|Instructor Led Training||55 Hours|
|Exercises & Project Work||80 Hours|
|Self Paced Video||70 Hours|
|MARCH 8th||Sat & Sun 8 PM IST (GMT +5:30)||ENROLL NOW|
|MARCH 14th||Sat & Sun 8 PM IST (GMT +5:30)||ENROLL NOW|
|MARCH 21st||Sat & Sun 8 PM IST (GMT +5:30)||ENROLL NOW|
|Section 1: Introduction|
|Introduction to Software Training||FREE||00:40:00|
|Object Oriented Design Patterns||00:35:00|
|Section 2: Advanced Computing|
|Introduction to Azure Data factory||FREE||00:25:00|
|Multi Threading in Softwares||00:40:00|
|Managing Software Testing||00:20:00|
|The Software Quiz||00:04:00|
|Introduction to Speech||FREE||00:40:00|
Artificial Intelligence and Machine Learning is taking over every other industry. From small companies to big tech-giants, all are implementing AI and ML to grow in their respective fields. On one hand, where AI and ML are so in demand, there is a shortage of skilled Artificial Intelligence Engineer and Machine Learning Engineer. Artificial Intelligence and Deep Learning Training Certification course by Eduranz is designed and structured by industry experts based on industry requirements and demands. This training program will help you master Python and its libraries for Artificial Intelligence, TensorFlow, and Keras. As part of this project-based training program, you will learn about Time Series Analysis, Predictive Analytics, Graphical Models, Reinforcement Learning, Convolutional Neural Networks, Recurrent Neural Networks, and many more. As part of Eduranz’s training program, you will get to work on real-time projects and assignments, which are also developed keeping in mind their implications in the real-world industry. The training program ends with tests, there will be a quiz that will perfectly reflect the type of questions asked in the job interviews, thus helping you score better marks.
Professionals who want to build a career in AI and Deep Learning Students aspiring to become AI Engineer and Deep Learning Engineer
The pre-requisites for Eduranz’s Artificial Intelligence course with Deep Learning are:
•Basic knowledge of programming and mathematics are beneficial
Artificial Intelligence Certification with Deep Learning has been designed and curated by industry professionals that prepare you for the industry that demands skilled Artificial Intelligence Engineer. As part of Eduranz’s Artificial Intelligence Course training program, you will get to work on real-time projects and assignments, which are also developed keeping in mind their implications in the real-world industry. Upon completion of the project work, which will be reviewed by a panel of industry experts, and upon scoring at least 60% marks in the quiz, you will be awarded the Artificial Intelligence and Deep Learning Training Certification by Eduranz.
In this chapter we will get an overview of all the important concepts of Python, its libraries and applications, to help us kick start the AI training course. Here’s the table of content for this chapter.
- Basics of Python
- OOPs Concept in Python
- Introduction to NumPy
- Introduction to Pandas
- Data Pre-processing
- Data Manipulation
- Data Visualization
- Loading different types of dataset in Python
- Arranging the data
- Plotting the graphs
Here, we will discuss about predictive analysis and important concepts related to it as shown below.
- Fundamentals of Statistics
- Generalized Linear Models
- Regression and Clustering
This chapter will guide you through the important concepts of Machine Learning and algorithms. Let us take a look at the important concepts added in this chapter.
- What is Machine Learning?
- Supervised Learning – Regression
- Supervised Learning – Classification
- Model Selection and Boosting
- Unsupervised Learning
- Dimensionality Reduction
- Association Rules Mining and Recommendation
- Regression Use case: Weather Forecasting
- Clustering Use Case: Image classification
- Clustering Use Case: Recommender system
- Dimensionality Reduction Use Case: Structure Discovery
- Association Rule Mining
- Use Case Apriori Algorithm: Market Basket Analysis
This chapter discusses about one of the most important concepts in this course, time series analysis.
- What is Time Series?
- Time Series Analysis techniques and applications
- Components of Time Series
- Moving average
- Smoothing techniques
- Exponential smoothing
- Univariate time series models
- Multivariate time series analysis
- Arima model
- Time Series in Python
- Use Case of Checking Stationarity
- Learn how to convert a non-stationary data to stationary
- Implement Dickey Fuller Test
- Use case of ACF and PACF
- Generate the ARIMA plot
- Time Series Analysis Forecasting
This chapter will discuss about graphical models. Here’s a list of topics included in this chapter.
- Understanding graphical model
- Bayesian Network
- Model learning
- Use case Bayesian Network
In this chapter, you will get an introduction to reinforcement learning and how to implement it. Here’s a list of concept you will get to learn in this chapter.
- Getting started with Reinforcement Learning
- Bandit Algorithms and Markov Decision Process
- Dynamic Programming and Temporal Difference Learning methods
- What is Deep Q Learning?
