The Future Technologies - RPA - Robotic Process Automation

Popular  RPA tools in industry :

1)  UI Path

Course - name	Relavant Certification Name
UiPath - RPA Developer foundation Training	UiPath - Level 1 - Foundation Training
UiPath - Orchestrator Training	UiPath - Level 2 - Orchestrator 2018.3 Training
UiPath - SAP Automation Training	RPA Developer - SAP Automation Training
UiPath - RPA Developer Advanced Training	UiPath - Level 3 - Advanced Training
UiPath - RPA Solution Architect Training (2.0)	UiPath - Solution Architect
UiPath - RPA Awareness Training (2.0)	UiPath - RPA Awareness Training
UiPath - RPA Infrastructure Training	UiPath - RPA Infrastructure Training
UiPath - RPA Business Analyst Training	UiPath - Business Analyst
UiPath - RPA Implementation Methodology Training	UiPath - Implementation Methodology

2)  Blue Prism

Artificial Intelligence

Reference -  https://intellipaat.com/artificial-intelligence-masters-training-course/

Deep Learning Course Content

Introduction to Deep Learning & Neural Networks

The domain of machine learning and its implications to the artificial intelligence sector, the advantages of machine learning over other conventional methodologies, introduction to Deep Learning within machine learning, how it differs from all others methods of machine learning, training the system with training data, supervised and unsupervised learning, classification and regression supervised learning, clustering and association unsupervised learning, the algorithms used in these types of learning. Introduction to AI, Introduction to Neural Networks, Supervised Learning with Neural Networks, Concept of Machine Learning, Basics of statistics, probability distributions, hypothesis testing, Hidden Markov Model.

Multi-layered Neural Networks

Introduction to Multi Layer Network, Concept of Deep neural networks, Regularization. Multi-layer perceptron, capacity and overfitting, neural network hyperparameters, logic gates, thevariousactivationfunctions in neural networks like Sigmoid, ReLu and Softmax, hyperbolic functions. Backpropagation, convergence, forward propagation, overfitting, hyperparameters.

Training of neural networks

The various techniques used in training of artificial neural networks, gradient descent rule, perceptron learning rule, tuning learning rate, stochastic process, optimization techniques, regularization techniques, regression techniques Lasso L1, Ridge L2, vanishing gradients, transfer learning, unsupervised pre-training, Xavier initialization, vanishing gradients.

Deep Learning Libraries

How Deep Learning Works, Activation Functions, Illustrate Perceptron, Training a Perceptron, Important Parameters of Perceptron,Multi-layer Perceptron What is Tensorflow, Introduction to TensorFlow open source software library for designing, building and training Deep Learning models, Python Library behind TensorFlow, Tensor Processing Unit (TPU) programmable AI accelerator by Google,Tensorflow code-basics, Graph Visualization, Constants, Placeholders, Variables, Step by Step – Use-Case Implementation, Keras.

Introduction to Keras API

Keras high-level neural network for working on top of TensorFlow, defining complex multi-output models, composing models using Keras, sequential and functional composition, batch normalization, deploying Keras with TensorBoard, neural network training process customization.

TFLearn API for TensorFLow

Implementing neural networks using TFLearn API, defining and composing models using TFLearn, deploying TensorBoard with TFLearn.

DNN: Deep Neural Networks

Mapping the human mind with Deep Neural Networks, the various building blocks of Artificial Neural Networks, the architecture of DNN, its building blocks, the concept of reinforcement learning in DNN, the various parameters, layers, activation functions and optimization algorithms in DNN.

CNN: Convolutional Neural Networks

Introduction to CNNs, CNNs Application, Architecture of a CNN, Convolution and Pooling layers in a CNN, Understanding and Visualizing a CNN, Transfer Learning and Fine-tuning Convolutional Neural Networks,feature maps, Kernel filter, pooling, deploying convolutinal neural network in TensorFlow

RNN: Recurrent Neural Networks

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, basic RNN cell, unfolded RNN,  training of RNN, dynamic RNN, time-series predictions.

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs, the importance of GPUs in training Deep Learning Networks, the forward pass and backward pass training technique, the GPU constituent with simpler core and concurrent hardware.

