values
• Updating Dictionary Values
• Deleting Elements Using del Keyword
• Iterating Dictionary
• Properties of Dictionary Keys
• Built-in Dictionary Functions
• Built-in Dictionary Methods
List Vs Tuple Vs Set Vs Frozenset Vs Dict
Range, Bytes, Bytearray & None
Python Functions
• Advantage of Functions in Python
• Creating a Function
• Function Calling
• Parameters in Function
• Call by Reference in Python
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
• Strings Indexing and Splitting
• String Operators
• Python Formatting Operator
• Built-in String Functions
Python File Handling
• Opening a File
• Reading the File
• Read Lines of the File
• Looping through the File
• Writing the File
• Creating a New File
• Using with Statement with Files
• File Pointer Positions
• Modifying File Pointer Position
• Renaming the File & Removing the File
• Writing Python Output to the Files
• File Related Methods
Python Exceptions
• Common Exceptions
• Problem without Handling Exceptions
• except Statement with no Exception
• Declaring Multiple Exceptions
• Finally Block
• Raising Exceptions
• Custom Exception
Python Packages
• Python Libraries
• Python Modules
• Collection Module
• Math Module
• OS Module
• Random Module
• Statistics Module
• Sys Module
• Date & Time Module

Loading the Module in our Python Code
• import Statement
• from-import Statement
• Renaming a Module
• Regular Expressions
• Command Line Arguments
Object Oriented Programming (OOPs)
• Object-oriented vs Procedure-oriented
Programming languages
• Object
• Class
• Method
• Inheritance
• Polymorphism
• Data Abstraction
• Encapsulation
Python Class and Objects
• Creating Classes in Python
• Creating an Instance of the Class
Python Constructor
• Creating the Constructor in Python
• Parameterized Constructor
• Non-Parameterized Constructor
• In-built Class Functions
• In-built Class Attributes
Python Inheritance
• Python Multi-Level Inheritance
• Python Multiple Inheritance
• Method Overriding
• Data Abstraction in Python
• Graphical User Interface (GUI) Programming
• Python Tkinter
• Tkinter Geometry
• pack() Method
• grid() Method
• place() Method
• Tkinter Widgets
DATA ANALYSIS WITH PYTHON NUMPY
NumPy Introduction
• What is NumPy
• The Need of NumPy
• NumPy Environment Setup
N-Dimensional Array (Ndarray)
• Creating a Ndarray Object
• Finding the Dimensions of the Array
• Finding the Size of Each Array Element
• Finding the Data Type of Each Array Item
• Finding the Shape and Size of the Array
• Reshaping the Array Objects
• Slicing in the Array
• Finding the Maximum,Minimum, and Sum of
the ArrayElements
• NumPy Array Axis
• Finding Square Root and Standard
Deviation
• Arithmetic Operations on the Array
• Array Concatenation
NumPy Datatypes
• NumPy dtype
• Creating a Structured Data Type
Numpy Array Creation
• Numpy.empty
• Numpy.Zeros
• NumPy.ones
Numpy Array from Existing Data
• Numpy.asarray
Numpy Arrays within the Numerical Range
• Numpy.arrange
• NumPy.linspace
• Numpy.logspace
• NumPy Broadcastingo Broadcasting Rules
NumPy Array Iteration
• Order of Iteration
• F-Style Order
• C-Style Order
• Array Values Modification
NumPy String Functions
NumPy Mathematical Functions
• Trigonometric Functions
• Rounding Functions

