Artificial Intelligence & Machine Learning

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Week 1: Introduction to AI and ML (8 hours)

  1. Overview of Artificial Intelligence (AI)
    • Definition and Scope of AI
    • Historical Perspective
  2. Introduction to Machine Learning (ML)
    • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
    • Basics of Data Science
  3. Python for Machine Learning
    • Basics of Python Programming
    • Popular Libraries: NumPy, Pandas, Matplotlib

Week 2: Supervised Learning (8 hours)

  1. Linear Regression
    • Simple Linear Regression
    • Multiple Linear Regression
  2. Classification Algorithms
    • Logistic Regression
    • Decision Trees and Random Forests
    • Support Vector Machines (SVM)
  3. Model Evaluation and Metrics
    • Cross-Validation
    • Confusion Matrix, Precision, Recall, F1 Score

Week 3: Unsupervised Learning (8 hours)

  1. Clustering Algorithms
    • K-Means Clustering
    • Hierarchical Clustering
  2. Dimensionality Reduction
    • Principal Component Analysis (PCA)
  3. Introduction to Neural Networks
    • Basics of Neural Networks
    • Feedforward Neural Networks

Week 4: Advanced Topics and Internship Project (8 hours)

  1. Deep Learning
    • Introduction to Deep Learning
    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN)
  2. Natural Language Processing (NLP)
    • Basics of NLP
    • Text Preprocessing and Analysis

Additional 20 Hours: Internship Projects

  1. Project Development – a comprehensive understanding and preparation. Here’s a detailed breakdown:
  2. Understanding the Scope:
    • Define the Problem Statement: Clearly articulate the problem you aim to solve with your AI ML project. For example, predicting heart failure, image recognition, natural language processing, etc.
    • Scope and Limitations: Define the boundaries of your project to make it manageable within the internship timeframe.
  3. Literature Review:
    • Research Existing Solutions: Explore relevant academic papers, articles, and existing projects to understand the state-of-the-art solutions related to your problem statement.
    • Identify Techniques: Determine the machine learning algorithms and techniques commonly used for similar problems.
  4. Data Collection:
    • Identify Data Sources: Locate datasets relevant to your problem statement. This could involve searching public repositories, collaborating with organizations, or using synthetic datasets.
    • Data Cleaning and Preprocessing: Cleanse and preprocess the data to handle missing values, outliers, and format inconsistencies.
  5. Exploratory Data Analysis (EDA):
    • Data Visualization: Utilize tools like matplotlib or seaborn to visualize relationships and patterns within the data.
    • Statistical Analysis: Perform statistical analyses to gain insights into the characteristics of the dataset.
  6. Feature Engineering:
    • Select Relevant Features: Identify the most influential features for your machine learning models.
    • Create New Features: If necessary, generate new features that may enhance model performance.
  7. Model Selection:
    • Choose Algorithms: Based on your literature review, select machine learning algorithms suitable for your problem. Common choices include regression, classification, clustering, etc.
    • Train Initial Models: Implement and train basic models to establish a baseline.
  8. Model Evaluation:
    • Select Evaluation Metrics: Choose appropriate metrics (accuracy, precision, recall, F1-score) based on the nature of your problem.
    • Cross-Validation: Use techniques like k-fold cross-validation to assess model performance more robustly.
  9. Hyperparameter Tuning:
    • Optimize Models: Fine-tune model hyperparameters to improve performance.
    • Grid Search or Random Search: Employ methods like grid search or random search to find the optimal hyperparameter combinations.
  10. Results Analysis and Documentation:
    • Document Findings: Summarize and document key findings, including insights from EDA, successful models, and challenges faced.
    • Create Visualizations: Develop visual aids to communicate results effectively.
  11. Mock Interview Preparation:
    • Review Project Details: Revisit all project-related documentation, ensuring a clear understanding of each step.
    • Explain Decision-Making: Be prepared to articulate why you made specific choices in terms of algorithms, features, and evaluation metrics.
    • Discuss Challenges: Anticipate potential questions about challenges faced during the project and how you addressed them.
    • Mock Interviews: Conduct practice interviews with peers or mentors to simulate real interview conditions.
  12. Communication Skills:
    • Clarity and Conciseness: Practice explaining complex concepts in a clear and concise manner.
    • Answering Technical Questions: Be prepared to answer technical questions related to machine learning, data preprocessing, and model evaluation.
  13. Mock Interview:
    • Simulate Interview Conditions: Set up a mock interview scenario with a peer or mentor.
    • Feedback and Improvement: Receive constructive feedback and make necessary improvements based on the mock interview experience.
  14. Project Presentation and Feedback (2 hours)
    • Students present their projects
    • Feedback and discussion

Additional Notes:

  • Assignments and hands-on exercises should be given after each topic to reinforce learning.
  • Encourage students to explore real-world datasets and solve practical problems.
  • Provide resources and guidance for further self-study in specialized areas within AI and ML.