This learning path is designed to help you prepare for the Google Certified Professional Machine Learning Engineer exam. Even if you don’t plan to take the exam, these courses will help you gain a solid understanding of how to implement machine learning on Google Cloud Platform. Candidates who pass the exam will earn the Google Professional Machine Learning Engineer certification.The Professional Machine Learning Engineer exam tests your knowledge of six subject areas.

Target Audience:

• Data professionals
• People studying for the Google Professional Machine Learning Engineer exam

Pre-requisites:

• Basic understanding of cloud concepts
• Experience writing Python code

Course Duration:

• 2 Days ( 14 hours )

Course Content:

• Overview of Google Cloud Platform
• Getting Started with Deep Learning: Introduction To Machine Learning
• Introduction to Google AI Platform
• Building Convolutional Neural Networks on Google Cloud
• Getting Started With Deep Learning: Recurrent Neural Networks
• Getting Started With Deep Learning: Improving Performance
• Inspecting and De-Identifying Data With Google Cloud Data Loss Prevention
• Introduction to Google BigQuery
• Structure and Analyze Data with Google BigQuery
• Drawing Insights with BigQuery ML
• Visualizing Trends from Order History with Google Data Studio
• Introduction to Google Cloud Dataflow
• Preview Exam: Google Professional Machine Learning Engineer
• Recommended Reading for Google Professional Machine Learning Engineer Exam

Course Customization Options

To request a customized training for this course, please contact us to arrange.

This course introduces people to the principles and process of logical data modeling, Physical Data Modeling using ERWin Tool. The course details how to convert the business data requirements into a graphical representation. It teaches people how to analyze business requirements that should be incorporated into a logical data model and in Physical Data model in turn. In short, participants will learn proven, practical skills for analyzing and modeling data requirements, and make them ready to be transformed into a relational database and design multi Dimensional Database.

Target Audience:

• Anyone who is interested to learn
• Who wants to make a career in data Modelling
• Database Administrators
• Database Modelers
• Analytics Managers
• ETL and BI Developers

Pre-requisites:

This certification training course does not presume or require any prior knowledge or prerequisites.

However, basic knowledge of database concepts be an added advantageous.

We are recommending that students should have following:

• Basic knowledge of database

Course Duration:

• 3 Days ( 21 hours )

Course Content:

Introduction to Logical Data Modeling

• Definitions
• Benefits of logical data modeling
• Data modeling vs. physical database design
• Roles involved in data modeling
• Steps in the data modeling process
• Example data model

Entities

• Identifying entities
• Validating entities

► Documenting “instances” of entities

Distinguishing entities from attributes

• Naming entities
• Starting an Entity/Relationship (E/R) diagram
• Workshop

Relationships

• Identifying significant relationships
• Determining the “cardinality” or “degree” of a relationship
• One-to-One
• One-to-Many
• Many-to-Many
• Determining whether a relationship is optional or mandatory
• Giving a relationship a name
• Documenting the relationships in the E/R diagram
• Walking people through an E/R diagram
• Workshop
• Resolving Many-to-Many Relationships
• Real-world examples of many-to-many relationships
• Why many-to-many relationships are broken down into simpler relationships
• Identifying “association” or “intersection” entities
• Documenting the new relationships in the E/R diagram
• Workshop

Attributes and Normalization

• Defining and categorizing attributes
• Domains and integrity rules
• Unique identifiers/primary keys
• Foreign keys
• Occurrence population
• Normalization: validating the placement of each attribute
• Attribute does not repeat (first normal form)
• Attribute is dependent on its entire UID (second normal form)
• Attribute is dependent only on its UID (third normal form)
• Workshop

Sub-types and Super-types

• Identifying subtypes: real-world examples of subtypes and super types
• Determining when entities are similar

Determining when entities are similar

• UIDs
• Attributes
• One-to-one relationships

One-to-one relationships

• Creating subtypes and super types
• “Type” entities
• Using subtypes to apply fourth normal form
• Establishing the relationships of the sub- and super-entities to other entities
• Mutually exclusive vs. non-mutually exclusive subtypes
• “Role” entities to handle complex subtypes
• Workshop