NumPy Statistical functions
• Finding the Min & Max Elements from the Array
• 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
DATA ANALYSIS WITH PYTHON PANDAS
Pandas Introduction & Pandas Environment
Setup
• Key Features of Pandas
• Benefits of Pandas
• Python Pandas Data Structure
• Series
• DataFrame
• Panel
Pandas Series
• Creating a Series
• Create an Empty Series
• Create a Series using Inputs
• Accessing Data from Series with Position
• Series Object Attributes
• Retrieving Index Array and Data Array of a
Series Object
• Retrieving Types(dtype)andSize of Type(itemsize)
• Retrieving Shape
• Retrieving Dimension,Size&Number of Bytes
• Checking Emptiness and Presence of NaNs
• Series Functions
Pandas DataFrame
• 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
• Datetime
• Time Offset
• Time Periods
• Convert String to Date
Viewing/Inspecting Data (loc & iloc)
Data Cleaning
Filter, Sort, and Groupby
Statistics on DataFrame
Pandas Vs NumPy
DataFrame Plotting
• Line: Line Plot (Default)
• Bar: Vertical Bar Plot
• Barh: Horizontal Bar Plot
• Hist: Histogram Plot
• Box: Box Plot
• Pie: Pie Chart
• Scatter: Scatter Plot
DBMS – Structured Query Language
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
Importing & Exporting Data
• 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_csv)
Writing into EXCEL (df.to_excel)
Data Extraction from DATABASES
• Python MySQL Database Connection
• Import mysql.connector Module
• Create the Connection Object
• Create the Cursor Object
• Execute the Query

DATA VISUALIZATION WITH PYTHON
MATPLOTLIB
• 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
• What is a Boxplot?
• Mean, Median, Quartiles, Outliers
• Inter Quartile Range (IQR),Whiskers
• Data Distribution Analysis
• Boxplot on a Normal Distribution
• Probability Density Function
• 68–95–99.7 Rule (Empirical rule)
Data Analysis Project using Python
Programming
MACHINE LEARNING
• 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
• Gathering Data
• Data preparation
• Data Wrangling
• Analyze Data
• Train the model
• Test the model
• Deployment
• Features of Machine Learning
• History of Machine Learning
• Applications of Machine Learning
Types of Machine Learning
• Supervised Machine Learning
• Unsupervised Machine Learning
• Reinforcement Learning
Supervised Machine Learning
• How Supervised Learning Works?
• Steps Involved in Supervised Learning
• Types of supervised Machine Learning
Algorithms
• Classification
• Regression
• Advantages of Supervised Learning
• Disadvantages of Supervised Learning
Unsupervised Machine Learning
• How Unsupervised Learning Works?
• Why use Unsupervised Learning?
• Types of Unsupervised Learning Algorithm
• Clustering
• Association
• Advantages of Unsupervised Learning
• Disadvantages of Unsupervised Learning
• Supervised Vs Unsupervised Learning
Reinforcement Machine Learning
How to get Datasets for Machine Learning?
• What is a Dataset?
• Types of Data in Datasets
• Popular Sources for Machine Learning Datasets

Data Preprocessing in Machine Learning
Why do we need Data Preprocessing?
• Getting the Dataset
• Importing Libraries
• Importing Datasets
• Finding Missing Data
• By Deleting the Particular Row
• By Calculating the Mean
• Encoding Categorical Data
• LableEncoder
• OneHotEncoder
• Splitting Dataset into Training and
Test Set
• Feature Scaling
• Standardization
• Normalization
Classification Algorithms in Machine Learning
What is the Classification Algorithm?
Types of Classifications
• Binary Classifier
• Multi-class Classifier
• Learners in Classification Problems
• Lazy Learners
• Eager Learners
Types of ML Classification Algorithms
Linear Models
• Logistic Regression
• Support Vector Machines
Non-linear Models
• K-Nearest Neighbors
• Naïve Bayes
• Decision Tree Classification
• Random Forest Classification
• Kernel SVM
Evaluating a Classification Model
• 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 ScikitLearn
• Confusion Matrix for MultiClass Classification
• Log Loss or Cross-Entropy Loss
• AUC-ROC curve
Use cases of Classification Algorithms
K-Nearest Neighbor(KNN) Algorithm in Machine
Learning
• Why do we Need a K-NN Algorithm?
• How does K-NN work?
• What is Euclidean Distance
• 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
• Data Pre-processing Step
• Fitting the K-NN algorithm to the
Training Set
• Predicting the Test Result
• Test Accuracy of the Result
(Creation of Confusion Matrix)
• Visualizing the Test Set Result.
• Improve the Performance of the KNN Mode

