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Learning Path
Day 1: Python Basics (1.5 Hours + 30 Minutes Introduction)
Topics Covered (1 hour):
○ Introduction to Python: Syntax, variables, and data types.
○ Control structures: Loops (for, while), conditionals (if-else).
○ Writing and using functions.
Hands-on Exercise (0.5 hours):
○ Write basic Python code and practice loops and conditionals.
Day 2: Python Data Structures (1.5 Hours)
Topics Covered (1 hour):
○ Core data structures: Lists, tuples, sets, dictionaries.
Hands-on Exercise (0.5 hours):
○ Perform operations using Python’s data structures.
Day 3: Introduction to Python Libraries (1.5 Hours)
Topics Covered (1 hour):
○ NumPy: Arrays, indexing, basic operations.
○ Pandas: DataFrames, data cleaning, and manipulation.
Hands-on Exercise (0.5 hours):
○ Load and manipulate datasets with Pandas.
○ Perform basic array operations with NumPy.
Day 4: Data Visualization (1.5 Hours)
Topics Covered (1 hour):
-
Visualizing data with Matplotlib.
-
Creating advanced plots using Seaborn.
Hands-on Exercise (0.5 hours):
-
Generate histograms, scatterplots, and line plots.
-
Visualize relationships in a dataset.
Day 5: Introduction to Machine Learning (1.5 Hours)
Topics Covered (1 hour):
○ Overview of machine learning: Supervised vs. unsupervised learning.
○ Understanding datasets: Features, labels, train-test split.
○ Introduction to the scikit-learn library.
Hands-on Exercise (0.5 hours):
○ Explore a dataset and split it into training and testing sets.
Day 6: Supervised Learning - Regression (1.5 Hours)
Topics Covered (1 hour):
○ Introduction to regression: Linear regression basics.
○ Evaluation metrics: Mean Absolute Error (MAE), R-squared.
Hands-on Exercise (0.5 hours):
○ Build and evaluate a linear regression model on a dataset.
Day 7: Supervised Learning - Classification (1.5 Hours)
Topics Covered (1 hour):
○ Introduction to classification: Logistic regression basic
○ Model evaluation: Accuracy, precision, recall, F1 score.
Hands-on Exercise (0.5 hours):
○ Implement logistic regression on a classification dataset
○ Interpret evaluation metrics.
Day 8: Introduction to Unsupervised Learning (1.5 Hours)
Topics Covered (1 hour):
○ Basics of clustering: K-Means algorithm.
○ Dimensionality reduction: Principal Component Analysis (PCA).
Hands-on Exercise (0.5 hours):
○ Perform clustering on a dataset and visualize clusters.
Day 9: Real-World Case Study (1.5 Hours)
Topics Covered (1 hour):
○ End-to-end ML pipeline: Data preprocessing, feature engineering, model
training.
○ Applying supervised and unsupervised learning techniques.
Hands-on Exercise (0.5 hours):
○ Work on a real dataset (e.g., customer segmentation or price prediction).
Day 10: Wrap-Up and Next Steps (1.5 Hours)
Topics Covered (1 hour):
Capstone Project:
○ At the end of the program, participants will complete a small project to
demonstrate their understanding of AI concepts and how to apply them.
Projects will be assessed based on creativity, technical execution, and
real-world relevance.
Q&A session for participants.
○ Provide a Roadmap document for further learning: Advanced Python, deep
learning, or NLP.
Hands-on Exercise (0.5):
○ Final project presentations and discussions. (5 min presentation for each
student)
Completion Certification
○ Participants who complete the course and capstone project will receive a
certificate of completion, which they can add to their resumes or Linked
profiles
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