Your First Machine Learning Projects
Apply your knowledge through hands-on projects with real datasets.
Learning Objectives
- ✓Build a house price prediction model (regression)
- ✓Create a handwritten digit classifier (classification)
- ✓Perform basic sentiment analysis on text data
- ✓Learn model evaluation and improvement techniques
Getting Started with Real Projects
Why Projects Matter
Theory is important, but nothing beats hands-on experience. These projects will give you practical skills and confidence to tackle real-world machine learning problems.
Learning by Doing: Each project introduces new concepts while reinforcing what you've learned. You'll make mistakes, debug issues, and celebrate successes - just like real data scientists!
Regression
Predict continuous values like house prices
Classification
Categorize data like digit recognition
NLP
Analyze text sentiment and meaning
Project 1: House Price Prediction
Regression problem - predicting continuous values
The Challenge:
Given features like square footage, number of bedrooms, location, and age of a house, predict its selling price. This is a classic regression problem used by real estate companies.
What You'll Learn:
Data Skills:
- • Loading and exploring datasets
- • Handling missing values
- • Feature engineering and selection
- • Data visualization techniques
ML Skills:
- • Linear regression implementation
- • Train/validation/test splits
- • Model evaluation metrics (RMSE, MAE)
- • Hyperparameter tuning
🛠️ Tools You'll Use:
📋 Step-by-Step Guide:
- 1. Download the Boston Housing dataset or California Housing dataset
- 2. Explore the data: check for missing values, outliers, and correlations
- 3. Visualize relationships between features and target price
- 4. Prepare the data: handle missing values, scale features if needed
- 5. Split data into training and testing sets
- 6. Train a linear regression model
- 7. Evaluate performance using RMSE and R² score
- 8. Try improving with feature engineering or different algorithms
Project 2: Handwritten Digit Classification
Classification problem - categorizing images
The Challenge:
Given 28x28 pixel images of handwritten digits (0-9), classify which digit each image represents. This is the "Hello World" of computer vision and the foundation for more complex image recognition.
What You'll Learn:
Image Processing:
- • Working with image data
- • Pixel normalization
- • Image visualization
- • Data augmentation basics
Classification:
- • Multi-class classification
- • Confusion matrices
- • Accuracy, precision, recall
- • Neural network basics
🎯 Expected Results:
With a simple neural network, you should achieve 95%+ accuracy. With a convolutional neural network (CNN), you can reach 99%+ accuracy - better than many humans!
📋 Step-by-Step Guide:
- 1. Load the MNIST dataset (built into most ML libraries)
- 2. Visualize some sample digits to understand the data
- 3. Normalize pixel values (divide by 255)
- 4. Reshape data for your chosen algorithm
- 5. Start with a simple model (logistic regression or SVM)
- 6. Evaluate using accuracy and confusion matrix
- 7. Try a neural network for better performance
- 8. Experiment with different architectures and parameters
Project 3: Sentiment Analysis
NLP problem - understanding text emotions
The Challenge:
Analyze movie reviews or social media posts to determine if the sentiment is positive, negative, or neutral. This is widely used by companies to understand customer feedback and social media monitoring.
What You'll Learn:
Text Processing:
- • Text cleaning and preprocessing
- • Tokenization and stop word removal
- • Bag of words and TF-IDF
- • Handling different text formats
NLP Techniques:
- • Feature extraction from text
- • Text classification algorithms
- • Handling imbalanced datasets
- • Model interpretation for text
🎭 Real-World Applications:
📋 Step-by-Step Guide:
- 1. Get a dataset (IMDB movie reviews, Twitter sentiment, etc.)
- 2. Clean the text: remove HTML tags, special characters, etc.
- 3. Tokenize and remove stop words
- 4. Convert text to numerical features (TF-IDF or word embeddings)
- 5. Split into training and testing sets
- 6. Train a classifier (Naive Bayes, SVM, or Neural Network)
- 7. Evaluate using accuracy, precision, recall, and F1-score
- 8. Test on your own text examples
Model Evaluation and Improvement
Beyond Basic Accuracy
Accuracy alone doesn't tell the whole story. Learn to evaluate models comprehensively and identify areas for improvement.
📊 Evaluation Metrics
🔧 Improvement Techniques
🎯 The Model Improvement Cycle
🎯 Hands-On Challenge
Choose one project to complete this week. Start with the one that interests you most!
Your Mission:
Recommended Resources
Kaggle Learn
Free micro-courses with hands-on coding exercises
Google Colab
Free cloud-based Jupyter notebooks with GPU access
Scikit-learn Examples
Comprehensive examples for every ML algorithm
Python Machine Learning Tutorials
Step-by-step video tutorials for practical projects
Hands-On Machine Learning
Code examples from the popular ML book by Aurélien Géron