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Artificial Intelligence & Machine Learning Prime Pack

6 best courses & a E-book Hand picked to master your skills

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Development, Data Science and AI ML, Artificial Intelligence, Machine Learning

    • Video
      Introduction to Artificial Intelligence: AI for beginners
      • Section 1:Introduction and History of AI
        • Lecture 1:Introduction to Artificial Intelligence 12:12 Preview
        • Lecture 2:Definitions 14:23 Preview
      • Section 2:Present Day AI and its Applications
        • Lecture 3:Characteristics of AI 08:30
        • Lecture 4:AI Applications 07:02
      • Section 3:Categorization and Methodologies
        • Lecture 5:3 Paradigms of AI 08:27
        • Lecture 6:Categories and types of Artificial Intelligence 06:26
        • Lecture 7:Intro to Neural Networks 04:03
      • Section 4:Pros and Cons
        • Lecture 8:Advantages and Disadvantages of AI 04:36
      • Section 5:The Future of AI
        • Lecture 9:AI Promises 08:14
        • Lecture 10:Tangible Projects 05:22
    • Video
      CNN for Computer Vision with Keras and TensorFlow in R
      • Introduction
        • Introduction 03:29 Preview
        • Course resources
      • Setting Up R Studio and R crash course
        • Installing R and R studio 05:52 Preview
        • Basics of R and R studio 10:47 Preview
        • Packages in R 10:52 Preview
        • Inputting data part 1: Inbuilt datasets of R 04:21 Preview
        • Inputting data part 2: Manual data entry 03:11 Preview
        • Inputting data part 3: Importing from CSV or Text files 06:49
        • Creating Barplots in R 13:42
        • Creating Histograms in R 06:01
      • Single Cells - Perceptron and Sigmoid Neuron
        • Perceptron 09:47
        • Activation Functions 07:30
      • Neural Networks - Stacking cells to create network
        • Basic Terminologies 09:47
        • Gradient Descent 12:17
        • Back Propagation 22:27
      • Important concepts: Common Interview questions
        • Some Important Concepts 12:44
      • Standard Model Parameters
        • Hyperparameters 08:19
      • Tensorflow and Keras
        • Keras and Tensorflow 03:04 Preview
        • Installing Keras and Tensorflow 02:54
      • R - Dataset for classification problem
        • Data Normalization and Test-Train Split 12:00
      • R - Building and training the Model
        • Building, Compiling and Training 14:57
        • Evaluating and Predicting 09:46
      • The NeuralNets Package
        • ANN with NeuralNets Package 08:07
      • Saving and Restoring Models
        • Saving - Restoring Models and Using Callbacks 20:16
      • Hyperparameter Tuning
        • Hyperparameter Tuning 09:05
      • CNN - Basics
        • CNN Introduction 07:42
        • Stride 02:51
        • Padding 05:07
        • Filters and Feature maps 07:48
        • Channels 06:31
        • PoolingLayer 05:32
      • Creating CNN model in R
        • CNN on MNIST Fashion Dataset - Model Architecture 02:04
        • Data Preprocessing 07:08
        • Creating Model Architecture 06:04
        • Compiling and training 02:53
        • Model Performance 06:26
      • Analyzing impact of Pooling layer
        • Comparison - Pooling vs Without Pooling in R 04:33
      • Project : Creating CNN model from scratch
        • Project - Introduction 07:04
        • Data for the project
        • Project in R - Data Preprocessing 10:28
        • CNN Project in R - Structure and Compile 04:59
        • Project in R - Training 02:57
        • Project in R - Model Performance 02:22
      • Project : Data Augmentation for avoiding overfitting
        • Project in R - Data Augmentation 07:12
        • Project in R - Validation Performance 02:24
      • Transfer Learning : Basics
        • ILSVRC 04:10
        • LeNET 01:31
        • VGG16NET 02:00
        • GoogLeNet 02:52
        • Transfer Learning 05:15
      • Transfer Learning in R
        • Project - Transfer Learning - VGG16 (Implementation) 12:44
        • Project - Transfer Learning - VGG16 (Performance) 08:02
    • Video
      Automated Multiple Face Recognition AI using Python
      • Introduction
      • Basics of Computer Vision And OpenCv
        • Computer Vision and Introduction to OpenCv 02:12
        • Implementing Basic OpenCv Functionalities 20:42
      • Understanding Face Recognition using face_recognition library
        • Introduction to Face Recognition Library 04:47
        • Understanding and Implementing Face Recognition Library 20:11
      • Project: Automated Multiple Face Detection
        • Project: Part 1 11:45
        • Project: Part 2 21:00
        • Project: Part 3 11:15
      • Future Scope and Face Recognition Market
        • Future Scope and Market Analysis 09:10
    • Video
      Automatic Number Plate Recognition, OCR Web App in Python
      • Introduction
        • Introduction 01:22 Preview
        • Project Architecture 03:02 Preview
        • Download the Resources (Code and Data)
      • Labeling
        • Get the Data 01:27 Preview
        • Download Image Annotation Tool 01:51
        • Install Dependencies 03:26
        • Label Images 02:26
        • XML to CSV 09:56
      • Data Processing
        • Read Data 07:44 Preview
        • Verify Labeled Data 06:05 Preview
        • Data Preprocessing 09:52
        • Split train and test set 03:26
      • Deep Learning for Object Detection
        • Get Transfer Learning from TensorFlow 2.x 02:54
        • InceptionResnet V2 model building 05:57
        • Defining Inputs and Outputs 01:37
        • Compiling Model 02:18
        • InceptionResnet V2 Training 03:29
        • InceptionResnet V2 Training - Part 2 02:06
        • Save Deep Learning Model 02:57
        • Tensorboard 04:03
      • Pipeline Object Detection Model
        • Make Predictions 10:05
        • Make Predictions part2 03:43
        • De-normalize the Output 03:35
        • Bounding Box 04:38
        • Create Pipeline 04:42