- Calculating Reward
- Discounted Reward
- Calculating Optimal quantities
- Implementing Q Learning
- Setting up an Optimal Action
In this chapter, we will talk about text processing, Natural Language Processing, sentiment analysis, and many more.
- Text Pre-processing and Nature Language Processing
- Analyzing Sentence Structure
- Text Classification
- Sentiment Analysis
- Use case: Twitter Sentiment Analysis
- Use case: Chat Bot
This chapter will highlight fundamentals of deep learning. Here’s an introductory lesson for you to get started with Deep Learning.
- What is Deep Learning?
- Why Deep Learning?
- Advantage of Deep Learning over Machine learning
- 3 Reasons to go for Deep Learning
- Real-Life use cases of Deep Learning
This chapter will guide you through workings of Deep Learning and tools that we can use to implement deep learning.
- How Deep Learning Works?
- Activation Functions
- Illustrate Perceptron
- Train a Perceptron
- Parameters of Perceptron
- Graph Visualization
- Constants, placeholders, and variables
- Create a Model
- TensorFlow code- basics
- Use case Implementation
- Building a single perceptron for classification on SONAR dataset
In this chapter we will talk about advanced concepts of Neural Networks with TensorFlow.
- Understand limitations of a Single Perceptron
- Understand Neural Networks in Detail
- Illustrate Multi-Layer Perceptron
- What is a backpropagation?
- Getting started with TensorBoard
- Understand Backpropagation with an example
- Using TensorFlow build MLP Digit Classifier
- Building a multi-layered perceptron for classification of Hand-written digits
In this chapter we will talk about Deep Networks and all the core concepts related to it. Here’s a list of concepts included in this chapter.
- What is Deep Network?
- Why Deep Networks?
- Understand How Deep Network Works?
- How Backpropagation Works?
- Illustrate Forward pass, Backward pass
- Different variants of Gradient Descent
- Types of Deep Networks
- Use-Case Implementation on SONAR dataset
- Building a multi-layered perceptron for classification on SONAR dataset
This chapter will guide you through the concepts related to Convolution Neural Networks.
- What is CNN?
- Application of CNN
- Architecture of a CNN
- Convolution and Pooling layers in a CNN
- Understanding and Visualizing a CNN
- Learn how to build a convolutional neural network for image classification
In this chapter we will be discussing about recurrent neural networks and how to implement it.
- Introduction 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
- Building a recurrent neural network for SPAM prediction.
This chapter will guide you through all the important concepts related to RBM and autoencoders.
- Introduction to Restricted Boltzmann Machine
- Applications of RBM
- Collaborative Filtering with RBM
- Getting started with Autoencoders
- Autoencoders applications
- Learn how to build an autoencoder model for classification of handwritten images extracted from the MNIST Dataset
Here you will learn how to implement Keras API and how to use Keras with TensorBoard.
- Getting started with Keras
- Compose Models in Keras
- What is sequential composition?
- What is functional composition?
- Predefined Neural Network Layers
- What is Batch Normalization?
- Save and Load a model with Keras
- Customize the model training process
- Use case Keras implementation
- Learn how to build a model using Keras to do sentiment analysis on twitter data reactions on GOP debate in Ohio
- Using TensorBoard with Keras
This chapter will elaborate implementation of TFLearn API with use cases. Here’s a list of concepts you will be learning in this chapter.
- What is TFLearn?
- Compose Models in TFLearn
- Sequential Composition
- Functional Composition
- Predefined Neural Network Layers
- Batch Normalization
- Save and Load a model with TFLearn
- Customize the Training Process
- Use case Implementation with TFLearn
- Use TensorBoard with TFLearn
- Build a recurrent neural network using TFLearn to do image classification on hand-written digits
Data Science with Machine Learning Course
● Installation of Python on Windows, Mac and Linux distributions for Anaconda Python Deployment of Python IDE
● Basic commands of Python
● Understanding Python Objects and Data Types
● Understanding Python Operators
● Understanding Python Statements, Control flow and Functions
● Understanding Python OOP Concepts
Upon the completion of Python concepts successfully, we will proceed towards the advanced concepts in Python, starting off with NumPy, a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on arrays. This Chapter will cover the topics as shown below:
● Intro to NumPy
● Creating Arrays in NumPy
● Using Arrays and Scalars
● Indexing NumPy Arrays
● NumPy Array Manipulation
● Array Transportation
● Universal Array Function
● Array Processing
● Array Input and Output
● Importing NumPy Modules
● Creating and Initializing NumPy Arrays of different dimensions
● Working with arange in NumPy arrays
● Perform arithmetic operation on NumPy Arrays
● Create 3 Dimensional NumPy array
In this Chapter, we will learn Python’s very reliable library used for Scientific and technical computing, which is SciPy, we will cover the concepts as shown below:
● Introduction to SciPy
● Its Functions Building NumPy
● Clusters, Lining, Signals, Optimization, Integration, Sub packages and SciPy with Bayesian Theory
● Working with SciPy Cluster and Lining
● Import SciPy by applying the Bayes phrase to the specified notes.