Naïve Bayes Classifier Algorithm in Machine
Learning
Why is it Called Naïve Bayes?
• Naïve Means?
• Bayes Means?
Bayes’ Theorem
• Posterior Probability
• Likelihood Probability
• Prior Probability
• 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
• Gaussian Naïve Bayes Classifier
• Multinomial Naïve Bayes Classifier
• Bernoulli Naïve Bayes Classifier
• Python Implementation of the Naïve Bayes
Algorithm
Steps to Implement the Naïve Bayes Algorithm
• Data Pre-processing Step
• Fitting Naive Bayes to the Training set
• Predicting the Test Result
• Test Accuracy of the Result
(Creation of Confusion matrix)
• Visualizing the Test Set Result
• Improve the Performance of the Naïve Bayes Model
Decision Tree Classification Algorithm in
Machine Learning
• Why use Decision Trees?
Types of Decision Trees
• Categorical Variable Decision Tree
• Continuous Variable Decision Tree
Decision Tree Terminologies
How does the Decision Tree Algorithm Work?
Attribute Selection Measures
• Entropy
• Information Gain
• Gini index
• Gain Ratio
• Algorithms used in Decision Tree
• ID3 Algorithm → (Extension of D3)
• C4.5 Algorithm→ (Successor of ID3)
• CART Algorithm → (Classification & Regression Tree)
How to Avoid/Counter Overfitting in Decision
Trees?
• Pruning Decision Trees
• 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
• Data Pre-processing Step
• Fitting a Decision-Tree Algorithm to
the Training Set
• Predicting the Test Result
• Test Accuracy of the Result
(Creation of Confusion matrix)
• Visualizing the Test Set Result
• Improve the Performance of the
Decision Tree Model
Random Forest Classifier Algorithm in Machine
Learning
• Working of the Random Forest Algorithm
• Assumptions for Random Forest
• Why use Random Forest?
How does Random Forest Algorithm Work?
• Ensemble Techniques
• Bagging (Bootstrap Aggregation)
• Applications of Random Forest
• Disadvantages of Random Forest
• Python Implementation of Random Forest
Algorithm
Steps to Implement the Random Forest
Algorith:
• Data Pre-processing Step
• Fitting the Random Forest Algorithm
to the Training Set
• Predicting the Test Result
• Test Accuracy of the Result
(Creation of Confusion Matrix)
• Visualizing the Test Set Result
• Improving the Performance of the
Random Forest Model

Logistic Regression Algorithm in Machine Learning
• Logistic Function (Sigmoid Function)
• Assumptions for Logistic Regression
• Logistic Regression Equation
Type of Logistic Regression
• Binomial Logistic Regression
• Multinomial Logistic Regression
• Ordinal Logistic Regression
• Python Implementation of Logistic Regression
(Binomial)
Steps to Implement the Logistic Regression:
• Data Pre-processing Step
• Fitting Logistic Regression to the
Training Set
• Predicting the Test Result
• Test Accuracy of the Result
(Creation of Confusion Matrix)
• Visualizing the Test Set Result
• Improve the Performance of the
Logistic Regression Model
Support Vector Machine Algorithm
• Types of Support Vector Machines
• Linear Support Vector Machine
• Non-Linear Support Vector Machine
• Hyperplane in the SVM Algorithm
• Support Vectors in the SVM Algorithm
How does SVM Works?
• How does Linear SVM Works?
• How does Non-Linear SVM Works?
• Python Implementation of Support Vector
Machine
Steps to Implement the Support Vector
Machine:
• Data Pre-processing Step
• Fitting Support Vector Machine to
the Training Set
• Predicting the Test Result
• Test Accuracy of the Result
(Creation of Confusion Matrix)
• Visualizing the Test Set Result
• Improve the Performance
of the Support Vector MachineModel
Regression Algorithms in Machine Learning
• Terminologies Related to the Regression
Analysis
• Dependent Variable
• Independent Variable
• Outliers
• Multi-collinearity
• Under fitting and Overfitting
Why do we use Regression Analysis?
Types of Regression
• Linear Regression
• Logistic Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Ridge Regression
• Lasso Regression
Linear Regression in Machine Learning
Types of Linear Regression
• Simple Linear Regression
• Multiple Linear Regression
Linear Regression Line
• Positive Linear Relationship
• Negative Linear Relationship
Finding the Best Fit Line
• Cost Function
• Gradient Descent
• Model Performance
• R-Squared Method
Assumptions of Linear Regression
Simple Linear Regression in Machine Learning
• SLR Model
• Implementation of Simple Linear Regression
Algorithm using Python
• Data Pre-processing Step
• Fitting Simple Linear Regression
to the Training Set
• Predicting the Test Result
• Test Accuracy of the
• Visualizing the Test Set Result.
• Try to Improve the Performance of the
Model