      • Optical Character Recognition (OCR)
        • Install Tesseract 04:45
        • Install Pytesseract 02:01
        • Extract Number Plate text from Image 06:24
      • Flask App
        • Install Visual Studio Code 04:26
        • First Flask App 05:59
        • Render HTML Template 06:58
        • Import Boostrap 02:42
      • Number Plate Web App
        • Create Web App 03:22 Preview
        • Footer 01:49
        • Template Inheritance 03:07
        • Upload Form in HTML 03:29
        • HTTP Method Upload File in Flask 07:08
        • Integrate Deep Learning Object Detection Model 13:11
        • Integrate Number Plate Detection and OCR to Flask App 05:52
        • Display Output in HTML Page 08:06
        • Display Output in HTML Page part 2 06:52
    • Video
      AWS Rekognition: Machine Learning Using Python Masterclass
      • Introduction
        • Introduction - Course Agenda & Meet Instructor 02:13 Preview
        • What You Should Have 02:04 Preview
        • Creating a billing alert 05:25 Preview
        • Amazon Machine Learning - Sage Maker *UPDATE*
        • *NEW* AWS Management Console *UPDATED* Lecture 04:11 Preview
      • AWS Machine Learning
        • Your First Machine Learning Project - Complete 18:29 Preview
        • Feedback 01:13
        • Project Clean Up 02:43
        • Your Second Machine Learning Project: Upload Data Set to S3 08:47
        • Creating Training Data Source & Machine Learning Model 10:33
        • Generating Predictions 08:01
        • AWS Billing Quick Check 01:28
        • Download Data Sets For Practice
      • Core AWS Configurations & Fundamentals
        • Machine Learning Fundamentals 12:34
        • AWS ML Pricing 02:17
        • Install AWS CLI 12:50
        • Creating AWS Security Group 09:24
        • Generating Key Pair 04:44
        • Connecting to AWS Instance with Putty For Windows 09:47
      • AWS Rekognition Using Python: In Depth
        • Download & Install PyCharm 08:40
        • Create New AWS User: Programmatic Access 05:04
        • Using Command Line For User Login 04:02
        • Install Boto3: Amazon Web Services (AWS) SDK for Python 05:46
        • Create Images Directory In Pycharm 03:21
        • Uploading Images To Web Server: WordPress 03:16
        • Python Pprint 02:20
        • Creating Helpers File 03:28
        • Fetching Labels From Image 12:38
        • Extracting Just Labels 04:17
        • Face Detection With Python 14:04
        • Using S3 Label Detection 07:18
        • Compare Faces Using Python
        • Compare Face Similarity – Lab2
        • Detect Text In Images
        • Limits Of Amazon Rekognition 05:28
      • Python Programming For Complete Beginners
        • Python Introduction & Agenda 11:23
        • Why Program? 14:12
        • Downloading Python 3.5-11 10:43
        • Downloading Python 3.5 10:43
        • Python Interpreter & Idle 14:22
        • Nuts & Bolts of Program 14:57
        • Python Strings 18:09
        • Getting Input 17:09
        • Reading & Writing Files 21:09
        • Python Expressions 13:00
        • Creating Your First Program Part 1 11:52
        • Creating Your First Program Part 2 14:19
        • Placing Comments in Code 11:53
        • Introduction To Strings Part 1 10:02
        • Introduction To Strings Part 2 10:01
        • What Are Functions? 19:03
        • Print Function 05:19
        • Escape Codes In Python 10:52
        • Input Function 18:40
        • Global Variables 08:46
        • Concept Of Python Dictionaries 16:55
        • Concept of Lists 16:10
        • What Are Tuples? 13:48
        • Introduction to Loops 21:20
        • Working With Graphics 10:25
        • Conditional Execution 09:56
        • IF Statement 14:29
        • Additional IF Statements 12:15
        • The While Loop 13:49
        • Project A 18:19
        • Project B 13:42
      • Python Advanced Programming
        • Introduction 06:02
        • Python Refresher 05:01
        • List Comprehension 20:05
        • Sets & Dictionaries 17:08
        • Looping Techniques 12:52
        • Python Modules 12:25
        • Python Packages 14:35
        • Time Functionality 13:32
        • Graphical User Interface Introduction 04:15
        • Creating Tk Widget 17:13
        • Working With Buttons and Labels 18:07
        • Python Message Box 17:32
        • Radio Button Implementation 08:49
        • Data Entry Widget Creation 11:40
        • Working With Oval Objects 19:51
        • POST and GET Methods 13:51
        • Advanced File Operations 12:33
        • Accessing Internet With Python 12:03
        • Turtle Star 15:14
        • Random Walk 08:58
        • Creating Multimedia 16:51
        • Text Input-Part 1 14:02
        • Text Input-Part 2 15:40
        • Networking With Python 11:40
        • Python Debugger 15:02
        • Advanced Project A 07:49
        • Advanced Project B 19:55
      • Conclusion & Bonus
        • Resources
    • Video
      Heart Attack and Diabetes Prediction Project in Apache Spark (Machine Learning Project)
      • Introduction
      • Download Resources
        • Download Resources
      • Project Begins
        • Introduction to Spark 04:17 Preview
        • Free Account creation in Databricks 01:51
        • Provisioning a Spark Cluster 02:14
        • Introduction to Machine Learning 08:28 Preview
        • Basics about notebooks 07:29
        • Dataframes 04:47
      • Heart Disease Prediction Project
        • Project Explanation Part 1 02:30
        • Project Explanation Part 2 19:41
        • Project Explanation Part 3 35:11
        • Project Explaination Part 4 22:01
        • Project Explaination Part 5 01:59
      • Diabetes Prediction Project
        • Project Explanation Part 1 01:25
        • Project Explanation Part 2 15:13
        • Project Explaination Part 3 15:25
        • Project Explaination Part 4 26:47
        • Project Explaination Part 5 00:20
    • eBook
      Artificial Intelligence with Python
        • Artificial Intelligence with Python Preview
  • Description

