Machine Learning
Introduction
Machine Learning (ML): A subset of AI where algorithms learn from and make predictions on data.
Model: In ML, a model is a representation of an algorithm's learned patterns from data. It's what you use to make predictions on new data.
Deep Learning: A subset of ML using neural networks with many layers. It's more complex and requires more data.
Neural Networks in ML: Traditional machine learning can utilize simpler neural networks, but not all ML algorithms are based on them.
Codeability: Machine learning is "codeable", involving algorithms that learn from data. Libraries simplify this process.
Steps to Create a Machine Learning Algorithm
Data Collection: Gather data relevant to your problem.
Data Preprocessing: Clean and transform data to a usable format.
Feature Engineering: Select or create the most relevant input features.
Model Selection: Choose an appropriate algorithm based on the problem.
Training: Feed the training data to the model to learn patterns.
Evaluation: Test the model's performance on the validation set.
Tuning: Adjust parameters to optimize performance.
Deployment: Implement the model in a real-world application.
Common Algorithms & Their Uses
Linear Regression: Predict continuous values.
Logistic Regression: Classify data into two categories.
Decision Trees: Make decisions based on questions.
Random Forest: Uses multiple decision trees for better accuracy.
K-Means Clustering: Group data into 'k' clusters.
Support Vector Machines (SVM): Classify data by finding the best boundary.
Simple Neural Networks: Used in some ML tasks; less deep than deep learning networks.
Ensemble Methods
Using Multiple Algorithms: Yes, you can combine multiple algorithms to improve predictive performance. This is known as ensemble learning.
Bagging: Uses multiple instances of the same algorithm on different subsets of data.
Boosting: Sequentially applies algorithms, where each one corrects the errors of its predecessor.
Stacking: Combines predictions from multiple algorithms to make a final prediction.
Evaluation Metrics
Accuracy: How often the model is correct.
Precision & Recall: Balance between correctly predicted positives and actual positives.
F1 Score: Combines precision and recall.
Mean Absolute Error (MAE): Average error in regression tasks.
Overfitting & Underfitting
Bias-Variance Tradeoff: Balance between simplicity (bias) and complexity (variance).
Regularization: Add penalty to complex models to prevent overfitting.
Tools & Libraries
Languages: Python and R are popular for ML.
Scikit-learn: Comprehensive Python library for ML.
Pandas: Python library for data manipulation.
NumPy: Python library for numerical operations.
Tips
Always visualize your data.
Start simple and then explore complexity.
Update your model with new data when possible.
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