Multiple Linear Regression in Machine Learning
• MLR Equation
• Assumptions for Multiple Linear Regression
Implementation of Multiple Linear Regression
model using Python
• Data Pre-processing Step
• Fitting Multiple Linear Regression to the Training Set
• Predicting the Test Result
• Test Accuracy of the
• Visualizing the Test Set Result.
• Try to Improve the Performance of the Model
Backward Elimination
• What is Backward Elimination?
• Steps of Backward Elimination
• Need for Backward
Elimination: An optimal
Multiple LinearRegression
model
• Implement the Steps for Backward
Elimination method
Polynomial Regression in Machine Learning
• Need for Polynomial Regression
• Equation of the Polynomial Regression Model
• Implementation of Polynomial Regression
using Python
Steps for Polynomial Regression:
• Data Pre-processing
• Build a Linear Regression Model
• Build a Polynomial Regression Model
• Visualize the Result for
Linear Regression Model
• Visualize the Result for
Polynomial Regression Model
• Predicting the Final Result with the
Linear Regression Model
• Predicting the Final Result with the
Polynomial RegressionModel
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression
• Ridge Regression
• Lasso Regression
• Linear Regression Vs Logistic Regression
• Classification vs Regression
Clustering Algorithms in Machine Learning
Types of Clustering Methods
• Partitioning Clustering
• Density-Based Clustering
• Distribution Model-Based Clustering
• Hierarchical Clustering
• Fuzzy Clustering
Clustering Algorithms
• K-Means Algorithm
• Mean-shift Algorithm
• DBSCAN Algorithm
• Expectation-Maximization Clustering using GMM
• Agglomerative Hierarchical Algorithm
• Affinity Propagation
• Applications of Clustering
Hierarchical Clustering Algorithm in Machine
Learning
• Hierarchical Clustering Technique Approaches
• Why Hierarchical Clustering?
• Agglomerative Hierarchical Clustering
• How the Agglomerative Hierarchical
Clustering Work?
Measure for the Distance between two
Clusters
• Single Linkage
• Complete Linkage
• Average Linkage
• 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
HierarchicalClustering using Python
• Data Pre-processing
• Finding the Optimal Number of Clusters using
the Dendrogram
• Training the Hierarchical Clustering Model
• Visualizing the Clusters