Autoencoders & Restricted Boltzmann Machine (RBM)

Introduction to RBM and autoencoders, deploying it for deep neural networks, collaborative filtering using RBM, features of autoencoders, applications of autoencoders.

Chatbots

Automated conversation bots using one of the descriptive techniques

• IBM Watson
• Google API.AI
• Microsoft’s Luis
• Amazon Lex
• Generative
• Open-Close Domain Bots
• Sequence to Sequence model (LSTM).

AI Deep Learning Projects

Project 1 : Image recognition with TensorFlow

Industry : Internet Search

Problem Statement : Building a robust deep learning model to recognize the right object on the internet depending on the user search for the image.

Description : In this project you will learn how to build Convolutional Neural Network using Google TensorFlow. You will do visualization of images using training, providing input images, losses and distributions of activations and gradients. You will learn to break each image into manageable tiles and input it to the Convolutional Neural Network for the desired result.

Highlights :

• Constructing Convolutional Neural Network using TensorFlow
• Convolutional, Dense & Pooling layers of CNNs
• Filtering the images based on user queries.

Project 2 : Building an AI-based chatbot

Industry : Ecommerce

Description : This project involves building the chatbots using Artificial Intelligence and Google TensorFlow.

Problem Statement : Understanding the customer needs and offering the right services through Artificial Intelligence chatbot. You will learn how to create the right artificial neural network with the right amount of layers to ensure the customer queries are comprehensible to the Artificial Intelligence chatbot. This will help to understand natural language processing, understanding beyond keywords, data parsing and providing the right solutions.

Highlights:

• Breaking user queries into components
• Building neural networks with TensorFlow
• Natural language processing.

Project 3 : Ecommerce product recommendation

Industry : Ecommerce

Problem Statement : Recommending the right projects to customers by artificial intelligence

Description : This project involves working with recommender systems to provide the right product recommendation to customers with TensorFlow. You will learn how to use Artificial Intelligence to check for user past buying habits, find out what are the products that go hand-in-hand, and recommend the best products for a particular product.

Highlights :

A2Z Topics - Areas - Subjects

A2Z subjects/areas :

2D Drawing
3D + Animation
3D Drawing
3D Printing

A

Accessibility
Accounting
Acoustics
Advertising
Analytics
Animation
Architecture
Audio + Music
Audio Effects
Audio Engineering
Audio for Video
Audio Foundations
Audio Plug-Ins
Automotive Design
 
B

B2B Marketing
B2C Marketing
Big Data
BIM
Black and White
Blogs
Branding
Business
Business Intelligence
Business Skills


C

CAD
Cameras + Gear
Career Development
Character Animation
Charts + Graphs
Civil Engineering
Classroom Management
Cloud Computing
CMS
CNC + CAM
Collaboration
Color
Color Correction
Communication
Compositing
Computer Skills (Mac)
Computer Skills (Windows)
Construction
Content Marketing
Content Strategy
Creative Insights
Creative Inspirations
Creative Spark
Creativity
Customer Experience


D

Data Analysis
Databases
DAWs
Design
Design Business
Design Foundations
Design Patterns
Design Projects
Design Skills
Design Techniques
Desktop Apps
Developer
Development Tools
Digital Painting
Digital Publishing
Documentaries
Drawing
DSLR Video
DVD Authoring


E

Ebooks
Ecommerce
Education + Elearning
Educational Technology
Elearning
Email
Email Marketing
Enterprise Content Management
Enterprise Marketing


F

Film Scoring
Filmmaking
Finance
Flash Photography
Forms
Freelancing


G

Game Design
Game Development
Games
GIS


H

HDR
Higher Education
Hillman Curtis Artist Series
Home + Small Office


I

Illustration
Infographics
Instructional Design
Interaction Design
Interior Design
iPad Music Production
iPhone, iPod, iPad
IT
IT and Hardware
IT Help Desk


K

K-12 Education
Keying


L

Languages
Lead Generation
Leadership
Lighting
Live Performance
LMS
Logo Design
lynda.com Presents


M

Management
Manufacturing
Marketing
Masking + Compositing
Mastering
Materials
MEP
Microphones
Mixing
Mobile Apps
Mobile Marketing
Mobile Web
Modeling
Motion Graphics
Music Business
Music Composition
Music Editing
Music Lessons
Music Notation
Music Production
Music Theory


