Intro to Machine Learning

What is Machine Learning?

• Machine learning involves computers learning from data to identify patterns and predict outcomes.
• It uses algorithms to learn without explicit programming.
• It is used for tasks like spam filtering, product recommendations, credit risk assessment, fraud detection, and medical diagnosis.

Types of Machine Learning

• Supervised learning algorithms learn from labeled data to predict target variables for new data.
  • Includes classification algorithms like Logistic Regression, SVM, Decision Trees, and Random Forest.
  • Also includes regression algorithms like Linear and Polynomial Regression.
• Unsupervised learning uses unlabeled data to discover patterns and relationships.
  • Includes clustering algorithms like K-Means and Hierarchical Clustering.
  • Also includes dimensionality reduction via Principal Component Analysis (PCA).
  • Also includes association rule learning algorithms like Apriori and Eclat.
• Reinforcement learning involves algorithms learning through interaction with an environment.
  • This method uses rewards or penalties to optimize the agent's behavior in tasks like game playing and robotics.

Machine Learning Process

• Data is collected from various sources.
• Data preprocessing involves cleaning and transforming data.
• Feature engineering improves model performance by selecting, creating, and transforming features.
• Model selection involves choosing an appropriate machine learning algorithm.
• Models are trained using preprocessed data.
• Evaluation uses appropriate metrics to assess performance.
• Hyperparameter tuning optimizes model parameters.
• Trained models are deployed to predict on new data.
• Monitoring ensures continuous performance and retraining as needed.

Key Concepts

• Features are input variables used for predictions.
• The target variable is what the model aims to predict.
• A model mathematically represents relationships between features and the target variable.
• Training data is used to train the model.
• Validation data evaluates model performance during training.
• Test data evaluates the final model’s performance on unseen data.
• Overfitting: models perform poorly on new data due to learning the training data too well.
• Underfitting: models are too simple to capture underlying patterns in the data.

Tools and Libraries

• Python is frequently used for machine learning.
• Scikit-learn is a library for various machine learning tasks.
• TensorFlow is for deep learning.
• Keras provides a high-level API for building neural networks.
• PyTorch is another library for deep learning.
• Pandas is used for data manipulation.
• NumPy is used for numerical computing.
• Matplotlib is used for data visualization.

Challenges

• Data quality issues can negatively impact model performance.
• Overfitting can cause models that are too complex to fail when generalizing to new data.
• Interpretability challenges arise when understanding a model's decision-making process is difficult.
• Scalability challenges occur when training and deploying models on large datasets becomes computationally expensive.