K-Means Clustering Algorithm in Machine Learning
• 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?
• Elbow Method
• 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
• Data Pre-processing
• Finding the Optimal Number of Clusters
using the ElbowMethod
• Training the K-means Algorithm on the Training Dataset
• Visualizing the Clusters
Association Rules in Machine Learning
• Association Rules
• Pattern Detection
• Market Basket Analysis
• Support, Confidence, Expected Confidence, Lift
• Finding Item Sets with High Support
• Finding Item Rules with High Confidence or Lift
Apriori Algorithm in Machine Learning
• Apriori Algorithm
• How does Apriori Algorithm Works?
• Apriori Algorithm Example
• Implementation of Apriori Algorithm using Python
• Limitations of Apriori Algorithm
Dimensionality Reduction & Model Selection Boosting
Dimensionality Reduction
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA
• Model Selection Boosting
• Model Selection
• Grid Search
• K-Fold Cross Validation
• XGBoost
STATISTICS
• 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 (Empiricalrule)
• 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
Natural Language Processing Introduction
• What is NLP?
• History of NLP
• Advantages of NLP
• Disadvantages of NLP
Components of NLP
• Natural Language Understanding
(NLU)
• Natural Language Generation (NLG)
• Difference between NLU and NLG
• Applications of NLP
• How to build an NLP Pipeline?
Phases of NLP
• Lexical Analysis and Morphological
• Syntactic Analysis (Parsing)
• Semantic Analysis
• Discourse Integration
• Pragmatic Analysis
• Why NLP is Difficult?
• NLP APIs
• NLP Libraries
• Natural Language Vs Computer Language
Exploring Features of NLTK
• Open the Text File for Processing
• Import Required Libraries
• Sentence Tokenizing
• Word Tokenizing
• Find the Frequency Distribution
• Plot the Frequency Graph
• Remove Punctuation Marks
• Plotting Graph without Punctuation Marks
• List of Stopwords
• Removing Stopwords
• Final Frequency Distribution

Word Cloud
• Word Cloud Properties
• Python Code Implementation of the Word Cloud
• Word Cloud with the Circle Shape
• Word Cloud Advantages
• Word Cloud Disadvantages
Stemming
• Stemmer Examples
• Stemming Algorithms
• Porter’s Stemmer
• Lovin’s Stemmer
• Dawson’s Stemmer
• Krovetz Stemmer
• Xerox Stemmer
• Snowball Stemmer
Lemmatization
• Difference between Stemmer and Lemmatizer
• Demonstrating how a lemmatizer works
• Lemmatizer with default PoS value
• Demonstrating the power of lemmatizer
• Lemmatizer with different POS values
Part-of-Speech (PoS) Tagging
• Why do we need Part of Speech (POS)?
• Part of Speech (PoS) Tags
Chunking
• Categories of Phrases
• Phrase Structure Rules
• Chinking
Named Entity Recognition (NER)
• Use-Cases
• Commonly used Types of Named Entity
• WordNet
Bag of Words
• What is the Bag-of-Words method?
• Creating a basic Structure on Sentences
• Words with Frequencies
• Combining all the Words
• Final Model of our Bag of Words
• Applications & Limitations
TF-IDF
• Term Frequency
• Inverse Document Frequency
• Term Frequency – Inverse
Document Frequency
Deploying a Machine Learning Model on a Web
using Flask
• 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 Model
DEEP LEARNING INTRODUCTION
• What is Deep Learning?
• Deep learning Process
Types of Deep Learning Networks
• Deep Neural Networks
• Artificial Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
TensorFlow
• History of TensorFlow
• Components of TensorFlow
• Use Cases/Applications of TensorFlow
• 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

ARTIFICIAL INTELLIGENCE
INTRODUCTION
What is Artificial Intelligence?
• Why Artificial Intelligence?
• Goals of Artificial Intelligence
• What Comprises to Artificial Intelligence?
• Advantages of Artificial Intelligence
• Disadvantages of Artificial Intelligence
• Applications of Artificial Intelligence
• History of Artificial Intelligence
• Types of Artificial Intelligence
Types of AI Agents
• Simple Reflex Agent
• Model-Based Reflex Agent
• Goal-Based Agents
• Utility-Based Agent
• Learning Agent
Search Algorithms in Artificial Intelligence
• Search Algorithm Terminologies
• Properties of Search Algorithms
• Types of Search Algorithms
• Subsets of Artificial Intelligence
• Implementation of Artificial Intelligence
R PROGRAMMING
• 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:
• Search Packages in R Environment
• Search Packages in Machine with inbuilt
function and manual searching
• Attach Packages to R Environment
• Install Add-on Packages from CRAN
• Detach Packages from R Environment
• Functions and Packages Help
R Programming IDE
• RStudio
• Downloading and Installing RStudio
Variable Assignment
• Displaying Variables
• Deleting Variables
Comments
• Single Line
• Multi Line Comments
Data Types
• Logical
• Integer
• Double
• Complex
• Character
Operators
• Arithmetic Operators
• Relational Operators
• Logical Operators
• Assignment Operators
• R as Calculator
• Performing different Calculations
Functions
• Inbuilt Functions
• User Defined Functions
STRUCTURES
• Vector
• List
• Matrix
• Data frame
• Array
• Factors
Inbuilt Constants & Functions
Vectors
• Vector Creation
• Single Element Vector
• Multiple Element Vector
• Vector Manipulation
• Sub setting & Accessing the Data in
Vector