N

Network Administration
Night + Low Light
Note Taking


O

Online Marketing
Operating Systems


P

Page Layout
Particles + Dynamics
PDF
Photo Management
Photography
Photography Foundations
Plugin
Podcasting
Portraits
Post Production
PPC
Presentations
Previsualization
Print Design
Print Production
Printing Photos
Product Design
Productivity
Productivity and Cloud Apps
Programming Foundations
Programming Languages
Project Management
Projects
Prototyping
Public Relations


R

Raw Processing
Recording Techniques
Remixing
Rendering
Responsive Design
Restoration
Retouching
Rigging

S

Santa Barbara Film Festival
Scanning
Screenwriting
SDKs
Security
SEM
SEO
servers
Sharing Photos
Sharpening
Shooting Video
Site-planning
Small Business Marketing
Social Media Advertising
Social Media Marketing
Social Networks
Songwriting
Sound Design
Spreadsheets
Start to Finish
Structural
Student Tools
Studio Setup
Synthesis


T

Teacher Professional Development
Teacher Tools
Textures
Time Management
Tuning
Typography


U

User Experience


V

Video
Video Cameras
Video Delivery
Video Editing
Video Foundations
Video Pre-Production
Video Production
Virtual Instruments
Virtualization
Visual Effects


W

Web
Web Conferencing
Web Design
Web Development
Web Fonts
Web Foundations
Web Graphics
Web Video
Wireframing
Word Processing
Writing

Hadoop projects - Use cases - Scenarios

Project 1 – Working with MapReduce, Hive, Sqoop

Topics : This project is involved with working on the various Hadoop components like MapReduce, Apache Hive and Apache Sqoop. Work with Sqoop to import data from relational database management system like MySQL data into HDFS. Deploy Hive for summarizing data, querying and analysis. Convert SQL queries using HiveQL for deploying MapReduce on the transferred data. You will gain considerable proficiency in Hive, and Sqoop after completion of this project.

Project 2 – Work on MovieLens data for finding top records

Data – MovieLens dataset

Topics : In this project you will work exclusively on data collected through MovieLens available rating data sets. The project involves the following important components:

·         You will write a MapReduce program in order to find the top 10 movies by working in the data file

·         Learn to deploy Apache Pig create the top 10 movies list by loading the data

·         Work with Apache Hive and create the top 10 movies list by loading the

Project 3 – Hadoop YARN Project – End to End PoC

Topics : In this project you will work on a live Hadoop YARN project. YARN is part of the Hadoop 2.0 ecosystem that lets Hadoop to decouple from MapReduce and deploy more competitive processing and wider array of applications. You will work on the YARN central Resource Manager. The salient features of this project include:

·         Importing of Movie data

·         Appending the data

·         Using Sqoop commands to bring the data into HDFS

·         End to End flow of transaction data

·         Processing data using MapReduce program in terms of the movie data, etc.

+

Project 4  – Partitioning Tables in Hive

Topics : This project involves working with Hive table data partitioning. Ensuring the right partitioning helps to read the data, deploy it on the HDFS, and run the MapReduce jobs at a much faster rate. Hive lets you partition data in multiple ways like:

·         Manual Partitioning

·         Dynamic Partitioning

·         Bucketing

This will give you hands-on experience in partitioning of Hive tables manually, deploying single SQL execution in dynamic partitioning, bucketing of data so as to break it into manageable chunks.

Project 5 – Connecting Pentaho with Hadoop Ecosystem

Topics : This project lets you connect Pentaho with the Hadoop ecosystem. Pentaho works well with HDFS, HBase, Oozie and Zookeeper. You will connect the Hadoop cluster with Pentaho data integration, analytics, Pentaho server and report designer. Some of the components of this project include the following:

·         Clear hands-on working knowledge of ETL and Business Intelligence

·         Configuring Pentaho to work with Hadoop Distribution

·         Loading, Transforming and Extracting data into Hadoop cluster

Project 6 – Multi-node cluster setup

Topics : This is a project that gives you opportunity to work on real world Hadoop multi-node cluster setup in a distributed environment. The major components of this project involve:

·         Running a Hadoop multi-node using a 4 node cluster on Amazon EC2

·         Deploying of MapReduce job on the Hadoop cluster

You will get a complete demonstration of working with various Hadoop cluster master and slave nodes, installing Java as a prerequisite for running Hadoop, installation of Hadoop and mapping the nodes in the Hadoop cluster.

