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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
      Machine Learning and Data Science Essentials with Python & R
      • Linear Algebra
        • Notations and Definitions 11:14 Preview
        • Introduction 07:10 Preview
        • Operations on matrices and vectors 11:58
        • Matrix properties, inverse and transpose 15:04
        • Introduction matrix operations on R 11:45
        • Introduction to matrix operations on python 11:23
      • Machine Learning and Linear Regression
        • Introduction to machine learning 14:42 Preview
        • Linear Regression 1 22:47
        • Linear Regression 2 14:06
        • Linear Regression with python & tensorflow 11:25
        • Linear Regression with R 30:40
      • Logistic Regression
        • Classification and logistic regression 11:40 Preview
        • Decision Boundary 11:34
        • Cost function for logistic regression 14:55
        • Logistic Regression with python & tensorflow 32:09
        • Logistic Regression with R 31:09
      • Problems and Solution
        • Multi-Class, under-fitting and over-fitting 13:49
        • Regularization 12:58
      • Clustering
        • K-means and K-NN algorithm 14:23
        • K-means and K-NN algorithm on R 19:02
        • K-NN on python using tensorflow 12:44
    • Video
      Machine Learning for Apps
      • Introduction to course
        • What is Machine Learning? 07:46 Preview
        • Basics of Machine Learning 06:34 Preview
        • Installing Anaconda / Python Environment 07:25
        • Downloading / Setting Up Atom & Plugins 09:02
      • Python Basics
        • Variables in Python 08:24 Preview
        • Functions, Conditionals, & Loops in Python 09:50
        • Arrays & Tuples in Python 13:52
        • Importing Modules in Python 05:22
      • Building a Classification Model
        • What is scikit-learn? Why use it? 03:52 Preview
        • Installing scikit-learn & scipy with Anaconda 03:28
        • Intro to the Iris Dataset 03:28
        • Datasets: Features & Labels Explained 07:39
        • Loading the Iris Dataset / Examining & Preparing Data 09:27
        • Creating / Training a KNeighborsClassifier 09:42
        • Testing Prediction Accuracy with Test Data 12:08
        • Building Our Own KNeighborsClassifier 18:00
      • Building a Convolutional Neural Network
        • What is Keras? Why use it? 08:01 Preview
        • What is a Convolutional Neural Network (CNN)? 26:30
        • Installing Keras with Anaconda 04:38
        • Preparing Dataset for a CNN 17:38
        • Building / Visualizing a CNN using Sequential: Part 1 14:07
        • Building / Visualizing a CNN using Sequential: Part 2 19:40
        • Training CNN / Evaluating Accuracy / Saving to Disk 17:53
        • Switching Python Environments / Converting to Core ML Model 13:39
      • Building a Handwriting Recognition App
        • Intro to App – Handwriting 02:56
        • Building Interface / Wiring Up 11:42
        • Drawing On Screen 21:01
        • Importing Core ML Model / Reading Metadata 05:16
        • Utilizing Core ML / Vision to Make Prediction 17:31
        • Handling / Displaying Prediction Results 15:13
      • Core ML Basics
        • Intro to App – Core ML Photo Analysis 04:25
        • What is Machine Learning? 07:46
        • What is Core ML? 05:03
        • Creating Xcode Project 02:43
        • Building ImageVC in Interface Builder / Wiring Up 07:40
        • Creating ImageCell & Subclass / Wiring Up 08:13
        • Creating FoodItems Helper File 07:02
        • Creating Custom 3x3 Grid UICollectionViewFlowLayout 09:12
        • Choosing, Downloading, Importing Core ML Model 05:18
        • Passing Images Through Core ML Model 12:18
        • Handling Core ML Prediction Results 09:42
        • Challenge – Core ML Photo Analysis 01:15
      • Downloadable Materials
        • Downloadable Materials
    • Video
      CNN for Computer Vision with Keras and TensorFlow in Python
      • Introduction
        • Introduction 03:29 Preview
        • Course resources
      • Setting up Python and Jupyter Notebook
        • Installing Python and Anaconda 03:04 Preview
        • Opening Jupyter Notebook 09:06 Preview
        • Introduction to Jupyter 13:26 Preview
        • Arithmetic operators in Python: Python Basics 04:28 Preview
        • Strings in Python: Python Basics 19:07
        • Lists, Tuples and Directories: Python Basics 18:41
        • Working with Numpy Library of Python 11:54
        • Working with Pandas Library of Python 09:15
        • Working with Seaborn Library of Python 08:57
      • Single Cells - Perceptron and Sigmoid Neuron
        • Perceptron 09:47
        • Activation Functions 07:30
        • Python - Creating Perceptron model 14:10
      • 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
        • Installing Tensorflow and Keras 04:04
      • Python - Dataset for classification problem
        • Dataset for classification 07:19
        • Normalization and Test-Train split 05:59
      • Python - Building and training the Model
        • Different ways to create ANN using Keras 01:58
        • Building the Neural Network using Keras 12:24
        • Compiling and Training the Neural Network model 10:34
        • Evaluating performance and Predicting using Keras 09:21
      • Saving and Restoring Models
        • Saving - Restoring Models and Using Callbacks 19:49
      • 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 Python
        • CNN model in Python - Preprocessing 05:42
        • CNN model in Python - structure and Compile 06:24
        • CNN model in Python - Training and results 06:50
      • Analyzing impact of Pooling layer
        • Comparison - Pooling vs Without Pooling in Python 06:20
      • Project : Creating CNN model from scratch
        • Project - Introduction 07:04
        • Data for the project
        • Project - Data Preprocessing in Python 09:19
        • Project - Training CNN model in Python 09:05
        • Project in Python - model results 03:07
      • Project : Data Augmentation for avoiding overfitting
        • Project - Data Augmentation Preprocessing 06:46
        • Project - Data Augmentation Training and Results 06:26
      • Transfer Learning : Basics
        • ILSVRC 04:10
        • LeNET 01:31
        • VGG16NET 02:00
        • GoogLeNet 02:52
        • Transfer Learning 05:15
      • Transfer Learning in Python
        • Project - Transfer Learning - VGG16 19:40
    • 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
      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
    • 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
    • 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 Courses
    • 220+ Lectures
    • 25+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 :

27 hours

6 Video Courses

1 eBooks

Lifetime Access
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