Study Notes - January 2025
Introduction to Machine Learning
Personal study notes on ML fundamentals
Key Insight: Machine Learning is about teaching computers to learn
patterns from data without being explicitly programmed for every
scenario.
1. What is Machine Learning?
Machine Learning (ML) is a subset of AI that enables systems to learn and
improve from experience. Instead of following pre-programmed rules, ML
algorithms build mathematical models based on training data.
Remember: ML = Pattern Recognition + Prediction
Core Components:
Data - The fuel for ML models
Features - Measurable properties of the phenomena
Algorithm - The learning method
Model - The output that makes predictions
2. Types of Machine Learning
A. Supervised Learning
Learning with labeled data - like learning with a teacher!
Example: Email spam detection
Input: Email content → Output: Spam/Not Spam
Training: Learn from thousands of pre-labeled emails
Common Algorithms:
Linear Regression (continuous values)
Logistic Regression (classification)
Decision Trees
Random Forests
Support Vector Machines (SVM)
B. Unsupervised Learning
Finding hidden patterns without labels - self-discovery!
Example: Customer segmentation
Input: Customer purchase history
Output: Natural groupings of similar customers
Common Techniques:
K-Means Clustering
Hierarchical Clustering
PCA (Principal Component Analysis)
Autoencoders
C. Reinforcement Learning
Learning through trial and error with rewards/penalties
Think of it like training a dog - reward good behavior, discourage bad
behavior!
3. The ML Workflow
1. Problem Definition - What are we trying to solve?
2. Data Collection - Gather relevant data
3. Data Preprocessing
Handle missing values
Remove outliers
Normalize/Standardize
4. Feature Engineering - Create meaningful features
5. Model Selection - Choose appropriate algorithm
6. Training - Feed data to the algorithm
7. Evaluation - Test on unseen data
8. Deployment - Put into production
9. Monitoring - Track performance over time
4. Key Concepts to Remember
Bias vs. Variance Trade-off
Total Error = Bias² + Variance + Irreducible Error
High Bias = Underfitting (too simple)
High Variance = Overfitting (too complex)
Goal: Find the sweet spot!
Training, Validation, and Test Sets
Golden Rule: Never touch test data until final evaluation!
Training Set (60%) - For learning patterns
Validation Set (20%) - For tuning hyperparameters
Test Set (20%) - For final evaluation
Cross-Validation
K-fold cross-validation helps maximize data usage:
1. Split data into K folds
2. Train on K-1 folds, validate on 1
3. Repeat K times
4. Average the results
5. Evaluation Metrics
For Classification:
Accuracy = (TP + TN) / Total
Precision = TP / (TP + FP) - "Of all positive predictions, how many
were correct?"
Recall = TP / (TP + FN) - "Of all actual positives, how many did we
catch?"
F1 Score = 2 × (Precision × Recall) / (Precision + Recall)
For Regression:
MSE (Mean Squared Error)
RMSE (Root Mean Squared Error)
MAE (Mean Absolute Error)
R² (Coefficient of Determination)
6. Common Pitfalls & Tips
Personal Reminders:
Always start simple - baseline models first!
More data usually > fancier algorithms
Feature engineering is often more impactful than model selection
Don't forget to check for data leakage!
Correlation ≠ Causation
7. Real-World Applications
ML is everywhere nowadays:
Healthcare: Disease diagnosis, drug discovery
Finance: Fraud detection, credit scoring
Retail: Recommendation systems, demand forecasting
Transportation: Autonomous vehicles, route optimization
Entertainment: Content recommendations (Netflix, Spotify)
8. Tools & Libraries to Master
Python Ecosystem:
scikit-learn - Swiss army knife of ML
pandas - Data manipulation
numpy - Numerical computing
matplotlib/seaborn - Visualization
TensorFlow/PyTorch - Deep learning
9. Next Steps in My Learning Journey
To-Do List:
1. Complete Andrew Ng's ML Course
2. Build 3 end-to-end projects
3. Participate in Kaggle competitions
4. Deep dive into Neural Networks
5. Learn about MLOps and deployment
Key Takeaway: Machine Learning is not magic - it's mathematics and
statistics applied cleverly to data. The real skill is knowing when and how
to apply it!
Remember: "In ML, there's no free lunch - every algorithm has its trade-
offs!"