This Chapter will cover Pandas, a Python library used for the Data Manipulation, the topics to be covered are as shown below: ● What is data manipulation?
● Use panda libraries to manipulate data
● Dependency of NumPy library libraries
● Pandas Series objects,
● Panda data frames
● Load and process data with pandas
● Combining data objects
● Merging, and various types of data object attachments.
● Record & clean notes, edit notes, visualize notes Hands-on Exercise – Manipulating data with pandas by Importing & navigating spreadsheets containing variable types such as float, integer, double, and others.
Learn how to create beautiful and interactive data visualizations in Python using various libraries such as:
● Pandas Built-in Data Visualization
● Plotly and Cufflinks
● Geographical Plotting Also, we will draw understand charts and diagrams using these libraries
● Using Matplotlib to create pie charts, scatter plots, line graphs, and histograms
● Create Graphs and Charts using different libraries
In this chapter, we will start off by understanding Machine Learning and its basic concepts such as:
● What is Machine Learning?
● Need of Machine Learning
● Types of Machine Learning o Supervised Learning o Unsupervised Learning o Reinforcement Learning
This chapter is all about diving into to the concepts of Supervised learning:
● Intro to Supervised Learning
● Types of Supervised Learning o Regression o Classification
● What is Regression? o Understanding Simple Linear Regression o Understanding Multiple Linear Regression
● Working with the math in the Linear Regression
In this chapter, we will understand the classification and logistic regression followed by:
● Linear regression vs Logistic Regression
● Math in the logistic regression and their formulas
● Confusion matrix and find the accuracy
● True and False positive rates
● Threshold evaluation with ROCR
In this chapter, you will be introduced to tree-based classification where in you understand the concepts of decision tree: o Impurity function and Entropy in Decision Tree o Concept of information gain for the right split of node o Gini index, to understand the concept of Gini index for the right split of node o Understanding Overfitting, pruning, pre-pruning, post-pruning, cost-complexity pruning o Also, you will be introduced to ensemble techniques, bagging, o Understanding random forests, and finding the apt number of trees in a random forest
Before diving into main topics, we will first understand the probabilistic classifiers:
● Then, we will understand Naïve Bayes concepts and the formula for the Bayes theorem.
● What is SVM or Support Vector Machine?
● Kernel Functions of SVM.
● Math and Formula behind SVM.
● Intro to Unsupervised Learning
● Types of Unsupervised Learning
o Dimensionality Reduction
● Understanding different types of Clustering
● Understanding the k-means clustering and math behind it
● Dimensionality reduction with PCA
● What is Natural Language Processing (NLP)?
● What is text mining?
● Importance and applications of text mining
● Understanding the working of NPL with text mining
● Working with Natural Language Toolkit (NLTK) environment
● Performing cleaning and pre-processing and text classification in text mining
● Understanding Deep Learning
● Understanding Deep Learning with Neural Networks
● Understanding essential differences b/w biological neural network & artificial neural network
● Understanding and implementing perceptron learning algorithm
● Working with Deep Learning frameworks like TensorFlow
● Working with Tensor Flow constants, variables and place-holders
● Introduction to Time Series Analysis
● Applications and components of time series
● Working with moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, and the ARIMA model,
● Performing Time series in Python
● Sentiment analysis in Python (Twitter sentiment analysis) with text analysis.