Recursive Relationships

• Real-world examples of recursive relationships
• Discovering recursive relationships
• Determining whether the relationships are optional or mandatory
• Documenting the new relationships in the E/R diagram
• Hierarchical vs. Network recursive relationships
• “Structure” or “Bill of Materials” entities: fifth normal form
• Workshop

Erwin Data Modeling Concepts

• Entities
• Instances
• Attributes
• Keys
• Relationships
• Unification
• Generalization hierarchies
• Normalization

Erwin Dimensional Modeling Fundamentals

• Modeling Approaches
• Dimensional Modeling First Steps
• Dimensional Model Complexity
• Time Variant Analysis
• Dimensional Sources and Data Mapping

Introduction to CA Erwin Data Modeler

• Creating and configuring new models
• Creating and defining entities
• Creating and defining attributes
• An introduction to the relationship of an attribute and its assigned domain
• Creating and defining relationships
• Creating and using Subject Area views of models
• Extending Erwin with user defined metadata
• Erwin reporting

Intermediate CA Erwin Data Modeler

• Creating and using user Defined Domains
• Managing naming standards
• Managing Data type standards
• An introduction to Forward Engineering (SQL Generation)
• An introduction to reverse Engineering
• An introduction to Complete Compare
• Complete Compare Scenarios
• Complete Compare Exercise
• Design Layer Architecture
• API programming
• ODBC reporting
• Data Warehouse modelling

Dimensional Modeling Self Paced Sessions

Session_1: Identify User Requirements

Session_2: Modeling Design Phase

Session_3: Semantic Layer

Session_4: Multi Dimensional Database Path I

Session_5: Multi Dimensional Database Part II

Delivery Methodology

• We are using an experiential delivering methodology that blends theoretical concepts with hands-on practical learning to ensure a holistic understanding of the subject or course

Course Customization Options

To request a customized training for this course, please contact us to arrange.

Blue Prism is a powerful Robotic Process Automation (RPA) tool that automates business processes by employing a robotic workforce. It is a cost-effective solution to help organizations obtain high-quality business intelligence while reducing human error. It is an application of technology that allows employees / delegates in a company to configure computer software, or a ‘robot’, to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems.

Course Objectives:

• Create and manage their own business objects and processes.
• Manage sessions and control robots and processes.
• Deploy and manage Blue Prism in accordance with different business needs
• Fetch Data Grid views from a Windows Based application using Dynamic Attributes and OCR
• Generate multiple Queue references on web-based application using Work Queues
• Retrieve excel files dynamically from a directory and perform various consolidations

Pre-requisites:

• To become a RPA developer one should have Strong understanding of the business & Logical mind.
• This course can only be delivered using customer software licenses.

Target Audience:

• Developers and programmers
• IT professionals in charge of deploying Blue Prism within their organization
• Technical business individuals interested in deploying a robotic workforce

Course Duration:

• 28 hours – 4 days

Course Content:

Introduction to RPA: 

• WHAT,HOW & WHY’s of RPA
• Pre-requisites of RPA
• RPA Benefits
• Attended and Unattended RPA
• Robotics Life Cycle

RPA Tool 

• Blue Prism Components
• High-level differences between Blue Prism and Other Tools
• Architecture & Installation of Blue Prism

Blue Prism Introduction 

• Tabbed Menu and Navigate Menu
• Controlling Play

• • Step-in
  • Step-over
  • Step-out

• Data Items Visibility & Exposure

• • Global Data Item
  • Local Data Item
  • Environmental Variables
  • Session Variables
  • Statistic Variables

• Startup Parameters
• Loops And Collections
• Hardcoded & Dynamic Variables
• Collections Inside A Collection
• Input And Outputs
• Exception Handli