·         Hadoop Multi-Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup

·         Running Map Reduce Jobs on Cluster

Project 7 – Hadoop Testing using MR

Topics : In this project you will gain proficiency in Hadoop MapReduce code testing using MRUnit. You will learn about real world scenarios of deploying MRUnit, Mockito, and PowerMock. Some of the important aspects of this project include:

·         Writing JUnit tests using MRUnit for MapReduce applications

·         Doing mock static methods using PowerMock&Mockito

·         MapReduceDriver for testing the map and reduce pair

After completion of this project you will be well-versed in test driven development and will be able to write light-weight test units that work specifically on the Hadoop architecture.

Project 8 – Hadoop Weblog Analytics

Data – Weblogs

Topics : This project is involved with making sense of all the web log data in order to derive valuable insights from it. You will work with loading the server data onto a Hadoop cluster using various techniques. The various modules of this project include:

·         Aggregation of log data

·         Processing of the data and generating analytics

The web log data can include various URLs visited, cookie data, user demographics, location, date and time of web service access, etc. In this project you will transport the data using Apache Flume or Kafka, workflow and data cleansing using MapReduce, Pig or Spark. The insight thus derived can be used for analyzing customer behavior and predict buying patterns.

Project 9 – Hadoop Maintenance

Topics : This project is involved with working on the Hadoop cluster for maintaining and managing it. You will work on a number of important tasks like:

·         Administration of distributed file system

·         Checking the file system

·         Working with name node directory structure

·         Audit logging, data node block scanner, balancer

·         Learning about the properties of safe mode

·         Entering and exiting safe mode

·         HDFS federation and high availability

·         Failover, fencing, DISTCP, Hadoop file formats

Cloud Optimization

Flume

Flume

·          Introduction to Flume

·          Configuration and Setup

·          Flume Sink with example

·          Channel

·          Flume Source with example

·          Complex flume architecture

ZooKeeper

·          Introduction to ZooKeeper

·          Challenges in distributed Applications

·          Coordination

·          ZooKeeper : Design Goals

·          Data Model and Hierarchical namespace

·          Cilent APIs

YARN and an introduction to Hadoop 2.0

YARN and an introduction to Hadoop 2.0

·          Hadoop 1.0 Limitations

·          MapReduce Limitations

·          History of Hadoop 2.0

·          HDFS 2: Architecture

·          HDFS 2: Quorum based storage

·          HDFS 2: High availability

·          HDFS 2: Federation

·          YARN Architecture

·          Classic vs YARN

·          YARN Apps

·          YARN multitenancy

·          YARN Capacity Scheduler

Sqoop

Sqoop

·          Install and configure Sqoop on cluster

·          Connecting to RDBMS

·          Installing Mysql

·          Import data from Mysql to hive

·          Export data to Mysql

·          Internal mechanism of import/export

Best Reference  document  at  http://sqoop.apache.org/docs/1.4.6/SqoopUserGuide.html

Importing  data  from  to  HDFS from RDBMS

From HDFS edge node or from client -  to see list of all available databases

Some  familiar commands

• sqoop list-databases  --connect "jdbc:mysql://quickstart.cloudera:3306/retail_db"  --username=retail_dba  --password=cloudera    --warehouse-dir=/user/cloudera/sqoop_import

mysql -u retail_dba -p

sqoop list-databases  --connect "jdbc:mysql://quickstart.cloudera:3306/retail_db"  --username=retail_dba  --password=clouderamysql -u retail_dba -p

CREATE TABLE employees_export (

emp_no int(11) NOT NULL,

birth_date date NOT NULL,

first_name varchar(14) NOT NULL,

last_name varchar(16) NOT NULL,

gender enum('M','F') NOT NULL,

hire_date date NOT NULL,

PRIMARY KEY (emp_no)

sqoop import  --connect "jdbc:mysql://quickstart.cloudera:3306/retail_db"  --username=retail_dba  --password=cloudera --table employees_export1  --warehouse-dir=/user/cloudera/sqoop_import -m 1