Python Certification Curriculum
In this Chapter, we will start from the very basic and understand the relevance of python followed by its setup, syntax, basic commands and a lot more, check below to know more:
• Introduction to Python
• Features of Python
• Advantages of using Python compared to other programming languages
• Types of Companies using Python
• Installation of Python on Windows, Mac and Linux distributions for Anaconda Python Deployment of Python IDE
• Basic commands of Python
• Installing Python Anaconda for Windows, Linux, and Mac
• Writing a “Hello World Program”
This Chapter will cover all the concepts in Data Types and Objects with their respective hands-on and practical sessions, you will learn how to work with data types, number, strings, lists, tuples and dictionaries, you can check the below topics to know more:
• Understanding Python Data Types and Numbers
• Performing Simple arithmetic operations in Python
• Assigning Variables in Python
• Operators in Python
o Understanding all the basic operators in Python
o Comparison Operators in Python
o Chaining Comparison Operators with Logical Operators
• Working with strings like indexing and slicing a string, formatting, etc
• Working with Lists like creating a basic and multiple list, adding elements & more
• Covering the essential concepts of Tuples
• Sets & Boolean in Python
• Dictionaries in python like creating and adding elements to a Dictionary
• Adding, Subtracting, Multiplying and Dividing numbers using arithmetic operations
• Creating a list with multiple distinct and duplicate elements
• Accessing and removing the elements from a list
• Slicing a list
• Creation and Concatenation of Tuples
• Slicing of Tuples
• Demonstration of Set and Boolean operations
• Demonstration on Python Dictionaries
Diving deep into Data Structure & Data Types
- Data frames
- Importing Data from various sources
- Database Input: Connecting to database
- Exporting Data to various formats
- Viewing Data: Viewing partial data and full data
- Variable & Value Labels: Date Values
Case study: We will go through a Case Study on HR Analytics
In this Python Chapter, we will go through all the basic concepts of Python Statements like if else, for loops, while loops, Control flow and all the essential Functions in Python, check the below topics to know more about this chapter:
• If Elif and Else Statements
• For loops in Python
• While loops & some useful operators in Python (range vs xrange on python)
• List Comprehensions in Python
• Chaining comparison in python
• Else with for and Switch Case in Python
• Using iteration in python
• Iterators in Python
• Iterators function
• Intro to Python functions
o Types of Python Functions
• Defining a Function in Python
o Rules for naming python function (identifier)
• Python Function Parameters
• Python Return Statement and calling a function
• Function Arguments
• What is the Python Function Argument?
o Types of Python Function Arguments
o Default Argument in Python
o Python Keyword Arguments
o Python Arbitrary Arguments
• Python Built-In Functions with Syntax and Examples
• Lambda Expressions, Map, and Filter Functions
Along with covering basics to advanced topics in this chapter, we will also cover the Lab-exercises and practical for all the above-mentioned topics based on an industrial-based case studies such as:
• Write a Python Function with or without the parameters
• Demo on If Else Statements and Iterators Functions
• Demo on Simple Boolean and Simple Math Functions
• Demo on create an object and write a for loop to print all odd numbers
• Demo on smaller or a greater number
• Use Lambda Expression to Map and Filter the Functions
essential concepts of OOP, where in we will start from basics to the advanced level while able to write smooth codes using the concepts of OOP, we will cover the topics like:
• Intro to Object-Oriented Programming and its need
• Attributes, Class Keywords,
• Class Object Attributes
• Methods in Python
• Data Hiding and Object Printing
• Constructors and Destructors in Python
• Class and static variable in python
• Class method and static method in python
• Inheritance, Encapsulation, Polymorphism & Abstraction
• Special Methods – Magic Method
• Write a Class
• Writing a Python program and incorporating the OOP concepts in it
Creating a Bank Account using OOP concepts
In this part of the chapter, we will understand the concepts of Modules, Packages and deep-dive into some of the common errors in Python, along with the concepts of Exception Handling, topics as shown below:
• Intro to Modules
• Working on PyPi using pip Install: Installing external packages and modules
• Numeric, Logarithmic, Power, Trigonometric and Angular functions in Python
• Understanding Python Errors and Exceptions
• Syntax Errors in Python
• Handling Exceptions in Python
• Raising Exceptions
• User-defined Exceptions
• Unit Testing in Python
• Demo on Modules
• Demo on Exception Handling
• Running Tests with Unittest Library
- Understanding more about Analytics World
- Data Science Vs Data Analytics Vs Machine Learning Vs Artificial Intelligence Vs Business Analysis
- Analytics keywords and their definitions
- Business Objectives
- Key driving factors in Analytics world
Case-Study: Case Study on how Predictive Analysis is helpful for Sales Industry
Chapter where you will learn all the essential topics of Decorators and Generators in Python such as:
• Understanding Decorators in Python
• Syntax of Decorators and Working with them
• Understanding Generators in Python
• Working with Generators
• Demonstration on Decorators and Generators
- Performing Data Preparation steps
- Outlier treatment
- Flat Liners
- Missing values
- Dummy Creation
- Variable Reduction
- Data Alignment and fine tuning
Upon the completion of Python concepts successfully, we will proceed towards the advanced concepts in Python, starting off with NumPy, a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on arrays.
This Chapter will cover the topics as shown below:
• Intro to NumPy
• Creating Arrays in NumPy
• Using Arrays and Scalars
• Indexing NumPy Arrays
• Numpy Array Manipulation
• Array Transpotation
• Universal Array Function
• Array Processing
• Array Input and Output
• Importing NumPy Modules
• Creating and Initializing NumPy Arrays of different dimensions
• Working with arange in Numpy arrays
• Perform arithematic operation on NumPy Arrays
• Create 3 Dimensional NumPy array
Scientific computing using SciPy
In this Chapter, we will learn Python’s very reliable library used for Scientific and technical computing, which is SciPy, we will cover the concepts as shown below:
• Introduction to SciPy
• Its Functions Building NumPy
• Clusters, Linning, Signals, Optimization, Integration, Sub packages and SciPy with Bayesian Theory.
