Artificial Intelligence Online Training in India

Introduction To Python:

• Installation and Working with Python

• Understanding Python variables

• Python basic Operators

• Understanding the Python blocks.

Introduction To Variables:

• Variables, expression condition and function

• Global and Local Variables in Python

• Packing and Unpacking Arguments

• Type Casting in Python

• Byte objects vs. string in Python

• Variable Scope

Python Data Type:

• Declaring and usingNumeric data types

• Using stringdata type and string operations

• Understanding Non-numeric data types

• Understanding the concept of Casting and Boolean.

• Strings

• List

• Tuples

• Dictionary

• Sets

Introduction Keywords and Identifiers and Operators

• Python Keyword and Identifiers

• Python Comments, Multiline Comments.

• Python Indentation

• Understating the concepts of Operators

Data Structure

• List
• What is List.
• List Creation
• List Length
• List Append
• List Insert
• List Remove
• List Append & Extend using “+” and Keyword
• List Delete
• List related Keyword in Python
• List Revers
• List Sorting
• List having Multiple Reference
• String Split to create a List
• List Indexing
• List Slicing
• List count and Looping
• List Comprehension and Nested Comprehension

• Dictionary
• Dict Creation
• Dict Access (Accessing Dict Values)
• Dict Get Method
• Dict Add or Modify Elements
• Dict Copy
• Dict From Keys.
• Dict Items
• Dict Keys (Updating, Removing and Iterating)
• Dict Values
• Dict Comprehension
• Default Dictionaries
• Ordered Dictionaries
• Looping Dictionaries
• Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets, Tuples and Looping Programming

• Sets
• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations
• Frozen Sets

• Tuple
• What is Tuple
• Tuple Creation
• Accessing Elements in Tuple
• Changinga Tuple
• TupleDeletion
• Tuple Count
• Tuple Index
• TupleMembership
• TupleBuilt in Function (Length, Sort)

• Control Flow
• Loops
• Loops and Control Statements (Continue, Break and Pass).
• Looping techniques in Python
• How to use Range function in Loop
• Programs for printing Patterns in Python
• How to use if and else with Loop
• Use of Switch Function in Loop
• Elegant way of Python Iteration
• Generator in Python
• How to use nested IF and Else in Python
• How to use nested Loop in Python
• Use If and Else in for and While Loop
• Examples of Looping with Break and Continue Statements
• How to use IN or NOTkeywordin Python Loop.

Exception and File Handling, Module, Function and Packages

• Python Exception Handling
• Python Errors and Built-in-Exceptions
• Exception handing Try, Except and Finally
• Catching Exceptions in Python
• Catching Specific Exception in Python
• Raising Exception
• Try and Finally

• Python File Handling
• Opening a File
• Python File Modes
• Closing File
• Writing to a File
• Reading from a File
• Renaming and Deleting Files in Python
• Python Directory and File Management
• List Directories and Files
• Making New Directory
• Changing Directory

• Python Function, Modules and Packages
• Python Syntax
• Function Call
• Return Statement
• Write an Empty Function in Python –pass statement.
• Lamda/ Anonymous Function
• *argsand **kwargs
• Help function in Python
• Scope and Life Time of Variable in Python Function
• Nested Loop in Python Function
• Recursive Function and Its Advantage and Disadvantage
• Organizing python codes using functions
• Organizing python projects into modules
• Importing own module as well as external modules
• Understanding Packages
• Programming using functions, modules & external packages
• Map, Filter and Reduce function with Lambda Function
• More example of Python Function

Data Automation (Excel, SQL, PDF etc)

• Python Object Oriented Programming—Oops
• Concept of Class, Object and Instances
• Constructor, Class attributes and Destructors
• Real time use of class in live projects
• Inheritance, Overlapping and Overloading operators
• Adding and retrieving dynamic attributes of classes
• Programming using Oops support

• Python Database Interaction
• SQL Database connection using
• Creating and searching tables
• Reading and Storing configinformation on database
• Programming using database connections

• Reading an excel
• Reading an excel file usingPython
• Writing toan excel sheet using Python
• Python| Reading an excel file
• Python | Writing an excel file
• Adjusting Rows and Column using Python
• ArithmeticOperation in Excel file.
• Plotting Pie Charts
• Plotting Area Charts
• Plotting Bar or Column Charts using Python.
• Plotting Doughnut Chartslusing Python.
• Consolidationof Excel File using Python
• Split of Excel File Using Python.
• Play with Workbook, Sheets and Cells in Excel using Python
• Creating and Removing Sheets
• Formatting the Excel File Data
• More example of Python Function

