Course Content
Introduction to AI and Machine Learning
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Overview of AI and ML – History and evolution of AI
00:00
Differences between AI, ML, and Data Science
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Types of Machine Learning : Supervised Learning
00:00
Unsupervised Learning
00:00
Reinforcement Learning
00:00
Semi-supervised Learning
00:00
Applications of AI/ML
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Mathematics for Machine Learning
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Linear Algebra : Vectors, Matrices, and Tensors
00:00
Eigenvalues and Eigenvectors
00:00
Singular Value Decomposition
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Calculus : Differentiation and Integration
00:00
Partial Derivatives and Gradient Descent
00:00
Probability and Statistics : Probability Theory
00:00
Distributions (Normal, Binomial, etc.)
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Bayes’ Theorem
00:00
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
00:00
Data Handling : Numpy, Pandas, Matplotlib
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Data Preprocessing and Cleaning
00:00
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)
00:00
Convolutional Neural : Networks (CNNs) CNN Architecture
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Applications in Image Processing
00:00
Reinforcement Learning
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Introduction to Reinforcement Learning : Markov Decision Processes (MDPs)
00:00
Q-Learning and Deep Q-Networks (DQN)
00:00
Applications in Gaming and Robotics
00:00
AI/ML Model Deployment
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Model Deployment Techniques : Flask, Django, FastAPI
00:00
Cloud Services : AWS, Google Cloud, Microsoft Azure
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Monitoring and Scaling ML Models
00:00
AI/ML
About Lesson

1. Artificial Intelligence (AI)

  • Definition: AI is a broad field of computer science focused on creating systems that can perform tasks that normally require human intelligence. These tasks include problem-solving, decision-making, natural language understanding, perception, and even creativity.
  • Scope: AI encompasses a wide range of subfields, including machine learning, robotics, natural language processing (NLP), computer vision, and expert systems.
  • Types of AI:
    • Narrow AI (Weak AI): Designed to perform a specific task (e.g., virtual assistants, recommendation systems).
    • General AI (Strong AI): A theoretical concept where a system would possess the ability to perform any intellectual task that a human can do.
  • Examples:
    • AI-powered chatbots like ChatGPT
    • Autonomous vehicles
    • Image and speech recognition systems

2. Machine Learning (ML)

  • Definition: ML is a subset of AI that focuses on building systems that can learn from and make decisions based on data. Instead of being explicitly programmed for every task, ML algorithms use statistical methods to improve their performance over time.
  • Core Concept: The key idea in ML is that the system identifies patterns in data and uses those patterns to predict outcomes or make decisions.
  • Types of ML:
    • Supervised Learning: The model is trained on labeled data (e.g., spam detection in emails).
    • Unsupervised Learning: The model works on unlabeled data to find hidden patterns (e.g., clustering users based on behavior).
    • Reinforcement Learning: The model learns by interacting with an environment, receiving rewards or penalties (e.g., game-playing AI).
  • Examples:
    • Predictive analytics in finance
    • Personalized recommendations on streaming services
    • Fraud detection in banking

3. Data Science

  • Definition: Data Science is an interdisciplinary field that involves extracting knowledge and insights from data. It combines aspects of statistics, mathematics, programming, and domain knowledge to analyze and interpret complex data.
  • Scope: Data Science involves the entire data lifecycle, from data collection and cleaning to modeling and interpretation. It’s often used to inform business decisions, predict trends, or discover new insights.
  • Key Components:
    • Data Wrangling: Cleaning and preparing data for analysis.
    • Exploratory Data Analysis (EDA): Understanding the underlying patterns, trends, and relationships in data.
    • Statistical Modeling and Algorithms: Building models to predict or explain phenomena.
    • Data Visualization: Representing data graphically to communicate findings.
  • Examples:
    • Analyzing customer behavior to improve marketing strategies
    • Forecasting sales using time series analysis
    • Identifying patterns in large datasets to improve healthcare outcomes

Key Differences

  • Purpose:

    • AI: Develops systems that can perform tasks requiring human-like intelligence.
    • ML: Focuses on creating models that learn from data to make decisions or predictions.
    • Data Science: Analyzes and interprets complex data to extract knowledge and inform decisions.

  • Scope:

    • AI: Broad and includes areas like robotics, expert systems, and ML.
    • ML: A specialized branch of AI centered on learning from data.
    • Data Science: Encompasses a broader range of data-related tasks beyond just ML, including data engineering, analysis, and visualization.

  • Tools and Techniques:

    • AI: Uses a wide range of algorithms and techniques, including ML, rule-based systems, and search algorithms.
    • ML: Relies heavily on algorithms such as decision trees, neural networks, and clustering methods.
    • Data Science: Uses statistical methods, ML algorithms, and tools like Python, R, SQL, and visualization software (e.g., Tableau).

Interrelationships

  • AI and ML: ML is a key technology that powers AI systems. While AI is the broader concept of machines acting intelligently, ML provides the methods for these systems to learn from data.
  • ML and Data Science: ML is a tool within Data Science. Data scientists use ML to create predictive models, but their work also involves data collection, cleaning, analysis, and interpretation.
  • AI and Data Science: Data Science provides the data and insights that can be used to train and improve AI systems. AI can also be applied within Data Science to automate and enhance data analysis.
Differences between AI, ML, and Data Science
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