Automation.

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1 Automation

2 WHAT IS AUTOMATION? Automation testing is a technique uses an application to implement entire life cycle of the software in less time and provides efficiency and effectiveness to the testing software. Automation testing is an Automatic technique where the tester writes scripts by own and uses suitable software to test the software. It is basically an automation process of a manual process. Like regression testing, Automation testing also used to test the application from load, performance and stress point of view. In other word, Automation testing uses automation tools to write and execute test cases, no manual involvement is required while executing an automated test suite. Usually, testers write test scripts and test cases using the automation tool and then group into test suites. The main goal of Automation testing is to increase the test efficiency and develop software value. BENEFITS OF AUTOMATION Fast: Runs tests significantly faster than human users. Repeatable: Testers can test how the website or software reacts after repeated execution of the same operation. Reusable: Tests can be re-used on different versions of the software. Reliable: Tests perform precisely the same operation each time they are run thereby eliminating human error. Comprehensive: Testers can build test suites of tests that covers every feature in software application. Programmable: Testers can program sophisticated tests that bring hidden information. WHO SHOULD ATTEND? Automation and control engineers Operations and engineering management Manufacturing systems/it and networking professionals Packaging engineers Industrial systems and machinery designers Purchasers of equipment for OEMs and discrete manufacturers, as well as processing and packaging operations systems integrators Software development engineers for the discrete and process production industries

3 LEARNING OBJECTIVES You will learn what is Artificial Intelligence (AI) and what is the relationship of AI with Machine Learning, Deep Learning and Data Science. You will also learn how Machines are learning faster than ever. You will learn how you can use Artificial Intelligence (AI) to drive your UI test automation projects. You will also learn how AI test automation tool uses machine learning to speed-up the authoring, execution and maintenance of automated tests. Master Machine Learning on Python & R Have a great intuition of many Machine Learning models Make accurate predictions Make powerful analysis Make robust Machine Learning models Use Machine Learning for personal purpose Handle specific topics like Reinforcement Learning, NLP and Deep Learning Handle advanced techniques like Dimensionality Reduction Know which Machine Learning model to choose for each type of problem Build an army of powerful Machine Learning models and know how to combine them to solve any problem Learn to use Python professionally, learning both Python 2 and Python 3! Create games with Python, like Tic Tac Toe and Blackjack! Learn advanced Python features, like the collections module and how to work with timestamps! Learn to use Object Oriented Programming with classes! Understand complex topics, like decorators. Build a complete understanding of Python from the ground up!

4 COURSE CONTENTS PYTHON PROGRMMING Introduction to Programming with Python Introduction to Python Mac/Linux installation Windows setup Interpreted vs. compiled programming languages Creating and running our first Python script Choosing an integrated development environment (IDE) How to share your code with us and get help with errors Programming Basics Basic types - numbers Basic types strings and string manipulation Basic types - Boolean operators Lists (arrays), Dictionaries and Variables Built-in functions and User-defined functions Adding arguments to a function Default, Keyword and infinite arguments Return values from functions If, elif, else statements For/while loops Importing libraries into a script Web Scraper Beautiful Soup Parsing our soup Directional navigation Image scraper Improvements to our web scraper Getting Started with PyMongo Introduction and setup Inserting documents Bulk inserts Counting documents Multiple find conditions Datetime and keywords Indexes Data Visualization Installing matplotlib World population graph Adding labels and custom line color Multiple lines and line styling Configuring the graph Let's make pie (charts) Letting Pandas make data simpler Using Panda's data for pie charts Custom legend

5 COURSE CONTENT R PROGRAMMING Installing R and R Studio (MAC & Windows) Core Programming Principles Types of variables Using Variables Logical Variables and Operators The "While" Loop Using the console The "For" Loop The "If" statement Fundamentals Of R What is a Vector? Let's create some vectors Using the [] brackets Vectorized operations The power of vectorized operations Functions in R Packages in R Matrices Project Brief: Basketball Trends Matrices Building Your First Matrix Naming Dimensions Colnames() and Rownames() Matrix Operations Visualizing With Matplot() Subsetting Visualizing Subsets Creating Your First Function Basketball Insights Matrices Data Frames Project Brief: Demographic Analysis Importing data into R Exploring your dataset Using the $ sign Basic operations with a Data Frame Filtering a Data Frame Introduction to qplot Visualizing with Qplot: Building Data frames Merging Data Frames Advanced Visualization with GGPlot2 Project Brief: Movie Ratings Grammar of Graphics - GGPlot2 What is a Factor? Aesthetics Plotting with Layers Overriding Aesthetics

6 COURSE CONTENTS Mapping vs Setting Histograms and Density Charts Starting Layer Tips Statistical Transformations Using Facets Coordinates Perfecting by Adding Themes SCALA Introduction to Scala Install Scala and SBT Introduction to SBT (Scala Build Tool) How to Install and Setup SBT on Windows 10 How to Install Scala and SBT on Ubuntu / Ubuntu LTS How to Install Scala and SBT on Mac Scala Basics Data Types and Variables How to Install Scala IDE Windows 10 + First Scala Hello World Application Scala String Interpolation Scala - IF ELSE Statements Scala while Loop and do-while Loop Scala For Loop Match expressions Functional Programming in Scala Scala Functions Anonymous Functions + Default Values Function + more Scala - Higher Order Functions Scala - Partially Applied Functions How to use closures in Scala Function Currying in Scala Strings Scala - Collections Arrays Lists Scala Sets Scala Maps Scala Tuples Scala Options Type Map, flatmap, flatten and filter (Higher-order Methods) Reduce, fold or scan KERAS Set up your environment. Install Keras. Import libraries and modules. Load image data from MNIST. Pre-process input data for Keras. Pre-process class labels for Keras.

7 COURSE CONTENT Define model architecture. Compile model. Fit model on training data. Evaluate model on test data. TENSORFLOW Getting Started with Machine Learning What is Machine Learning (ML)? Types of ML and ML Pipeline Variants of ML mode and Framing a ML problem Playing with Machine Learning (ML) Optimization A Neural Network Playground Combining Features and Feature Engineering Image Models and Effective ML What makes a good dataset? Error Metrics and Accuracy Precision and Recall Creating Machine Learning Datasets Splitting Dataset and Python Notebooks Building ML models with Tensorflow What is TensorFlow? Core TensorFlow Getting Started with TensorFlow Lab Overview TensorFlow Lab Review and Estimator API Machine Learning with tf.estimator Building Effective ML and Refactoring Lab Review Train and Evaluate and Monitoring Scaling ML models with Cloud ML Engine Why Cloud ML Engine? Development Workflow Packaging trainer TensorFlow Serving Feature Engineering Good Features Causality and Numeric Enough Examples and Raw Data to Features Categorical Features and Feature Crosses Bucketizing and Wide and Deep Where to do Feature Engineering Hyperparameter Tuning + Demo ML Abstraction Levels

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