• • Internal Exception
  • System Exception
  • Business Exception
• Recovery And Resume
• Recovery Mode
• Exception Bubbling
• Arbitrary and Throttle waits
• Attach and Detach
• Visual Bo And Utility Bo Usage
• Configuring Process Alerts
• Code Stage
• Regular Expressions
• Notepad-Data Entry
• Break-Point
• Pre-conditions And Post-Conditions
• MS-Excel
• Internal Business Objects

Advance concepts 

• Dynamic attributes and parameters
• Credential management
• Spying
• WIN32/AA/HTML/RM
• Attributes
• Environmental locking

• • Acquire Lock
  • Query Lock
  • Release Lock

• Work Queues

• • Managing Queue
  • Tags with real time examples
  • Priority with real time examples
  • Status with real time examples

• Email (Mailbox Automation)
• Excel Automation using VBO
• Database automation
• Scheduling and Session Management
• Optical Character Recognition
• PDF reading
• Typecasting
• Login agent
• MS Access with database
• Run Modes:

• • Background
  • Foreground
  • Exclusive

• Code stage with C# codes
• Code stage with vb script

LIFE CYCLE CONCEPTS 

• FTE Calculations
• Estimate Effort Calculations
• Feasible & Technical Assessment Analysis
• Source Code Management & Version Control
• Release Management

Documentation 

• Standard Operating Procedure (SOP) / Business (Functional Requirement Document (BRD)
• Process Definition Document (PDD)
• Solution Design Document (SDD)
• Current & Future State Process Flows
• BOT Management Document
• Implementation Document
• Change Request Creation

Exclusive Topics: 

• Resource pooling and management
• Excel as database
• Web services
• Mainframe and sap automation(Grid / SAP mode)
• Citrix Based Application
• Mainframe Application
• Code Stage
• Html, DOM-path
• JavaScript
• Chrome Automation
• Comparing Blue prism 5 with Blue prism 6
• Summary and Closing Remarks

Course Customization Options

To request a customized training for this course, please contact us to arrange.

Deep learning training gives you an in-depth understanding of the architecture of TensorFlow Core, API layers, and the use cases. Master unsupervised learning models, deep learning models and more. Right from installing and configuring TensorFlow, importing data, simple models to develop complex layered models and architectures to crunch huge data sets leveraging the distributed, robust and scalable machine learning framework from Google.

Learn to implement Keras on top of TensorFlow to experiment with deep neural networks and tune machine learning models to produce more successful results with our deep learning with TensorFlow course.    

Course Objectives:

• Articulate the core architecture and API layers TensorFlow
• Construct a computing environment and learn to install TensorFlow
• Develop TensorFlow graphs required for everyday computations
• Use logistic regression for classification along with TensorFlow
• Develop, design and train a multilayer neural network with TensorFlow
• Demonstrate Activation functions and Optimizers in detail with hands-on
• Demonstrate intuitively convolutional neural networks for image recognition
• Design and construct a neural network from simple to more accurate models
• Understand recurrent neural networks, its applications and learn how to build these solutions
• Understand hyper-parameters and tuning
• Learn how to build industry’s leading uses cases eg, Recommendation systems, Speech recognition, commercial grade Image classification and object localization etc….
• Lead ML/DL projects based on TensorFlow implementation

Pre-requisites:

• Basic knowledge of statistical concepts is desirable.

Target Audience:

• Software engineers
• Data scientists
• Data analysts
• Statisticians with an interest in deep learning

Course Duration:

• 28 hours – 4 days

Course Content:

Introduction 

• Data science & its importance
• Key Elements of Data Science
• Artificial Intelligence & Machine Learning Introduction
• Who uses AI?
• AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply Chain
• What makes a Machine Learning Expert?
• What to learn to become a Machine Learning Developer?
• Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
• Deep Learning: A revolution in Artificial Intelligence
• What is Deep Learning?
• Advantage of Deep Learning over Machine learning
• 3 Reasons to go for Deep Learning
• Real-Life use cases of Deep Learning

