Phase 1: Foundation Building (Weeks 1–4)

Goal: Understand the core fundamentals and build a strong base.

Step 1: Learn Prerequisites

• Mathematics
• Programming (Python)

Phase 2: Core Machine Learning (Weeks 5–10)

Goal: Learn ML theory and implement key algorithms.

Step 2: Learn Supervised Learning

• Regression
• Classification

Step 3: Learn Unsupervised Learning

• Clustering
• Dimensionality Reduction

Step 4: Model Evaluation & Tuning

• Train-Test Split, Cross-validation
• Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
• Hyperparameter Tuning: Grid Search, Random Search

Phase 3: Data Skills (Weeks 11–14)

Goal: Master data preprocessing, cleaning, visualization.

Step 5: Data Preprocessing

• Handling Missing Values
• Encoding Categorical Variables
• Feature Scaling (Standardization, Normalization)

Step 6: Data Visualization & EDA

Phase 4: Advanced ML & Special Topics (Weeks 15–20)

Goal: Learn techniques to enhance model performance and real-world applications

Step 7: Feature Engineering & Selection

Step 8: Model Deployment (Real World Ready)

Phase 5: Deep Learning & Neural Networks (Weeks 21–28)

Goal: Dive into neural networks and computer vision/NLP basics.

Step 9: Deep Learning Fundamentals

Step 10: Deep Learning Libraries

Phase 6: Projects & Real-World Applications

Step 11: Build End-to-End ML Projects

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