Lists
• Creating a List
• Naming List Elements
• Accessing List Elements
• Manipulating List Elements
• Merging Lists
• Converting List to Vector
Matrix
• Creating a Matrix
• Accessing Elements of a Matrix
• Matrix Manipulations
• Dimensions of Matrix
• Transpose of Matrix
Data Frames
• Create Data Frame
• Vector to Data Frame
• Character Data Converting into
Factors: StringsAsFactors
• Convert the columns of a data frame to characters
• Extract Data from Data Frame
• Expand Data Frame, Column Bind
and Row Bind
Merging / Joining Data Frames
• Inner Join
• Outer Join
• Cross Join
Arrays
• Create Array with Multiple Dimensions
• Naming Columns and Rows
• Accessing Array Elements
• Manipulating Array Elements
• Calculations across Array Elements
Factors
• Factors in Data Frame
• Changing the Order of Levels
• Generating Factor Levels
• Deleting Factor Levels
Loading and Reading Data in R
• Data Extraction from CSV
• Getting and Setting the Working Directory
• Input as CSV File, Reading a CSV File
• Analyzing the CSV File, Writing into a CSV File
• Data Extraction from URL
• Data Extraction from CLIPBOARD
Data Extraction from EXCEL
• Install “xlsx” Package
• Verify and Load the “xlsx” Package,
Input as “xlsx” File
• Reading the Excel File, Writing the
Excel File
Data Extraction from DATABASES
• RMySQL Package, Connecting to
MySql
• Querying the Tables, Query with
Filter Clause
• Updating Rows in the Tables,
Inserting Data into the Tables
• Creating Tables in MySql, Dropping
Tables in MySql
• Using dplyr and tidyr package
Machine Learning using R
• Data Pre-processing
Classification Algorithms
• K Nearest Neighbors Classification
• Naive Bayes Classification
• Decision Tree Classification
• Random Forest Classification
• Support Vector Machine Classification
• Logistic Regression
• Kernel SVM
Regression Algorithms
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
Clustering Algorithms
• K-Means Clustering
• Hierarchical Clustering
Association Rule Algorithms
• Apriori
• Eclat

Dimensionality-Reduction
• Principal Component Analysis
• Linear Discriminant Analysis
• Kernal PCA
Model Selection & Boosting
• Grid Search
• K Fold Cross Validation
• XGBoost
• Natural Language Processing
• Deep Learning – Artificial Neural Networks
DATA MINING WEKA
Explore Weka Machine Learning Toolkit
• Installation of WEKA
• Features of WEKA Toolkit
• Explore & Load data sets in Weka
Perform Data Preprocessing Tasks
• Apply Filters on Data Sets
• Performing Classification on Data Sets
• J48 Classification Algorithm
• Decision Trees Algorithm
• K-NN Classification Algorithm
• Naive-Bayes Classification Algorithm
• Comparing Classification Results
Performing Regression on Data Sets
• Simple Linear Regression Model
• Multi Linear Regression Model
• Logistic Regression Model
• Cross-Validation and Percentage Split
Performing Clustering on Data Sets
• Clustering Techniques in Weka
• Simple K-means Clustering Algorithm
• Association Rule Mining on Data Sets
• Apriori Association Rule Algorithm
• Discretization in the Rule Generation Process
Graphical Visualization in Weka
• Visualization Features in Weka
• Visualize the data in various dimensions
• Plot Histogram, Derive Interesting Insight