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Machine Learning And Deep Learning With Javascript

TOP 110


Alpha and Omega
Jan 21, 2021
Reaction score
3 years of service

Last updated 6/2019
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.11 GB | Duration: 6h 27m
Learn Machine Learning and Deep Learning from scratch using JavaScript and Tensorflow.js with hands-on projects

What you'll learn
Get acquainted with machine learning and deep learning capabilities using JavaScript and understand the JavaScript Machine Learning ecosystem
Learn JavaScript libraries to build neural network models
Know how to decide, analyze, and make predictions from real-world data
Solve real-world problems such as predicting mental health issues
Use clustering algorithms to understand customer behavior and categorize customers
Train your machine learning models to work with different kinds of data
Work with powerful algorithms using the pre-written libraries in Python
Build deep learning models with TensorFlow .js and practice on realistic datasets
Working knowledge of JavaScript is required.
Machine learning and Deep Learning have been gaining immense traction lately, but until now JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser based JavaScript library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you're a JavaScript developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.This course takes a step by step approach to teach you how to use JavaScript library, TensorFlow.js for performing machine learning and deep learning on a day-to-day basis. Beginning with an introduction to machine learning, you will learn how to create machine learning models, neural networks, and deep learning models with practical projects. You will then learn how to include a pre-trained model into your own web application to detect human emotions based on pictures and voices. You will also learn how to modify a pre-trained model to train the emotional detector from scratch using your own data.Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using JavaScript and the TensorFlow.js library.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Arish Ali started his machine learning journey 5 years ago by winning an all-India machine learning competition conducted by IISC and Microsoft. He was a data scientist at Mu Sigma, one of the biggest analytics firms in India. He has worked on some cutting-edge problems involved in multi-touch attribution modeling, market mix modeling, and Deep Neural Networks. He has also been an Adjunct faculty for Predictive Business Analytics at the Bridge School of Management, which along with Northwestern University (SPS) offers a course in Predictive Business Analytics. He has also worked at a mental health startup called Bemo as an AI developer where his role was to help automate the therapy provided to users and make it more personalized. He is currently the CEO at Neurofy Pvt Ltd, a people analytics startup.Jakub Konczyk has done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share them with others. He first discovered Machine Learning when he was trying to predict real estate prices in one of the early stages startups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to Keep it simple!
Section 1: Hands-On Machine Learning using JavaScript
Lecture 1 The Course Overview
Lecture 2 Introduction to Machine Learning
Lecture 3 Tour of the JavaScript Machine Learning Landscape
Lecture 4 Setting Up Our Machine Learning Environment
Lecture 5 Understand Regression with Linear Regression
Lecture 6 Understanding How Linear Regression Works
Lecture 7 Predicting Salaries after College Using Linear Regression
Lecture 8 Understand Classification with Logistic Regression
Lecture 9 Classifying Clothes Using Logistic Regression
Lecture 10 Model Evaluation
Lecture 11 Better Measures than Accuracy
Lecture 12 Understanding the Results
Lecture 13 Improving the Models
Lecture 14 What are Support Vector Machines?
Lecture 15 Using SVM Kernels to Transform Problems
Lecture 16 Image Classifier Using SVM
Lecture 17 Making Better Decision with Decision Trees
Lecture 18 Combining Decision Trees to Make Better Predictions
Lecture 19 Predicting Customer Churn Using Random Forests
Lecture 20 Introduction and Advantage of Unsupervised Learning
Lecture 21 Grouping Unlabeled Data in Meaningful Ways Using K-means Clustering
Lecture 22 Using Principal Component Analysis to Speed-up Machine Learning Algorithms
Lecture 23 Analyzing Plant Species Using K-means Clustering
Lecture 24 Introduction to Neural Networks
Lecture 25 How a Neural Network Works
Lecture 26 Neural Networks in Tensorflow.js
Lecture 27 Multiclass Classification Using TensorFlow.js
Section 2: Hands-On Machine Learning with TensorFlow.js
Lecture 28 The Course Overview
Lecture 29 Introduction to Machine Learning
Lecture 30 Getting Started with TensorFlow.js Using a Simple Example to Predict Weight
Lecture 31 Setting Up Our Machine Learning Environment
Lecture 32 Types of Supervised Learning
Lecture 33 Applying Regression
Lecture 34 Predicting Salaries after College Using TensorFlow
Lecture 35 Applying Classification
Lecture 36 Predicting Mental Health Issues Using Logistic Regression
Lecture 37 Understanding Simple Neural Networks
Lecture 38 Concepts in Neural Network
Lecture 39 Working with Deep Neural Networks
Lecture 40 Image Classification Using Neural Networks
Lecture 41 Model Evaluation
Lecture 42 Better Measures than Accuracy
Lecture 43 Improving the Models
Lecture 44 Optimizing the Models
Lecture 45 Using High-Level Layers API to Construct Neural Networks
Lecture 46 Building Advanced Neural Networks with Layers Easily
Lecture 47 Detecting Digits Using Layers
Lecture 48 Building A Classifier Using Layers
Lecture 49 Importing a Keras Model into TensorFlow.js
Lecture 50 Saving and Loading TensorFlow Models
Lecture 51 Importing TensorFlow SavedModel into TensorFlow.js
Lecture 52 Playing PAC-MAN Using a Webcam
Section 3: Deep Learning Projects with JavaScript
Lecture 53 The Course Overview
Lecture 54 What Makes Deep Learning in JavaScript Special?
Lecture 55 Getting Started with TensorFlow.js
Lecture 56 Loading Pre-Trained CNN and LSTM Models
Lecture 57 Preparing a New Text for Sentiment Analysis
Lecture 58 Using Loaded Model for Real-Time Text Analysis
Lecture 59 Loading a Set of Pre-Trained CNN Models for Emotion Detection in Photos
Lecture 60 Preparing a New Image for Analysis
Lecture 61 Using Our Models for Photo Emotion Detection
Lecture 62 Loading a Pre-Trained CNN Model for Voice Emotion Detection
Lecture 63 Preparing a New Audio Sample for Analysis
Lecture 64 Using the Loaded CNN Model for Detecting Emotions in Speech
Lecture 65 Create a New Model Based on a Pre-Trained CNN Model
Lecture 66 Getting and Preparing a New Audio Sample for Training and Testing
Lecture 67 Training and Testing the New Model
Lecture 68 Getting and Preparing Audio Sample
Lecture 69 Building a CNN Model for Emotion Detection
Lecture 70 Training and Testing the Model
Lecture 71 Using Trained CNN Model on New Audio Samples
This course is for JavaScript developers interested in Machine Learning and Deep learning. This course is also for data analysts and data scientists who want to explore the possibilities of Machine Learning and Deep Learning using JavaScript.

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KatzSec DevOps

Alpha and Omega
Jan 17, 2022
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2 years of service
TOP salamat sa pag contribute. Next time always upload your files sa
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