Machine Learning And Deep Learning With Javascript

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

Requirements

Working knowledge of JavaScript is required.

Description

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!

Overview

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.