USE MACHINE LEARNING TO MAKE APPS AND AI TO DETECT FRAUD
Machine Learning Professional Institute
Lateral Entry Professionals and Freshers
Online and Classroom Sessions
Week Days and Week Ends
Daily 2 hrs during Weekdays
•Learn how to work with Machine Learning.
•You will learn how to write Machine Learning.
•Learn to how to code and deploy Machine Learning
•Different Machine Learning practical questions asked during real time interviews .
•Learn while you build rich interactive applications with Machine Learning
•The best way to learn modern Machine Learning step-by-step from scratch.
•Learn Basic and Advanced Machine Learning Programming and become a Machine Learning Developer
•In This Course u Will Learn How To Develop Apps using Machine Learning
•Learn the essential skills to level-up from beginner to advanced Machine Learning developer in 2021!
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•Free Aptitude classes & Mock interviews
•Course has been framed by Industry experts
•Get Certified at the Best Training Institute.
•We Provide the Course Certificate of completion
•Assignments and test to ensure concept absorption.
• Finessing your tech skills and help break into the IT field
•One-on-one training, online training, team or Corporate training can be provided
•This Instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises
•C, asp.net vb.net c# c c++, Java Developer, Php Developer, dot net c# asp.net vb.net
•full stack web developer, Core Java, Javascript, Jquery, Ajax, Html5, Css3, Bootstrap, Node.js, Mysql, Mongodb
•Java/j2ee, Microsoft, Erp, Cloud, Qa/testing, Automation Testing, Analytics, Machine Learning, Artificial Intelligence, Agile Project Management, Mobility
•QA and Testing, erp, IMS, Cloud Computing, asp.net vb.net c# c c++, core java j2ee, oracle plsql unix shell script, cobol jcl db2 vsam cics, Sharepoint C#
•Sharepoint Architect, Mobile Architect, MSBI Module Lead, Filenet Developer, WBM, IBM BPM
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Course Trailer
•Using Android Studio
•Mobile machine learning plan: Get excited!
•How to Get Android Studio
•Exploring Android Studio Interface
•Setting up Emulator and Running a Project
•Coding in Java
•Java Language Basics
•Variable Types
•Operations on Variables
•Arrays and Lists
•Array and List Operations
•If Statements and Switch Statements
•While Loops
•For Loops
•Functions
•Parameters and Return Values
•Classes and Objects
•Superclass and Subclasses
•Static Variables and Axis Modifiers
•Declaring Fields
•Declaring Methods
•Hello Mammoth
•Print “Hello Mammoth”
•Project 1: Make an Android App
•Android App Development
•Building Basic User Interface
•Connecting UI to Backend
•Implementing Backend and Tidying UI
•Resources
•Discover machine learning
•Machine Learning Explained
•PyCharm and Plan: Get excited!
•How to Get PyCharm and Python
•Let’s Explore PyCharm
•Source code: PyCharm
•Coding in Python
•Variables
•Variable Operations and Conversions
•Collection Types
•Operations on Collections
•Control Flow: If Statements
•While and For Loops
•Source code: Coding in Python
•Declaring Variables
•Calling Functions
•Declaring a Class
•Project 2: Build a Model in TensorFlow
•Topics List
•Importing TensorFlow to PyCharm
•Constant Nodes and Sessions
•Variable Nodes
•Placeholder Nodes
•Operation Nodes
•Loss, Optimizers, and Training
•Building a Linear Regression Model
•Source code: TensorFlow
•Project 3: Image Prediction Model with MNIST
•Intro & Demo: Simple MNIST
•Topics List and Intro to MNIST Data
•Building Computational Graph
•Training and Testing Model
•Saving Graph for Android Import
•Setting up Android Studio Project
•Building User Interface
•Loading Digit Images
•Formatting Image Data
•Making Prediction Using Model
•Displaying Results and Summary
•Source code: Simple MNIST
•Project 4: Make an Android app with complex MNIST
•Intro & Demo: Advanced MNIST
•Building Neuron Functions
•Building the Convolutional Layers
•Dense, Dropout, & Readout Layers
•Loss & Optimizer + Training & Testing
•How to Optimize a Saved Graph
•Setting up Android Project
•Setting up UI
•Load and Display Digit Images
•Formatting Model Input
•Source code: Advanced MNIST
•Project 5: Make a Weather Prediction app
•Intro & Demo: Weather Prediction
•Tasks List
•Retrieving Data
•Formatting Datasets
•Writing, Training, & Evaluating Functions
•Training, Testing, and Freezing Model
•Build App Backend and Project Summary
•Source code: weather prediction
•Project 5: Fraud Detection (Credit Card)
•New Location to Download Dataset
•Introducing a Dataset
•Building Training: Testing Datasets
•Eliminating Dataset Bias
•Building a Computational Graph
•Building Functions to Connect Graph
•Training the Model
•Testing the Model
•Source code: credit card fraud detection
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