    Overview: Hand on Artificial Intelligence, Automated Multiple Face Recognition in an image, Use Python programming to extract text and labels from images using Pycharm, Boto3, and AWS Rekognition Machine Learning, Automatic Number Plate Recognition, Convolutional Neural Networks (CNN) and Deep Learning, Apache Spark using Databricks Notebook.

    • 6 Modules
    • 220+ Lectures
    • 30+Hrs HD Videos
    • 6+ Projects
    • Course Designed by Industry Experts
    • Up-to-Date Curriculum
    • eBooks
    • Full Lifetime Access
    • 30 Days Refund Policy
    • Certificate on Completion


    About the Premium Packs

    At Tutorialspoint, we ensure professional success with our machine learning premium packs. This pack will enable you to gain an in-depth understanding of what has become the brain behind business intelligence and will cover technologies like Machine learning, Regression analysis, Aws software, Deep learning, Regression analysis, Tensorflow, Python, R, Case studies and Projects & much more.


    Scope of Machine Learning

    As of 2021, Data scientists can earn as much as $118,000 a year!

    Machine learning is expanding in all fields such as banking, health, IT, security, HR, etc.

    Tesla’s self-driving car is the best in the industry - it is built by machine learning.


    Projects Overview

    Heart Disease Prediction Project

    Diabetes Prediction Project

    Creating CNN Model from Scratch

    Data Augmentation for Avoiding Overfitting

    Automated Multiface Detection

    Goals

    • You will implement Spark Machine Learning 2 Mini Projects in Apache Spark using Databricks Notebook.
    • Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning.
    • Learn about 4 types of Artificial Intelligence systems
    • Discover 3 Paradigms of AI: Fuzzy Logic, Neural Networks and Genetic Algorithms
    • Face recognition and detection using euclidean distance
    • Successfully use Python to extract text from images and labels
    • Solid understanding of AWS Rekognition
    • Fundamentals of AWS Machine Learning
    • Object Detection from Scratch

    Prerequist

    • Google Chrome (Latest version), Firefox (Latest version), Safari (Latest version), Microsoft Edge* (Latest version)
    • Installation of Python, Anaconda, R and R Studio software (we have a separate lecture to help you install)
    • Familiar with Maths and Computer Science
    • Basics of Python Programming
    • Basics on HTML
Artificial Intelligence & Machine Learning Prime Pack
This Prime Pack Includes :

31.5 hours

6 Video Courses

1 eBooks

Lifetime Access
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