All_ML_session

Introduction to Data Science & AI

Overview

This course provides a comprehensive introduction to Data Science, Artificial Intelligence (AI), and Machine Learning (ML). It covers foundational concepts, practical tools, and real-world applications, with a focus on Python programming. By the end of the course, you will be equipped to build, deploy, and interpret AI models.

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Course Structure

1. Introduction to Data Science & AI

   • Overview of Data Science, AI, and ML.
   • Real-world applications.
   • Role of Python in Data Science & AI.
   • Setting up the Python environment (Anaconda, Jupyter, VS Code).

2. Python for Data Science & AI

   • Python basics: Variables, Data Types, Operators.
   • Control Structures: Loops and Conditional Statements.
   • Functions, Modules, and File Handling.
   • Exception Handling & Best Practices.

3. Data Handling with NumPy & Pandas

   • Introduction to NumPy: Arrays, Operations, Broadcasting.
   • Pandas for Data Manipulation: Series, DataFrames.
   • Data Cleaning: Handling missing values, duplicates.
   • Data Transformation: Merging, Grouping, Pivoting.

4. Data Visualization

   • Matplotlib for Basic Plots (Line, Bar, Scatter, Pie).
   • Seaborn for Statistical Data Visualization.
   • Interactive Visualization with Plotly.

5. Exploratory Data Analysis (EDA)

   • Understanding Data Distributions.
   • Outlier Detection & Handling.
   • Feature Engineering & Scaling Techniques.
   • Correlation Analysis & Insights Extraction.

6. Introduction to Machine Learning

   • Supervised vs Unsupervised Learning.
   • ML Workflow: Problem Statement, Data Processing, Model Building.
   • Bias-Variance Tradeoff & Performance Metrics.
   • Overview of ML Libraries (Scikit-Learn, TensorFlow, PyTorch).

7. Regression Analysis

   • Linear Regression: Model, Assumptions, Implementation.
   • Multiple Linear Regression & Polynomial Regression.
   • Regularization Techniques: Ridge & Lasso.
   • Evaluating Regression Models.

8. Classification Techniques

   • Logistic Regression & Decision Boundaries.
   • k-Nearest Neighbors (k-NN) Algorithm.
   • Decision Trees & Random Forests.
   • Performance Metrics: Accuracy, Precision, Recall, AUC-ROC.

9. Feature Engineering & Selection

   • Handling Categorical Variables: Encoding Techniques.
   • Feature Scaling: Normalization & Standardization.
   • Feature Selection: PCA, LDA, Feature Importance.
   • Handling Imbalanced Data.

10. Ensemble Learning & Model Stacking

    • Bagging: Random Forest.
    • Boosting: AdaBoost, Gradient Boosting, XGBoost.
    • Stacking & Blending Techniques.
    • Hyperparameter Tuning with GridSearchCV & RandomizedSearchCV.

11. Unsupervised Learning

    • Clustering: k-Means, Hierarchical, DBSCAN.
    • Dimensionality Reduction: PCA, t-SNE, Autoencoders.

12. Natural Language Processing (NLP)

    • Text Processing: Tokenization, Lemmatization, Stemming.
    • Bag-of-Words & TF-IDF.
    • Sentiment Analysis & Text Classification.
    • Advanced NLP: Transformers, BERT, GPT.

13. Deep Learning

    • Neural Networks Fundamentals.
    • Convolutional Neural Networks (CNNs) for Image Processing.
    • Recurrent Neural Networks (RNNs) & LSTMs for Time-Series Data.
    • Generative AI & GANs.

14. Model Deployment & MLOps

    • Saving & Loading Models.
    • Deployment with Flask & FastAPI.
    • CI/CD Pipelines for ML Models.
    • Monitoring & Maintaining ML Models.

15. Advanced Topics

    • Time Series Analysis & Forecasting.
    • Reinforcement Learning (RL) Basics.
    • AI for Business Decision-Making.
    • Edge AI & IoT Applications.

16. Ethics & Compliance

    • Explainable AI: SHAP & LIME.
    • Ethical AI & Bias in Machine Learning.
    • GDPR, HIPAA, and AI Compliance.

17. Capstone Project

    • Hands-on Real-world Project.
    • Model Deployment & Performance Evaluation.
    • Presentation & Peer Review.
    • Certification & Career Guidance.

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Tools & Libraries

• Python Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, PyTorch.
• NLP Libraries: NLTK, SpaCy, Hugging Face Transformers.
• Deployment Tools: Flask, FastAPI, Docker.
• Cloud Platforms: AWS, Azure, Google Cloud.

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Prerequisites

• Basic programming knowledge (preferably Python).
• Familiarity with high school-level mathematics (linear algebra, probability).

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Learning Outcomes

By the end of this course, you will:

• Understand the fundamentals of Data Science, AI, and ML.
• Be proficient in Python for data analysis and machine learning.
• Build, evaluate, and deploy machine learning models.
• Gain hands-on experience with real-world projects.
• Be prepared for a career in Data Science & AI.

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Certification

Upon successful completion of the course and capstone project, you will receive a certificate of completion.

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Contact

For inquiries, please contact [Your Name] at [Your Email].

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License

This course material is licensed under the MIT License.