English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 08m | 534 MB
A quick and easy way for JavaScript developers to begin their journey with machine learning
Machine learning is a growing and in-demand skill, but so far JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. TensorFlow.js is a great way to begin learning machine learning in the browser with TensorFlow.js. It allows you to operate offline to train new models and retrain existing models.
This course covers most of the major topics in machine learning and explains them with the help of Tensorflow.js implementations. The course is focused on the result-oriented nature of most JavaScript developers, and focuses on Tensorflow.js to the fullest in the least amount of time.
At the end of the course, you’ll evaluate and implement the right model to design smarter applications.
This is an application-focused course offering practical and actionable guidance with step-by-step instructions. This course will enable you to develop your own machine learning models and methods to use them efficiently in the browser or a node js server.
What You Will Learn
- 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 and work with different kinds of data
- Work with powerful algorithms using the pre-written libraries in Python
- Recognize images using a webcam
- Get insights into how neural networks work
- Boost your model performance
Table of Contents
Getting Ready for Machine Learning
1 The Course Overview
2 Introduction to Machine Learning
3 Getting Started with TensorFlow.js Using a Simple Example to Predict Weight
4 Setting Up Our Machine Learning Environment
Using Supervised Learning for Predictions
5 Types of Supervised Learning
6 Applying Regression
7 Predicting Salaries after College Using TensorFlow
8 Applying Classification
9 Predicting Mental Health Issues Using Logistic Regression
Deep Neural Networks
10 Understanding Simple Neural Networks
11 Concepts in Neural Network
12 Working with Deep Neural Networks
13 Image Classification Using Neural Networks
Making Our Models Better
14 Model Evaluation
15 Better Measures than Accuracy
16 Improving the Models
17 Optimizing the Models
Building Advance Models Easily with Layers
18 Using High-Level Layers API to Construct Neural Networks
19 Building Advanced Neural Networks with Layers Easily
20 Detecting Digits Using Layers
21 Building A Classifier Using Layers
Handling Models in TensorFlow
22 Importing a Keras Model into TensorFlow.js
23 Saving and Loading TensorFlow Models
24 Importing TensorFlow SavedModel into TensorFlow.js
25 Playing PAC-MAN Using a Webcam
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