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Weeks 1-2
Basics of Python programming
Introduction to essential libraries (NumPy, Pandas)
Understanding data types and structures
Weeks 3-4
Exploratory Data Analysis (EDA)
Visualization tools (Matplotlib, Seaborn)
Techniques to handle missing data
Weeks 5-6
Introduction to supervised and unsupervised learning
Algorithms overview (Linear Regression, Decision Trees, Clustering)
Model evaluation techniques
Weeks 7-10
Feature engineering and model optimization
Deep dive into Neural Networks and Deep Learning
Time-series analysis and natural language processing (NLP)
Weeks 11-24
Collaborating on GitHub and participating in Kaggle competitions
Working on real-world projects
Building a strong portfolio for job readiness
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