Science Knowings: JavaScript Course For Social Media

Machine Learning in JavaScript

Transitioning from Augmented Reality to Machine Learning

Welcome! We've explored the fascinating world of Augmented Reality. Today, we embark on a new adventure into Machine Learning in JavaScript.

Introduction to Machine Learning

Machine Learning empowers computers to learn from data without explicit programming. It enables them to make predictions and decisions based on patterns and relationships identified from data.

What is Machine Learning?

Machine Learning involves training computer models using data. These models can then perform automated tasks like classification, regression, and prediction.

Types of Machine Learning

There are three primary types of Machine Learning:

  • Supervised learning: Requires labeled data (e.g., input and output pairs) to train models.
  • Unsupervised learning: Learns patterns from unlabeled data, such as clustering and dimensionality reduction.
  • Reinforcement learning: Trains models through trial and error, rewarding or penalizing actions based on outcomes.

Machine Learning Algorithms

Machine Learning employs various algorithms to build models. Some common algorithms include:

  • Linear Regression: Predictive model for continuous variables.
  • Logistic Regression: Binary classification model.
  • Decision Trees: Tree-like models for classification and regression.
  • Neural Networks: Complex models inspired by the human brain, capable of handling non-linear relationships.

TensorFlow.js

TensorFlow.js is a JavaScript library that brings the power of TensorFlow to the browser. It allows you to train and deploy Machine Learning models directly in JavaScript.

Building a Machine Learning Model

Building a Machine Learning model involves:

  • Data collection and preparation: Gathering and cleaning data relevant to the task.
  • Feature engineering: Extracting and transforming features from data to improve model performance.
  • Model selection and training: Choosing an appropriate algorithm and training a model using the prepared data.

Training and Evaluating a Model

After training a model, it's crucial to evaluate its performance. This involves:

  • Metrics selection: Defining metrics (e.g., accuracy, precision, recall) to assess model performance.
  • Training/test split: Dividing data into training and test sets to prevent overfitting.
  • Model tuning: Optimizing model parameters to enhance performance.

Deploying a Model

Once a model is trained and evaluated, it needs to be deployed:

  • Serving infrastructure: Choosing a platform to host and serve the model.
  • API development: Creating APIs to allow other applications to interact with the model.
  • Monitoring and maintenance: Continuously monitoring model performance and making necessary adjustments.

Real-World Applications of Machine Learning

Machine Learning finds applications in various domains, including:

  • Image recognition: Object detection, facial recognition.
  • Natural language processing: Sentiment analysis, machine translation.
  • Predictive analytics: Forecasting demand, risk assessment.
  • Healthcare: Disease diagnosis, drug discovery.

Next Steps: Data Visualization Libraries (D3.js)

To effectively present insights from Machine Learning models, data visualization is crucial. In the next session, we'll explore D3.js, a powerful library for creating interactive data visualizations.