Machine Learning for web with JavaScript
About the Program
In the Machine Learning with JavaScript course, students will learn how to implement machine learning algorithms and create intelligent applications using JavaScript. This course is designed to introduce JavaScript developers to the world of machine learning by providing a practical understanding of how to apply machine learning techniques to real-world problems within the browser or Node.js environment.
Students will learn to leverage popular JavaScript libraries like TensorFlow.js, Brain.js, and Synaptic, allowing them to integrate machine learning models into web applications, develop predictive models, and automate tasks based on data-driven insights. The course is highly hands-on, with each module featuring coding exercises and projects that help reinforce the concepts taught.
By the end of the course, students will have the knowledge and skills to build machine learning applications using JavaScript and deploy them to the web or servers. Students will also understand how to train models, preprocess data, and evaluate performance within the context of real-world use cases.
Course Objectives:
- Understand Machine Learning Concepts
- Grasp fundamental machine learning concepts such as supervised and unsupervised learning, classification, regression, and clustering.
- Understand data preprocessing, feature engineering, and evaluation metrics.
- Implement Machine Learning Models with JavaScript
- Use JavaScript libraries like TensorFlow.js and Brain.js to build, train, and test machine learning models.
- Apply models to real-world problems such as image classification, sentiment analysis, and regression tasks.
- Work with Data
- Learn how to gather, clean, and preprocess data for machine learning models.
- Explore and transform datasets for different types of machine learning tasks.
- Develop Web-based Machine Learning Applications
- Integrate machine learning models into web applications using TensorFlow.js.
- Enable real-time predictions, such as recommending products, classifying images, or understanding user behavior.
- Deploy Machine Learning Models
- Understand how to deploy machine learning models in a production environment using Node.js or the browser.
- Learn about model performance evaluation and optimizations.
- Handle Data and Performance
- Implement techniques for improving model performance and efficiency.
- Handle challenges in processing large datasets and running models efficiently in JavaScript environments.
Requirements
At least a core i5 computer, 8GB RAM. Prior experience with JavaScript is required.
Student to Teacher Ratio of 10:1
Machine Learning for web with JavaScript
Curriculum
- What is Machine Learning?
- Types of Machine Learning: Supervised vs. Unsupervised Learning
- Tools and Libraries for Machine Learning in JavaScript (TensorFlow.js, Brain.js, etc.)
- Setting Up Your Development Environment
- Introduction to Data Science Concepts for ML
- Hands-on: Build Your First Machine Learning Model in JavaScript
- Understanding Data for Machine Learning
- Collecting Data: Web Scraping, APIs, Datasets from Kaggle
- Data Cleaning and Preprocessing in JavaScript
- Data Normalization and Transformation
- Feature Engineering and Feature Selection
- Hands-on: Prepare Data for Machine Learning
- Introduction to Supervised Learning
- Linear Regression in JavaScript
- Logistic Regression for Binary Classification
- Decision Trees and Random Forests
- Hands-on: Build a Classification Model Using TensorFlow.js
- Introduction to Unsupervised Learning
- K-Means Clustering
- DBSCAN Clustering Algorithm
- Principal Component Analysis (PCA) for Dimensionality Reduction
- Hands-on: Implement K-Means Clustering in JavaScript
- Introduction to Neural Networks
- Building a Neural Network with TensorFlow.js
- Backpropagation and Training Neural Networks
- Convolutional Neural Networks (CNNs) for Image Classification
- Hands-on: Build and Train a Basic Neural Network in JavaScript
- Introduction to Natural Language Processing (NLP)
- Tokenization, Lemmatization, and Vectorization
- Sentiment Analysis with TensorFlow.js
- Text Classification with Naive Bayes or Neural Networks
- Hands-on: Build a Text Sentiment Analysis Model
- Model Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, AUC
- Cross-Validation and Hyperparameter Tuning
- Model Overfitting and Underfitting
- Strategies for Improving Model Performance
- Hands-on: Evaluate and Optimize a Machine Learning Model
- Implementing Real-Time Predictions in the Browser
- Real-time Image Classification with TensorFlow.js
- Building Interactive Applications Using Machine Learning
- Deploying Machine Learning Models in Web Applications
- Hands-on: Build a Web-based Real-Time Image Classifier
- What is Reinforcement Learning?
- Q-learning Basics and its Application in JavaScript
- Building a Simple RL Agent to Play a Game
- Hands-on: Implement a Simple Reinforcement Learning Agent
- Introduction to Model Deployment with TensorFlow.js
- Running Machine Learning Models on Node.js Server
- Optimizing Models for Performance and Scalability
- Model Maintenance and Retraining
- Hands-on: Deploy a Machine Learning Model on a Node.js Server
Program Expectations
- Gain proficiency in using JavaScript for machine learning tasks.
- Develop practical skills in building, training, and deploying machine learning models.
- Learn how to handle and preprocess data for machine learning projects.
- Build interactive, real-time applications powered by machine learning algorithms.
- Understand how to evaluate and optimize machine learning models in production environments.
This Machine Learning with JavaScript course is ideal for developers who want to add machine learning skills to their toolset and create intelligent applications using JavaScript. Through a combination of theory, hands-on exercises, and a final project, students will be well-prepared to use machine learning to solve problems in the real world.