Course Content
Introduction to AI and Machine Learning
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Overview of AI and ML – History and evolution of AI
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Differences between AI, ML, and Data Science
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Types of Machine Learning : Supervised Learning
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Unsupervised Learning
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Reinforcement Learning
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Semi-supervised Learning
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Applications of AI/ML
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Mathematics for Machine Learning
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Linear Algebra : Vectors, Matrices, and Tensors
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Eigenvalues and Eigenvectors
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Singular Value Decomposition
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Calculus : Differentiation and Integration
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Partial Derivatives and Gradient Descent
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Probability and Statistics : Probability Theory
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Distributions (Normal, Binomial, etc.)
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Bayes’ Theorem
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Optimization Techniques : Cost Function
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Optimization Algorithms
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Programming for AI/ML
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Introduction to Python : Python basics and libraries
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Data Handling : Numpy, Pandas, Matplotlib
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Data Preprocessing and Cleaning
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Introduction to Jupyter Notebooks
00:00
Supervised Learning
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Linear Regression : Simple and Multiple Linear Regression
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Evaluation Metrics
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Classification Algorithms : Logistic Regression
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Decision Trees
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Support Vector Machines (SVM)
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k-Nearest Neighbors (k-NN)
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Model Evaluation : Confusion Matrix
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Precision, Recall, F1-Score
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ROC-AUC Curve
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Unsupervised Learning
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Clustering : k-Means Clustering
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Hierarchical Clustering
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DBSCAN
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Dimensionality Reduction : Principal Component Analysis (PCA)
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t-SNE
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LDA
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Neural Networks and Deep Learning
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Introduction to Neural Networks : Perceptron Model
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Activation Functions
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Deep Learning : Multi-Layer Perceptrons (MLP)
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Backpropagation
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Optimization Techniques (Adam, RMSProp)
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Convolutional Neural : Networks (CNNs) CNN Architecture
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Applications in Image Processing
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Reinforcement Learning
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Introduction to Reinforcement Learning : Markov Decision Processes (MDPs)
00:00
Q-Learning and Deep Q-Networks (DQN)
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Applications in Gaming and Robotics
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AI/ML Model Deployment
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Model Deployment Techniques : Flask, Django, FastAPI
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Cloud Services : AWS, Google Cloud, Microsoft Azure
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Monitoring and Scaling ML Models
00:00
AI/ML
About Lesson

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The goal is to learn a strategy, or policy, that will maximize cumulative reward over time. Here’s a detailed breakdown of how RL works:

Key Concepts in Reinforcement Learning

  1. Agent: The learner or decision-maker that interacts with the environment. The agent takes actions to achieve its goals.

  2. Environment: Everything the agent interacts with. The environment responds to the agent’s actions and provides feedback.

  3. State: A representation of the current situation of the environment. It’s what the agent perceives at a given time.

  4. Action: The choice made by the agent in response to the current state. Actions affect the environment and lead to transitions from one state to another.

  5. Reward: A feedback signal from the environment in response to an action taken by the agent. It indicates how good or bad the action was with respect to the agent’s goals.

  6. Policy: A strategy that the agent follows to determine its actions based on the current state. The policy can be deterministic (a specific action for each state) or stochastic (a probability distribution over actions).

  7. Value Function: A function that estimates the expected cumulative reward (also known as return) that can be obtained from a given state or state-action pair. It helps the agent to evaluate the long-term benefits of its actions.

  8. Q-Function (Action-Value Function): A function that estimates the expected cumulative reward for taking a specific action in a given state and following the policy thereafter. It’s used to help the agent decide which actions to take.

How Reinforcement Learning Works

  1. Initialization: The agent starts with an initial policy and an initial value function (often set to zero or random values).

  2. Interaction: The agent interacts with the environment in discrete time steps. At each step:

    • The agent observes the current state.
    • The agent selects an action based on its policy.
    • The environment responds to the action, transitioning to a new state and providing a reward.

  3. Learning: The agent updates its policy and value function based on the rewards and new state information it receives. This is done using algorithms that adjust the policy to maximize future rewards.

  4. Exploration vs. Exploitation: The agent faces a trade-off between exploring new actions (exploration) and using known actions that have given high rewards in the past (exploitation). Balancing this trade-off is crucial for effective learning.

Types of Reinforcement Learning Algorithms

  1. Model-Free Methods:

    • Value-Based Methods: Focus on estimating the value function or Q-function. Examples include Q-learning and SARSA (State-Action-Reward-State-Action).
    • Policy-Based Methods: Directly optimize the policy without explicitly estimating the value function. Examples include REINFORCE and policy gradient methods.
    • Actor-Critic Methods: Combine value-based and policy-based methods. They use two components: the actor (which updates the policy) and the critic (which evaluates the policy by estimating the value function). Examples include A3C (Asynchronous Actor-Critic Agents) and DDPG (Deep Deterministic Policy Gradient).

  2. Model-Based Methods:

    • Model-Based RL: Involves learning a model of the environment and using it to plan and make decisions. It can be more sample-efficient but requires a good model of the environment.

Applications of Reinforcement Learning

  • Game Playing: RL has been used to achieve superhuman performance in games like Chess, Go, and Dota 2 (e.g., AlphaGo, AlphaZero).
  • Robotics: RL helps robots learn complex tasks through trial and error, such as grasping objects or navigating environments.
  • Finance: Used for algorithmic trading and portfolio management, where the agent learns to make investment decisions to maximize returns.
  • Autonomous Vehicles: RL helps in developing driving policies for self-driving cars to navigate safely and efficiently.

Challenges in Reinforcement Learning

  • Sample Efficiency: RL algorithms often require a large number of interactions with the environment to learn effectively.
  • Exploration vs. Exploitation: Finding the right balance between exploring new strategies and exploiting known ones can be challenging.
  • Scalability: Applying RL to high-dimensional or continuous state-action spaces requires advanced techniques and significant computational resources.
Reinforcement Learning
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