• Working with SciPy Cluster and Lining
• Import SciPy by applying the Bayes phrase to the specified notes.
Implementing Decision Tree model in R
This Chapter will cover Pandas, a Python library used for the Data Manipulation, the topics to be covered are as shown below:
• What is data manipulation?
• Use panda libraries to manipulate data
• Dependency of NumPy library libraries
• Pandas Series objects,
• Panda data frames
• Load and process data with pandas
• Combining data objects
• Merging, and various types of data object attachments.
• Record & clean notes, edit notes, visualize notes
Hands-on Exercise – Manipulating data with pandas by Importing & navigating spreadsheets containing variable types such as float, integer, double, and others.
Learn how to create beautiful and interactive data visualizations in Python using various libraries such as:
• Pandas Built-in Data Visualization
• Plotly and Cufflinks
• Geographical Plotting
Also, we will draw understand charts and diagrams using these libraries
• Using MatPlotLib to create pie charts, scatter plots, line graphs, and histograms
• Create Graphs and Charts using different libraries
We will start off this chapter by revising the previous concepts in data analysis such as Pandas, MatplotLib, Numpy and SciPy. Then, we will move onto the topics shown below:
• Understanding of Machine Learning
• Understanding SciKit Learn
• Need of Machine Learning
• Types in Machine Learning
• Machine Learning Worklfow
• Understanding SciKit Learn!
• Machine Learning Use-Cases
• A brief understanding of various ML Algorithms:
o Supervised Learning
o Unsupervised Learning
• Working with Machine Learning Algorithms
While diving more into Machine Learning, in this chapter, we will understand one of its essential types that is Supervised Learning. We will cover the topics as shown below:
• What exactly is Supervised learning?
• Understanding the Classification and Regression Algorithms
• What is linear regression and how to do calculations in Linear Regression?
• Understanding Linear regression in Python
• Understanding Logistics regression
• Working with Supports vector machine
• xgboost (standalone step)
• Working with
• Using SciKit Library with Random Forest algorithm for implementing Supervised Learning
After learning the concepts of Supervised Learning, this chapter will cover the yet another type of Machine Learning, which is Unsupervised Learning, below are the topics which we are going to cover:
Introduction to Unsupervised Learning
Looking into the Use Cases of Unsupervised Learning Understanding Clustering,
Types of Clustering – Exclusive Clustering, Overlapping Clustering, Hierarchical Clustering
Understanding K-Means Clustering and its algorithm
Stepwise calculation of k-means algorithm
Running k-means with SciKit Library
Understanding association mining rule
Market basket analysis
Working with association rule mining measures covering support, trust, lift, and apriori Algorithm,
• Demo on Unsupervised Learning
• Demo on Algorithms in the SciKit Learn package for applying machine learning techniques, and training the network model
• Demo on Apriori
This is an advanced topic where we will integrate spark with python for performing cluster-computing. PySpark, a tool of apache, lightning-fast cluster computing technology, designed for fast computation)
Introduction to Pyspark with PySpark, need for a Python Spark, Fundamentals of PySark, Pyspark in Industry, Installing PySpark, Fundamentals of PySpark, Excellence Mapreduce, Use of PySpark Demo and PySpark.
Hand Exercise: Demonstration of contours and conditional statements related to tuple operations, properties, lists, etc., List operations, related properties, set properties, related operations, dictionary operations, related properties
AWS Solutions Architects Curriculum
In this chapter, you will be first introduced to Cloud Computing, different models and its concepts, then we will move towards essential concepts in AWS as shown below:
• What is Cloud Computing?
• Different Cloud Computing Models
• What is AWS? And how is AWS a leader in the cloud market?
• Intro to AWS Management Console
• Quick intro to the essential AWS Services such as:
o Elastic Cloud Compute (EC2)
o AWS Simple Storage Service (S3)
o Virtual Private Cloud (VPC)
o Amazon Machine Image (AMI)
o Elastic Block Storage (EBS)
o Elastic Load Balance (ELB) and more
• Thorough understanding of AWS Architecture
• Understanding Public/Elastic IP’s and comparing them
• Launching, Initiating and Terminating and AWS EC2 Instance
• What is Auto-Scaling?