Neural Networks Basics 

• How Neural Networks Work?
• Various activation functions – Sigmoid, Relu, Tanh
• Perceptron and Multi-layer Perceptron
• What is TensorFlow?
• TensorFlow code-basics
• Graph Visualization
• Constants, Placeholders, Variables
• Creating a Model
• Step by Step – Use-Case Implementation
• Introduction to Keras
• Understand Neural Networks in Detail
• Illustrate Multi-Layer Perceptron
• Backpropagation – Learning Algorithm
• Understand Backpropagation – Using Neural Network Example
• MLP Digit-Classifier using TensorFlow

Deep Neural Networks 

• Why Deep Networks
• Why Deep Networks give better accuracy?
• Understand How Deep Network Works?
• How Backpropagation Works?
• Illustrate Forward pass, Backward pass
• Different variants of Gradient Descent
• Types of Deep Networks
• Batch Normalization
• Activation and Loss functions
• Hyper parameter tuning
• Training challenges and techniques
• Optimizers, learning rate, momentum, etc.

Convolutional Neural Networks 

• Introduction to CNNs
• CNNs Application
• Architecture of a CNN
• Forward propagation & Backpropagation for CNNs
• Convolution, Pooling, Padding & its mechanisms
• Understanding and Visualizing a CNN
• An overview of pre-trained models (AlexNet, VGGNet, InceptionNet & ResNet) and Transfer Learning
• Image classification using CNN

Advanced Computer Vision 

• Auto encoders
• Semantic segmentation
• YOLO
• Siamese Networks
• Object & face recognition using techniques above

Natural Language Processing 

• Sentiment Analysis
• Topic Summarization
• Topic Modelling
• Nltk, Gensim, vader, etc.
• Bag of Words and Tf-IDF
• Cosine Similarity of terms, documents concepts
• Text Cleaning and Pre-processing using Regex
• Tokenization, Stemming and Lemmatization

RNN And LSTM 

• Introduction to Sequential data
• Word embeddings and lang translation
• RNNs and its mechanisms
• Vanishing & Exploding gradients in RNNs
• Time series analysis
• LSTMs
• LSTMs with attention mechanism
• GRU

Visualization Using Tensorboard 

• What is Tensor board?
• Test vs Train set accuracy
• T-SNE
• Occlusion Experiment
• CAM, Saliency and Activation maps
• Visualizing Kernels
• Style transfer

Reinforcement Learning And Gans 

• Introduction
• How GANs work?
• Applications of GANs (Generative adversarial networks)
• Summary and Closing Remarks

Course Customization Options

To request a customized training for this course, please contact us to arrange.

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine Learning algorithms comb through data and identify patterns that are too complex to be discerned by the human mind.

These patterns can then be used for decision making and action Apache Spark is a powerful platform that for running Machine Learning. This course will how you how to perform various Machine Learning using Apache Spark built in MLib component.

Course Objectives:

• Overview of Apache Spark
• Clustering
• Regression
• Classification
• Recommendation

Pre-requisites:

• This is an intermediate course. Participants should have basic knowledge on the following subjects: Python Apache Spark

Target Audience:

• Big Data Analysts
• Data Scientists
• Data Analysts

Course Duration:

• 14 hours – 2 days

Course Content:

Module 1: Apache Spark Basics

• Recap of Apache Spark Basics
• Install Apache Spark on Local Computer
• Read CSV Data
• Manipulating Dataframe
• ML Libraries

Module 2: Preprocessing 

• Normalizer
• Standardizer
• Tokenizer
• TF-IDF

Module 3: Clustering 

• What is Clustering
• Clustering Algorithms
• KMeans Clustering
• Hierarchical Clustering

Module 4: Classification 

• What is Classification
• Naives Bayes Clasiifier
• Decision Tree Classifer
• •Multi Layer Perception

Module 5: Regression 

• What is Clustering
• Clustering Algorithms
• Linear Regression
• Decision Tree Regression
• Gradient Boosted Tree Regression

Module 6: ML Pipeline

• What is Pipeline
• Creating a Pipeline for Movie Review Classification

Module 7: Recommendation (Optional) 

• Recommendation Systems
• Collaborative Filtering
• Summary and Closing Remarks

Course Customization Options

To request a customized training for this course, please contact us to arrange.

