Data Science Training

APEC Provides Best Data Science Course in Various Fields of Education like for both IT and Non IT Students and Professionals .

Data Science Training

APEC Provides Best Data Science Course in Various Fields of Education like for both IT and Non IT Students and Professionals .

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Embarking on a journey into the dynamic field of data science is an enriching experience, and APEC in Hyderabad stands as a distinguished institution for comprehensive data science training. Our program is meticulously crafted to equip participants with the skills and knowledge needed to excel in the data-driven world. Covering a spectrum of topics, including statistical analysis, machine learning, and data visualization, our expert instructors guide you through the intricacies of data science methodologies. APEC’s data science training in Hyderabad goes beyond theoretical concepts, emphasizing practical applications and hands-on projects to reinforce your learning.



• 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
• 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
• Backward Direction Slicing with -ve
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 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
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
• 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
Pandas Introduction & Pandas Environment
• 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
• Sub Queries & Key Constraints
• Aggregative Functions, Clauses & Views
Importing & Exporting Data
• Data Extraction from CSV (pd.read_csv)
• Data Extraction from TEXT File
• Data Extraction from 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 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
• What is Machine Learning
• Importance of Machine Learning
• Need for Machine Learning
• Statistics Vs Machine Learning
• Traditional Programming Vs Machine
• 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
• 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
• 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
• 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
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
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
• 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
• 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
• 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
Steps to Implement the Random Forest
• 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
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
Steps to Implement the Support Vector
• 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
• 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

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
• 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
• Hierarchical Clustering Technique Approaches
• Why Hierarchical Clustering?
• Agglomerative Hierarchical Clustering
• How the Agglomerative Hierarchical
Clustering Work?
Measure for the Distance between two
• Single Linkage
• Complete Linkage
• Average Linkage
• Centroid Linkage
• Working of Dendrogram in Hierarchical
• Hierarchical Clustering Example with Scratch
• 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
• 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
• Mean, Median and Mode
• Data Variability, Range, Quartiles
• IQR, Calculating Percentiles
• Variance, Standard Deviation, Statistical
• Types of Distributions – Normal, Binomial,
• 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,
• Measure of Distance, Euclidean Distance
• Probability Calculation – Independent &
• Entropy, Information Gain
• Regression
Natural Language Processing Introduction
• What is NLP?
• History of NLP
• Advantages of NLP
• Disadvantages of NLP
Components of NLP
• Natural Language Understanding
• 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 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
• Stemmer Examples
• Stemming Algorithms
• Porter’s Stemmer
• Lovin’s Stemmer
• Dawson’s Stemmer
• Krovetz Stemmer
• Xerox Stemmer
• Snowball Stemmer
• 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
• 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
• 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
• What is Deep Learning?
• Deep learning Process
Types of Deep Learning Networks
• Deep Neural Networks
• Artificial Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• 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

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
• 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
• Single Line
• Multi Line Comments
Data Types
• Logical
• Integer
• Double
• Complex
• Character
• Arithmetic Operators
• Relational Operators
• Logical Operators
• Assignment Operators
• R as Calculator
• Performing different Calculations
• Inbuilt Functions
• User Defined Functions
• Vector
• List
• Matrix
• Data frame
• Array
• Factors
Inbuilt Constants & Functions
• Vector Creation
• Single Element Vector
• Multiple Element Vector
• Vector Manipulation
• Sub setting & Accessing the Data in

• Creating a List
• Naming List Elements
• Accessing List Elements
• Manipulating List Elements
• Merging Lists
• Converting List to Vector
• 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
• Create Array with Multiple Dimensions
• Naming Columns and Rows
• Accessing Array Elements
• Manipulating Array Elements
• Calculations across Array Elements
• 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
• 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

• 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
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


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IT Training courses take a maximum of 2-3 month durations & the course duration, however, depends on the availability and convenience of the learner. You can check our detailed upcoming batches, course duration & timing, fees at the course training page.

Yes, after the completion of the course. We will provide opportunity to work on our live projects.

We provide a proper certificate after completion of the course. Along with our institute certificate you will be also able to write Course Name and other certifications after completing the course.

You can expect a 10%-25% increment in salary. The overall growth depends upon how you perform and how you are beneficial to the organization.

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