• Best Practices and Costs for AWS EC2 and understanding various backup services concepts in AWS
• Setting-up for AWS free-tier account
• Launching an EC2 Instance
• Creating an S3 bucket through console and AWS CLI
• Understanding the process of hosting a website
• Launching a Linux Virtual Machine using an AWS EC2 Instance
In this chapter, you will understand the essential concepts behind the object storage, where in you will identify when to use which service and topics like:
• AWS Storage
• In-depth of AWS S3 – Creation, Version Control, Security, Replication, Transfer, Acceleration
• Other concepts in S3 like, storage classes, life cycle policy, cost-optimization and more
• Create and Configure CloudFront with S3
• Understanding and working with Elastic Block Storage
• Amazon Glacier storage for persisting data backup and archiving
• Data importing and exporting using Amazon Snowball
• Host a Static Website on AWS S3
• Uploading images and documents to AWS S3 from a Website
• Replicate Data across regions
• Export and Import data from Glacier storage using lifecycle policy
• Access any static website using AWS CloudFront
• Elastic Block Storage (EBS) for block-level persistent storage volumes with S3 buckets
• Connect cloud-based storage with the on-premise software using AWS Storage Gateway
We will start this chapter by understanding the essential concepts behind Auto-Scaling and various topics as shown below:
• Auto-Scaling mechanism and its components
• Auto-Scaling Lifecycle and its policy
• Understanding Fault Tolerance in AWS
• Understanding Elastic load balancing and its types
• Comparison between Classic, Network and Application Load Balancer
• Accessing the Elastic Load Balancer
• Working with Route53 and various routing policies
• Creating a Classic and a Network load balancer
• Working with Application load balancer and Auto-Scaling
• Scaling policy in Auto-Scaling
• Understanding how to register a domain using Route 53
• Routing internet traffic to the resources and automatically checking health of resources
In this part of the Chapter, we will first understand the essential types of AWS Database services which are basically used for managing structured and unstructured data in other-term we call them SQL and NoSQL Databases. The topics which will be covered here are:
• What exactly is a Relational Database?
• Understanding AWS RDS and Amazon Aurora
• Benefits of using AWS RDS and Amazon Aurora
• What exactly is a NoSQL or a Non-Relational Database?
• Understanding NoSQL Service of AWS i.e., AWS DynamoDB
• Working with a Data warehousing product i.e., Amazon Redshift
• Using an in-memory data store with ElasticCache
• Using AWS Kinesis for the Analysis of Data
• Creating an RDS Instance and its read replica instance
• Adding data to a replica RDS
• Storing an application’s data to master RDS
• Creating Tables and running queries in master RDS
• Creating PostgreSQL and MySQL instance using Amazon Aurora
• Create a NoSQL Table and run queries in it using DynamoDB
• Working with Redis Cache
• Using Kinesis Data Stream for Visualizing the website’s traffic
In this chapter, we will get an in-depth knowledge of Amazon Virtual Private Cloud, IAM, CloudWatch and CloudTrail, as shown below:
• Understanding VPC and its components
• Benefits of VPC
• VPC Use-Cases
• Understanding & Creating a Virtual Private Network
• Working on VPC Networking, IP addressing and VPN Connections
• Network Access Control List and Security Groups and Network Address Translation (NAT)
• Understanding VPC Peering
• Deep-dive into Identity Access Management (IAM)
• User management using IAM
• Policies and API Keys access using various AWS Services
• Key Management Service in IAM
• Accessing billings and creating alerts on billings
• IAM Best Practices
• Creating a VPN and attach an EC2 instance on it and access it on internet and a private network by using AWS PrivateLink
• Creating two instances on different VPC’s with the help of VPC peering
• Creating a new role for an application which can access to an S3 Bucket
• Creating new policies for new users to either give them the admin or restricted privileges
• Rotating different credentials for IAM users to keep the users account protected
• Log-in to AWS Console using MFA
• Accessing AWS Services by creating API keys
• Creating multiple budgets according to each and every service used on a monthly basis
This chapter will give you the insights of Monitoring services in AWS, where in you will work on concepts like:
• Managing IAM events using AWS CloudTrail
• Monitor and Manage AWS resources using CloudWatch
• Deploy configuration alerts and notifications using CloudWatch
• CloudWatch Billing and exploring Trusted Advisor
• Logging IAM events through AWS CloudTrail
• Monitoring an EC2 Instance with the help of CloudWatch
• Storing Logs in S3 by enabling ClouTrail
Learn about different Application Services provided by AWS used for sending E-mails, to receive notifications and for queuing the messages, this chapter will also deal with the latest Serverless Computing service which AWS Lambda, find the more topics below:
• Working with AWS Simple Email Service (SES), AWS Simple Notification Service (SNS), AWS Simple Queue Service (SQS) and AWS Simple Work Flow (SWF)
• Working with the orchestration service i.e., AWS Elastic Beanstalk
• AWS OpsWorks and CLI
• Work with AWS Lambda – A Serverless Computing Service
• Send an Email using AWS SES
• Enable and generate a notification service by sending a notification using AWS SNS
• Orchestrate an S3 Bucket using Elastic Beanstalk
• Copy an Object by using AWS Lambda
• Send an E-mail to the user using AWS Lambda whenever an object is added to S3 bucket
• Sending a notification via Lambda whenever a message is sent to SQS
• Model and Provision your apps with the help of AWS OpsWorks
In this chapter, you will know how to do the configuration management of your server along with its automation, check more topics below:
• Manage resources using AWS CloudFormation
• Configuration Management and Automation of Servers with the help of OpsWorks
• More into AWS Elastic Beanstalk
• Comparing CloudFormation, OpsWorks and Elastic Beanstalk
• Automatically checking the health of resources using Route53
• Using AWS CloudFormation for the provisioning of Infrastructure
• Routing the traffic towards the resources and checking the health of the resources automatically
• Installing LAMP Server on an EC2 Instance using CloudFormation
• Managing Servers using AWS OpsWorks Stacks
• Deploying a Web Application with the help of DynamoDB using Elastic Beanstalk (for orchestration)
This Section will cover all the important aspects required to clear the AWS Solutions Architect Exam:
• Examination guidelines
• Most likely asked Interview Questions
• Some other useful tips for completing exams and job interviews.