Automation Anywhere is an RPA (Robotic Process Automation) tool for creating a virtual workforce of software bots that optimizes and lowers the costs of end-to-end business processes in an enterprise. Anywhere and other RPA systems to allow candidates to evaluate their choice of tool relative to the organizational problem they wish to solve with RPA.

Course Objectives:

• Step through the deployment of an Automation Anywhere sample system. Case studies will be examined, and comparisons made between Automation.

• Understand how RPA works and where Automation Anywhere fits into the Business Process Automation big picture.
• Use Automation Anywhere to create software bots that handle tasks such as Excel computations, email responses, and database manipulations.
• Analyze existing business processes and develop an RPA plan for enhancing the efficiency of a workflow.
• Lower operating costs and reduce errors through automation.
• Diagnose and debug problems during the development and deployment of Automation Anywhere software bots.
• Communicate effectively with management and staff about the role and expectation of software bots within the organization.

Pre-requisites:

• An understanding of general automation concepts
• A basic understanding of programming concepts

Target Audience:

• Persons in charge of streamlining and optimizing business processes
• Managers with technical skills
• Developers and Engineers

Course Duration:

• 28 hours – 4 days

Course Content:

Introduction

• RPA’s role in Business Process Automation

Overview of Automation Anywhere vs other RPA tools and technologies

• OpenSpan
• UIPath
• Blue Prism

Business use cases for RPA

• Case study and discussion
• How humans should interact with software bots and vice versa

Designing a Robotic Process Automation plan

• Understanding and documenting tasks and workflows
• Estimating the ROI for deploying RPA

Installing and configuring Automation Anywhere

• Setting up the database
• Configuring with Control Room

Overview of Automation Anywhere architecture and interface

• Understanding the Dashboard, Control Room, Task Editor, etc.

Creating a software bot

• A simple screen recording software bot
• A simple web recording software bot

Using the software bot to automate Excel processes

• Reading, writing, and processing tasks

Connecting to a database with the software bot

• Accessing and manipulating data

Using a software bot to automate email tasks

• Reading, writing and sending emails 

Extending a software bot’s capabilities

• Writing complex instructions and conditions
• Accessing a file system and logging tasks
• Image recognition

Integrating software bots with other applications and services

• Soap web services
• Rest web services

Troubleshooting

• Handling exceptions during execution

Securing Automation Anywhere

• PGP (Pretty Good Privacy)

Deploying Automation Anywhere software bots

• Limiting the scale of the initial rollout

Monitoring the performance of a “virtual workforce”

• Identifying and resolving incorrect behavior

Scaling the software bot workforce

• Software, hardware and human factors in extending the reach of the robotic workforce
• Summary and Closing Remarks

Course Customization Options

To request a customized training for this course, please contact us to arrange.

UiPath Training coverage will be for the RPA Developer Role with Foundational to Intermediate level knowledge along-with introduction to advanced topics. Become relevant & build career in ‘Future of Work’

Course Objectives:

• All the topics are applicable for SAP FICO. Special techniques applicable for SAP will be taught. Focus will be on SAP FICO module and the participants will be able to automate SAP FICO processes based on this training.

Pre-requisites:

• Students should have prior experience of using Office 365.

Target Audience:

• Intermediate to advanced users, business analysts and even developers who need an easy and quick way to create interactive forms that may need to initiate a business process or workflow.