• Working with PDF and MS Word using Python
• Extracting Text from PDFs
• Creating PDFs
• Copy Pages
• Split PDF
• Combining pages from many PDFs
• Rotating PDF’s Pages

• Complete Understanding of OS Module of Python
• Check Dirs. (exist or not)
• How to split path and extension
• How to get user profile detail
• Get the path of Desktop, Documents, Downloads etc.
• Handle the File System Organization using OS
• How to get any files and folder’s details using OS

Data Analysis & Visualization

• Pandas
• Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
• How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
• Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
• Exploring the Data Plotting, Correlations, and Histograms
• Getting statistical information about the data Analysis Concepts, Handle the None Values
• Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
• Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
• Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
• Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
• Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
• Exporting the results to Excel Joins:
• Python | Pandas Data Frame Inner Join
• Under sting the properties of Data Frame Left Join (Left Outer Join)
• Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
• Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
• Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
• Pandas | How to Group Data How to apply Lambda / Function on Data Frame
• Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

• NumPy
• Introduction to NumPy: Numerical Python
• Importing NumPy and Its Properties
• NumPy Arrays
• Creating an Array from a CSV
• Operations an Array from aCSV
• Operations with NumPy Arrays
• Two-Dimensional Array
• Selecting Elements from 1-D Array
• Selecting Elements from 2-D Array
• Logical Operation with Arrays
• Indexing NumPy elements using conditionals
• NumPy’sMean and Axis
• NumPy’sMode, Median and Sum Function
• NumPy’sSort Function and More

• MatPlotLib
• Bar Chart using Python MatPlotLib
• Column Chart using Python MatPlotLib
• Pie Chart using Python MatPlotLib
• Area Chart using Python MatPlotLib
• Scatter Plot Chart using Python MatPlotLib
• Play with Charts Properties Using MatPlotLib
• Export the Chart as Image
• Understanding plt. subplots () notation
• Legend Alignment of Chart using MatPlotLib
• Create Charts as Image
• Other Useful Properties of Charts.
• Complete Understanding of Histograms
• Plotting Different Charts, Labels, and Labels Alignment etc.

• Introduction to Seaborn
• Introduction to Seaborn
• Making a scatter plot with lists
• Making a count plot with a list
• Using Pandas with seaborn
• Tidy vs Untidy data
• Making a count plot with a Dataframe
• Adding a third variable with hue
• Hue and scattera plots
• Hue and count plots

• Visualizing Two Quantitative Variables
• Introduction to relational plots and subplots
• Creating subplots with col and row
• Customizing scatters plots
• Changing the size of scatter plot points
• Changing the style of scatter plot points
• Introduction to line plots
• Interpreting line plots
• Visualizing standard deviation with line plots
• Plotting subgroups in line plots

• Visualizing a Categorical and a Quantitative Variable
• Current plots and bar plots
• Count plots
• Bar plot with percentages
• Customizing bar plots
• Box plots
• Create and interpret a box plot
• Omitting outliers
• Adjusting the whiskers
• Point plots
• Customizing points plots
• Point plot with subgroups

• Customizing Seaborn Plots
• Changing plot style and colour
• Changing style and palette
• Changing the scale
• Using a custom palette
• Adding titles and labels: Part 1
• Face Grids vs. Axes Subplots
• Adding a title to a face Grid object
• Adding title and labels: Part 2
• Adding a title and axis labels
• Rotating x-tics labels
• Putting it all together
• Box plot with subgroups
• Bar plot with subgroups and subplots

Python - MySQL

• Introduction to MySQL

• What is the MySQLdb

• How do I Install MySQLdb

• Connecting to the MYSQL

• Selecting a database

• Adding data to a table

• Executing multiple queries

• Exporting and Importing data tables.

• SQL Functions
• Single Row Functions
• Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date

General Functions

• Count, Average, Sum, Now etc.

Joining Tables

• Obtaining data from Multiple Tables

• Types of Joins (Inner Join, Left Join, Right Join & Full Join)

• Sub-Queries Vs. Joins

Operators (Data using Group Function)

• Distinct, Order by, Group by, Equal to etc.