Soft Skill Course Curriculum
In this chapter, we will discuss some of the common mistakes to avoid while communicating, also we will talk about ways to improve communication skills to enhance your personality. Take a look at the table of content
- Understanding communication skills
- Common mistakes to avoid
- Tips to improve communication skills
This chapter highlights the science behind job interview preparation. Check out the table of content.
- What to do before the interview?
- What to do during the interview?
- What to do after the interview?‘
In this part of the chapter, we will discuss dos and don’ts while building a resume. We will also see how to build the best resume, for both freshers and working professionals, to impress the interviewer.
This chapter highlights dos and don’ts while writing a cover letter, also gives a step by step walkthrough to building the best cover letter for both freshers and working professionals.
In this chapter, we will discuss the importance of a professional profile to showcase skills and achievements. We will also see how to build an attractive LinkedIn profile.
Here we will elaborate different ways to write a professional email. We will also talk about dos and don’ts while writing a professional mail.
Java Course Curriculum
In this chapter, we will start from the very basic and understand the relevance of Java programming language followed by the concept of OOP, OOP principles, and a lot more, check below to know more:
● Introduction to Java Programming
● Introduction to Object Orientated Programming (OOP concept)
● Object Oriented Programming Principles
● Features of Java programming language
● Java bytecode
● Java Virtual machine or JVM
This chapter highlights basic data types, variables, operators and many more. Check out the table of content.
● Basic Data Types
○ Primitive Data Types
○ Reference or Object Data Types
○ Arithmetic operators
○ Bit wise operators
○ Relational operators
○ Boolean logic operators
○ Assignment operators
● Operator precedence
● Decision making and control statements
In this part of the chapter, we will understand the concepts of selection statements, control statements, iterations, while, do while, for loop, along with the concepts of break, return, and many more as shown below:
● Selection statements in Java
● If statement
● Switch statement
● Iteration statement
● While and do-while
● For loop
● For-each loop
● Nested loop
● Jump statement
This chapter highlights concepts of objects and classes in depth. Let us take a look at the table of content for this chapter.
● Objects in Java
● Constructors in Java
● Returning and passing objects as parameters
● Nested and inner classes in Java
● Single and multilevel inheritance in java,
● Extended classes in Java
● Access Control in Java
● Using super in Java
● Overloading and overriding methods in Java
● Abstract classes in Java
● Using final with inheritance in Java
In this chapter, we will focus on some of the packages, how to define, access, and import packages in Java. We will also discuss interfaces in Java. Take a look at the table of content for this chapter. ● Defining a package in Java
● Concept of CLASSPATH in Java
● Access modifiers in java
● Importing a package in Java
● Defining and implementing interfaces in Java
Here we will elaborate different string handling methods in Java. Take a look at the detailed content list that are discussed in this chapter.
● String constructors in Java
● Special string operations in Java
● Character extraction in Java
● Searching and comparing strings in Java
● String buffer class in Java.
This chapter elaborates some of the most important concepts of java programming language. Take a look at the table of content.
● Exception handling in Java
● Types of Exception in Java
● Uncaught exceptions in Java
● TRY in Java
● Catch and multiple catch statements in Java
● Using THROW in Java
● File handling
○ I/O streams
○ File I/O
This chapter highlights java collection, maps, queues, JDBC, drivers and many more.