Course Duration:

• 28 hours – 4 days

Course Content:

Module 1: RPA Introduction

RPA Introduction

• What is RPA?
• How RPA Works?
• Processes Suitable for RPA
• RPA Market Size and Growth
• RPA Development Skills
• ROI

RPA and AI

• AI Technologies and RPA
• Digital Workforce

RPA Benefits and Use Cases

• Benefits of RPA
• Use Cases

RPA Products

• RPA Products
• Leadership Positions
• Product Selection Criteria

Module 2: UiPath Introduction

UiPath Introduction

• UiPath Software
• Studio.Robot.Orchestrator.UiExplorer
• UiPath UI and Keyboard Shortcuts
• Updating UiPath Studio
• Chrome & Firefox Extensions
• Connecting to a Source Control
• Logging

Workflows

• Sequences
• Flowcharts
• State Machines
• Activities and Packages
• Managing Packages

Module 3: UI Automation

UI Elements

• About UI Elements
• UI Activities Properties
• Input Methods
• Output Methods (Screen Scraping)
• Relative Scraping

Selectors

• About Selectors
• Selectors with Wildcards
• Full Versus Partial Selectors
• UiPath Explorer

Recording

• Recording Types
• | Basic | Desktop | Web | Citrix |
• Automatic Recording
• Manual Recording

UI Automation

• Mouse. Keyboard. Find. Control.

User Events

• Element Triggers
• Image Triggers
• System Triggers

Screen Scraping

• Full Text
• Visible Text
• OCR

Data Scraping

• Semi-Structured / Patterned Data
• Structured Data / Tabular Data

Module 4: Programming and Data Manipulation

Data Types

• Scalar
• Arrays and Collections
• User Defined, Libraries

Variables

• Managing Variables
• Naming Best Practices
• The Variables Panel
• Types of Variables and Using Them
• GenericValue
• String, Boolean, Number, DateTime
• Array, List, DataTable
• Class Type (.Net Type)

Arguments

• Managing Arguments
• The Arguments Panel
• Naming Best Practices
• Using Arguments

Namespaces

• Importing Namespaces

Control Flows

• If | While | Do While | For-Each, Break,
• Continue | Switch
• Assign | Delay

Advanced Activities

• Parallel Pick
• Pick Branch
• SOAP and REST Services
• Integration with Python, Java, VB.NET, JScript

Data Manipulation

• Runtime Data Manipulation

Error Handling

• Try-Catch

Debugging

• Debugging a Workflow
• Best Practices

Module 5: Image and Text Automation

About Image and Text Automation

• Virtual Machine / Citrix Environment

Image and Text Automation (Citrix)

• Mouse and Keyboard Activities
• Text, OCR and Image Activities

Module 6: Excel, PDF, Email Automation

Excel Automation

• Excel App Integration vs Workbook
• Excel Application Scope
• Read, Write and Append Range
• Read and Write Cell
• Build Data Table
• Read Row

PDF Automation

• Native and Image
• PDF Extracting
• Large Text Data
• Read PDF Text
• Read PDF with OCR
• Screen Scraping
• Extracting Specific Elements
• Get Text
• Anchor Base
• Find Element
• Find Image
• Find Relative Element
• Scrape Relative

Email Automation

• Email as Input and Output
• Email Protocols – SMTP, POP3, Outlook, IMAP, Exchange
• Mail Activities – Get, Send, Move, Delete
• Save Attachments
• Save Mail Message

Module 7: Orchestrator

Orchestrator

• Control Center
• Dashboard
• Provisioning and Deployment
• Robots and Environments
• Processes and Packages
• Jobs and Schedules
• Queues and Transactions
• Assets, Alerts, Audit, Logs
• Users and Roles

Module 8: SAP and Terminal Automation

SAP Automation

• How to Automate SAP Applications (using screen snapshots)

Mainframe and Terminals Automation

• How to Automate Mainframe Terminals (using screen snapshots)

Module 9: Advanced Topics 

• Introduction to RE Framework
• Introduction to Cognitive Activities
• Introduction to Process Mining
• Introduction to RPA as a Service
• Introduction to Custom Activities
• Introduction to UiPath Go!

Course Customization Options

To request a customized training for this course, please contact us to arrange.

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI and know how to set direction for your team’s work, this course will show you how.