Database Objects (Constraints & Views)

• Not Null

• Unique

• Primary Key

• Foreign Key

Structural & Functional Database Testing using TOAD Tool

• SQL Basic
• SQL Introduction
• SQL Syntax
• SQL Select
• SQL Distinct
• SQL Where
• SQL And & Or
• SQL Order By
• SQL Insert
• SQL Update
• SQL Delete

• SQL Advance
• SQL Like
• SQL Wildcards
• SQL In
• SQL Between
• SQL Alias
• SQL Joins
• SQL Inner Join
• SQL Left Join
• SQL Right Join
• SQL Full Join
• SQL Union

• SQL Functions
• SQL Avg()
• SQL Count()
• SQL First()
• SQL Last()
• SQL Max()
• SQL Min()
• SQL Sum()
• SQL Group By

Introduction to Data Science

• What is Analytics & Data Science

• Common Terms in Analytics

• What is data

• Classification of data

• Relevance in industry and need of the hour

• Types of problems and business objectives in various industries

• How leading companies are harnessing the power of analytics

• Critical success drivers

• Overview of analytics tools & their popularity

• Analytics Methodology & problem-solving framework

• List of steps in Analytics projects

• Identify the most appropriate solution design for the given problem statement

• Project plan for Analytics project & key milestones based on effort estimates

• Build Resource plan for analytics project

• Why Python for data science

Accessing/Importing and Exporting Data

• Importing Data from various sources (Csv, txt, excel, access etc)

• Database Input (Connecting to database)

• Viewing Data objects - sub setting, methods

• Exporting Data to various formats

• Important python modules: Pandas

Data Manipulation: Cleansing - Munging Using Python Modules

• Cleansing Data with Python

• Filling missing values using lambda function and concept of Skewness.

• Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.

• Normalizing data

• Feature Engineering

• Feature Selection

• Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.

• Label encoding/one hot encoding

Data Analysis: Visualization Using Python

• Introduction exploratory data analysis

• Descriptive statistics, Frequency Tables and summarization

• Univariate Analysis (Distribution of data & Graphical Analysis)

• Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)

• Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)

• Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

Introduction to Statistics

• Descriptive Statistics

• Sample vs Population Statistics

• Random variables

• Probability distribution functions

• Expected value

• Normal distribution

• Gaussian distribution

• Z-score

• Central limit theorem

• Spread and Dispersion

• Inferential Statistics-Sampling

• Hypothesis testing

• Z-stats vs T-stats

• Type 1 & Type 2 error

• Confidence Interval

• ANOVA Test

• Chi Square Test

• T-test 1-Tail 2-Tail Test

• Correlation and Co-variance

Introduction to Predictive Modelling

• Concept of model in analytics and how it is used

• Common terminology used in Analytics & Modelling process

• Popular Modelling algorithms

• Types of Business problems - Mapping of Techniques

• Different Phases of Predictive Modelling

Data Exploration for Modelling

• Need for structured exploratory data

• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

• Identify missing data

• Identify outliers’ data

• Imbalanced Data Techniques

Data Pre-Processing & Data Mining

• Data Preparation

• Feature Engineering

• Feature Scaling

• Datasets

• Dimensionality Reduction

• Anomaly Detection

• Parameter Estimation

• Data and Knowledge

• Selected Applications in Data Mining

Introduction to Machine Learning

• Artificial Intelligence
• AI overview
• Meaning, scope, and 3 stages of AI
• Decoding AI
• Features of AI
• Applications of AI
• Image recognition
• Effect of AI on society
• AI for industries
• Overview of machine learning
• ML and AI relationship

• Machine Learning

• Techniques of Machine Learning

• Machine Learning Algorithms

• Algorithmic models of Learning

• Applications of Machine Learning

• Large Scale Machine Learning

• Computational Learning theory

• Reinforcement Learning

Supervised Machine Learning

• Supervised Learning
• What is Supervised Learning
• Algorithms in Supervised learning
• Regression & Classification
• Regression vs classification
• Computation of correlation coefficient and Analysis
• Multivariate Linear Regression Theory
• Coefficient of determination (R2) and Adjusted R2
• Model Misspecifications
• Economic meaning of a Regression Model
• Bivariate Analysis
• Naive Bayes classifier, Model Training
• ANOVA (Analysis of Variance)

• Semi-supervised and Reinforcement Learning

• Bias and variance Trade-off

• Representation Learning

Regression

• Regression and its Types

• Logistic Regression

• Linear Regression

• Polynomial Regression

Classification

• Meaning and Types of Classification

• Nearest Neighbor Classifiers

• K-nearest Neighbors

• Probability and Bayes Theorem

• Support Vector Machines

• Naive Bayes

• Decision Tree Classifier

• Random Forest Classifier

Unsupervised Learning: Clustering

• About Clustering

• Clustering Algorithms

• K-means Clustering

• Hierarchical Clustering

• Distribution Clustering

Model optimization and Boosting

• Ensemble approach

• K-fold cross validation

• Grid search cross validation

• Ada boost and XG Boost

Introduction to Deep Learning

• What are the Limitations of Machine Learning

• What is Deep Learning

• Advantage of Deep Learning over Machine learning

• Reasons to go for Deep Learning

• Real-Life use cases of Deep Learning

Deep Learning Networks

• What is Deep Learning Networks

• Why Deep Learning Networks

• How Deep Learning Works

• Feature Extraction

• Working of Deep Network

• Training using Backpropagation

• Variants of Gradient Descent

• Types of Deep Networks

• Feed forward neural networks (FNN)