● Collection framework in Java
● Preeminent interfaces in Java
● Comparable and Comparator in Java
● Lists in Java
● Maps in Java
● Sets in Java
● Queues in Java
● Java Database Connectivity (JDBC)
● Drivers in Java
● Accessing drivers in Java
● Connection in Java
● Statement in Java
● CRUD Operations in Java (with examples)
● Pepared statement in Java
● Callable statement in Java
- Project: ChatbotProblem Statement: Your company wants to install a chatbot on the website to enhance the user experience. As the AI Engineer, your task is to build a chatbot using Python and deep learning techniques with great accuracy.Industry: AITopic: Deep Learning, NLTK, Keras, pickle, tensorflow, json
Project: Driver Drowsiness Detection System
Problem Statement: You are working with a team of AI Engineers on a project to prevent accidents due to driver drowsiness. Your goal is to build a deep learning model to detect if the drivers eyes are closed or open.
Industry: AI, IoT
Topic: OpenCV, CNN, TensorFlow, Keras, Pygame
At the time of enrollment team Eduranz will assign one mentor for you and he will be guiding you in this lifetime Journey.
24/7 Tech Adviser Support
Lifetime 24/7 Technical and Non Technical Support from team Eduranz.
Get Lifetime opportunity to access and attend the live sessions multiple times.
Assignments & Quizzes
Every module will be followed by certification based assessment and quiz.
Become a Certified Professional.
Team Eduranz will update your Resume before forwarding it to our 60+ global Clients.
All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them underwent a rigorous selection process that included screening profiles, technical assessments, and training demonstrations before being certified for training. We also ensure that only high-level graduates live in our faculty.
Live Virtual Classes or Online Classes: With online class training, you can access courses via video conferencing from your desktop to increase productivity and reduce work time and personal time. Independent study online: In this mode, you will receive videos with lectures and can continue the course as you wish. Win Python Portable Distribution is an open-source environment where all the exercises are immediately carried out. Installation instructions are given during training.
This Eduranz Artificial Intelligence course gives you hands-on experience in mastering one of the best programming languages, Python. In this Artificial Intelligence online course, you will learn basic and advanced Python concepts, including MapReduce in Python, machine learning, streaming Hadoop, and Python packages such as Scikit and Scipy. After successfully completing training, you will receive an Eduranz Attendance Certificate. As part of this Artificial Intelligence online course, you work on real-time projects that are very important in the corporate world, on step-by-step assignments and curricula developed by industry experts. After completing the Artificial Intelligence course, you can apply for some of the best jobs in the top-ranked MNC in the world. Eduranz provides lifetime video access, tutorials, 24×7 support, and upgrades to the latest version at no additional cost. Therefore it is definitely a one-time investment for a practical Python online course.
Eduranz offers a 24/7 request solution and you can pick up your tickets at any time from our dedicated support team. You can use email support for all your questions. If your request is not answered via email, we can also arrange one-on-one discussions with the faculty. You will be glad to know that you can switch to Eduranz support after completing the training. We also don’t limit the number of tickets you can collect when solving questions and doubts.
Eduranz offers independent learning for those who want to learn at their own pace. This training also gives you the benefits of email questions, tutorial sessions, 24×7 support, and access to modules or LMS for lifelong learning. In addition, you will receive the latest version of the learning material at no additional cost. Independent Eduranz training is 75% lower than teacher-led online training. If you experience problems while studying, we can arrange virtual courses directly with the trainer at any time.
Eduranz offers the most up-to-date, relevant and valuable projects in the real world as part of the training program. In this way, you can integrate what you have learned in the real industry. Each training is delivered with various projects where you can thoroughly test your skills, learning and practical knowledge so that you are well prepared for the industry. They work on very interesting projects in the fields of high technology, e-commerce, marketing, sales, networking, banking, insurance and more. After successfully completing your project, your skills will be counted as a result of six months of intensive industry experience.
Eduranz actively supports all trainees who have successfully completed the training. That’s why we are involved in more than 80 top MNCs worldwide. This way you can be in exclusive organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco and other similar-sized companies. We also support you during job interviews and preparation of your CV.
In any case, you can switch from self-directed self-training to online training only by paying an additional amount and participating in the next set of training that will be specifically notified to you.
After completing the Eduranz Training Program along with all real projects, tests and assignments and achieving at least 60% points in the qualification exam; You received a certificate that was certified by Eduranz. This certification is recognized by Eduranz’s partner organizations, which includes a lot of top MNCs worldwide that are also part of the Fortune 500 list.
In our job support program, we help you start your dream job by sharing your resume with potential tenants, helping you make resumes, and preparing you for interview questions. Eduranz training should not be seen as an employment agency or employment guarantee, because the entire employment process is handled directly between the student and the employing company and the final choice is always left to the employer.