Much of this content has never been taught elsewhere and is drawn from my experience building and shipping many deep learning products. This course also has two “flight simulators” that let you practice decision-making as a machine learning project leader. This provides “industry experience” that you might otherwise get only after years of ML work experience.

Course Objectives:

• Understand how to diagnose errors in a machine learning system, and
• Be able to prioritize the most promising directions for reducing error
• Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
• Know how to apply end-to-end learning, transfer learning, and multi-task learning

Pre-requisites:

This course is aimed at individuals with basic knowledge of machine learning, who want to know how to set technical direction and prioritization for their work. – It is recommended that you take course one and two of this specialization (Neural Networks and Deep Learning, and Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization) prior to beginning this course.

Target Audience:

• Machine Learning Researchers
• AI Engineer
• Data Mining and Analysis
• Machine Learning Engineer
• Data Scientist
• Business Intelligence (BI) Developer

Course Duration:

• 35 hours – 5 days

Course Content:

ML Strategy 1

• Why ML Strategy
• Orthogonalization
• Single number evaluation metric
• Satisfying and Optimizing metric
• Train/Dev/Test distributions
• Size of the Dev and Test sets
• When to change Dev/Test sets and metrics
• Why human-level performance?
• Avoidable bias
• Understanding human-level performance
• Surpassing human-level performance
• Improving your model performance 

ML Strategy 2

• Carrying out error analysis
• Cleaning up incorrectly labeled data
• Build your first system quickly, then iterate
• Training and testing on different distributions
• Bias and Variance with mismatched data distributions
• Addressing data mismatch
• Transfer learning
• Multi-task learning
• What is end-to-end deep learning?
• Whether to use end-to-end deep learning

Course Customization Options

To request a customized training for this course, please contact us to arrange.

Become a Machine Learning (ML) specialist with the Machine Learning using Python and R program. Gain knowledge of ML algorithms and applications using the two most popular programming languages. Use Python and R to enable regression analysis and to build predictive models. Orient yourselves with Black Box techniques like Neural Networks and Support Vector Machine. Machine Learning Training using Python and R programming includes an overview of analytical techniques used to manipulate massive amounts of data and then driving meaningful business insights from the same. The course module demonstrates the various techniques used to analyze structured and unstructured data, build advanced prediction models with Machine Learning algorithms and Data Visualization. The course is loaded with practical case studies that enable the participants to solve complex business problems and improve profitability in their companies.

Course Objectives:

• Become familiar with analyzing data, computing statistical measures along with Data Wrangling, Data Cleansing, Data Manipulation, etc.
• Become familiar with Machine Learning algorithms including Black Box techniques such as Neural Networks and Support Vector Machine
• Become familiar with Regression algorithms and the application of Python, R as statistical software in Machine Learning and Data Science
• Build predictive models using Amazon Machine Learning Services
• Be able to create Data Visualization, Data Manipulation in different forms and draw meaningful business insights from the underlying data

Pre-requisites:

• Basic Mathematical Knowledge
• Basic Data Science Concepts

Target Audience:

• Candidates aspiring to be Data Scientist, Machine Learning Expert, Data Analyst, etc.
• Employees of organizations
• Managers with knowledge of basic programming and decision-makers
• Graduates
• Mid-level and Senior-level Executives
• Data Science and Data Analytics Professionals

Course Duration:

• 5 Days (35 Hours)

Course Content:

• Python, R Introduction and Installation
• Connecting A Variety of Data Sources using Python and R
• Machine Learning Primer using Python and R
• Handling Balanced versus Imbalanced Datasets
• Basic Statistics and Data Visualization using Python and R
• Data Manipulation using Python and R
• Functions and Programming in Python and R
• Data Mining Supervised, Unsupervised, Reinforcement Learning
• Linear and Logistic Regression using Python and R
• Decision Trees using Python and R
• Closing and Remarks

Course Customization Options

To request a customized training for this course, please contact us to arrange.

To request a customized training for this course, please contact us to arrange.