Machine Learning (ML)

Machine Learning (ML) is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data. ML techniques are widely used in various applications, ranging from recommendation systems to autonomous vehicles.

Proficiency in ML allows individuals to analyze data, extract meaningful insights, and build predictive models that drive business value. Understanding ML concepts such as supervised learning, unsupervised learning, and reinforcement learning is essential for developing intelligent systems and solving complex problems.

Moreover, ML is continuously evolving, with advancements in deep learning, neural networks, and probabilistic graphical models pushing the boundaries of what's possible. Learning ML opens up exciting career opportunities in fields such as data science, artificial intelligence, and predictive analytics.

Syllabus:

• 1) Introduction to Machine Learning
• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
• Machine Learning Workflow
• Model Evaluation and Validation
• 2) Supervised Learning
• Regression
• Classification
• Decision Trees and Random Forests
• Support Vector Machines
• 3) Unsupervised Learning
• Clustering
• Dimensionality Reduction
• Association Rule Learning
• Principal Component Analysis (PCA)
• 4) Deep Learning
• Neural Networks Basics
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Generative Adversarial Networks (GANs)
• 5) Reinforcement Learning
• Markov Decision Processes
• Q-Learning and Deep Q-Networks (DQN)
• Policy Gradient Methods
• Actor-Critic Models
• 6) Model Deployment and Scalability
• Deploying ML Models in Production
• Scalability and Performance Optimization
• Monitoring and Maintenance
• Model Interpretability and Explainability