AI & Machine Learning

AI & Machine Learning

14 Weeks
6 Projects
Intermediate
$1,199

Course Overview

Master the fundamentals of artificial intelligence and machine learning

The AI & Machine Learning course is designed for developers and data professionals who want to gain a comprehensive understanding of artificial intelligence and machine learning concepts and techniques. This course covers both theoretical foundations and practical applications of AI and ML.

You'll learn how to build, train, and deploy machine learning models for various applications, from predictive analytics to computer vision and natural language processing. Through hands-on projects and real-world case studies, you'll develop the skills needed to solve complex problems using AI and ML techniques.

By the end of this course, you'll have a solid understanding of machine learning algorithms, neural networks, and deep learning, and be able to apply these techniques to real-world problems.

What You'll Learn

Key topics covered in this course

Machine Learning Fundamentals
Supervised and Unsupervised Learning
Neural Networks
Deep Learning
Computer Vision
Natural Language Processing
Model Deployment

Course Content & Information

Detailed curriculum and request more information

AI & Machine Learning

Module 1: Introduction to AI and Machine Learning

  • History and Evolution of AI
  • Types of Machine Learning
  • AI Ethics and Responsible AI
  • Python for Machine Learning
  • Project: Data Analysis with Python

Module 2: Supervised Learning

  • Linear and Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines
  • Model Evaluation and Validation
  • Project: Predictive Modeling

Module 3: Unsupervised Learning

  • Clustering Algorithms
  • Dimensionality Reduction
  • Anomaly Detection
  • Association Rule Learning
  • Project: Customer Segmentation

Module 4: Neural Networks and Deep Learning

  • Neural Network Fundamentals
  • Activation Functions and Backpropagation
  • Deep Learning Frameworks (TensorFlow, PyTorch)
  • Convolutional Neural Networks
  • Project: Image Classification

Module 5: Computer Vision

  • Image Processing Fundamentals
  • Object Detection and Recognition
  • Image Segmentation
  • Transfer Learning for Computer Vision
  • Project: Object Detection System

Module 6: Natural Language Processing

  • Text Processing and Tokenization
  • Word Embeddings
  • Recurrent Neural Networks
  • Transformer Models
  • Project: Sentiment Analysis System

Module 7: Model Deployment and MLOps

  • Model Serving and APIs
  • Containerization with Docker
  • Cloud Deployment
  • Monitoring and Maintenance
  • Project: Deploying an ML Model as a Service
Machine Learning Projects

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