• Convolutional neural networks (CNN)

• Recurrent Neural networks (RNN)

• Generative Adversal Neural Networks (GAN)

• Restrict Boltzman Machine (RBM)

Deep Learning with Keras

• Define Keras

• How to compose Models in Keras

• Sequential Composition

• Functional Composition

• Predefined Neural Network Layers

• What is Batch Normalization

• Saving and Loading a model with Keras

• Customizing the Training Process

• Intuitively building networks with Keras

Convolutional Neural Networks (CNN)

• Introduction to Convolutional Neural Networks

• CNN Applications

• Architecture of a Convolutional Neural Network

• Convolution and Pooling layers in a CNN

• Understanding and Visualizing CNN

• Transfer Learning and Fine-tuning Convolutional Neural Networks

Recurrent Neural Network (RNN)

• Intro to RNN Model

• Application use cases of RNN

• Modelling sequences

• Training RNNs with Backpropagation

• Long Short-Term Memory (LSTM)

• Recursive Neural Tensor Network Theory

• Recurrent Neural Network Model

• Time Series Forecasting

Natural Language Processing

• NLP with python

• Bags of words

• Stemming

• Tokenization

• Lemmatization

• TF-IDF

• Sentiment Analysis

• Overview of Tensor Flow

What is Tensor Flow

• Tensor Flow code-basics

• Graph Visualization

• Constants, Placeholders, Variables

• Tensor flow Basic Operations

• Linear Regression with Tensor Flow

• Logistic Regression with Tensor Flow

• K Nearest Neighbor algorithm with Tensor Flow

• K-Means classifier with Tensor Flow

• Random Forest classifier with Tensor Flow

Neural Networks Using Tensor Flow

• Quick recap of Neural Networks

• Activation Functions, hidden layers, hidden units

• Illustrate & Training a Perceptron

• Important Parameters of Perceptron

• Understand limitations of A Single Layer Perceptron

• Illustrate Multi-Layer Perceptron

• Back-propagation – Learning Algorithm

• Understand Back-propagation – Using Neural Network Example

• TensorBoard

Introduction to Big Data Hadoop and Spark

• What is Big Data

• Big Data Customer Scenarios

• Understanding BIG Data: Summary

• Few Examples of BIG Data

• Why BIG data is a BUZZ

• How Hadoop Solves the Big Data Problem

• What is Hadoop

• Hadoop’s Key Characteristics

• Hadoop Cluster and its Architecture

• Hadoop: Different Cluster Modes

• Why Spark is needed

• What is Spark

• How Spark differs from other frameworks

• Spark at Yahoo!

BIG Data Analytics and why it’s a Need Now

• What is BIG data Analytics

• Why BIG Data Analytics is a ‘need’ now

• BIG Data: The Solution

• Implementing BIG Data Analytics – Different Approaches

Traditional Analytics vs. BIG Data Analytics

• The Traditional Approach: Business Requirement Drives Solution Design

• The BIG Data Approach: Information Sources drive Creative Discovery

• Traditional and BIG Data Approaches

• BIG Data Complements Traditional Enterprise Data Warehouse

• Traditional Analytics Platform v/s BIG Data Analytics Platform

Big Data Technologies

• Scala
• What is Scala
• Scala in other Frameworks
• Introduction to Scala REPL
• Basic Scala Operations
• Variable Types in Scala
• Control Structures in Scala
• Understanding the constructor overloading,
• Various abstract classes
• The hierarchy types in Scala,
• For-each loop, Functions and Procedures
• Collections in Scala- Array

• Spark
• Overview to Spark
• Spark installation, Spark configuration,
• Spark Components & its Architecture
• Spark Deployment Modes
• Limitations of Map Reduce in Hadoop
• Working with RDDs in Spark
• Introduction to Spark Shell
• Deploying Spark without Hadoop
• Parallel Processing
• Spark MLLib - Modelling Big Data with Spark

Apache Kafka and Flume

• What is Kafka Why Kafka

• Configuring Kafka Cluster

• Kafka architecture

• Producing and consuming messages

• Operations, Kafka monitoring tool

• Need of Apache Flume

• What is Apache Flume

• Understanding the architecture of Flume

• Basic Flume Architecture