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About This Machine Learning (ML)

Machine Learning (ML) is a subfield of artificial intelligence (AI) that empowers computers to learn from data without explicit programming. It focuses on developing algorithms that can identify patterns, make predictions, and improve their performance over time. ML has revolutionized various industries, from healthcare and finance to marketing and entertainment, by enabling data-driven decision-making and automation.

  • Learning Objectives

  • Supervised Learning: Training models on labelled data to make predictions or classifications.
  • Unsupervised Learning: Discovering hidden patterns and structures in unlabelled data.
  • Reinforcement Learning: Training agents to make decisions in an environment through trial and error.
  • Deep Learning: Using artificial neural networks with multiple layers to learn complex representations from data.
  • Data Pre-processing: Cleaning, transforming, and preparing data for ML models.
  • Model Evaluation: Assessing the performance of ML models using appropriate metrics.

A structured framework is essential for successful Machine Learning (ML) :

  • Problem Definition:
  • ◦ Clearly define the business problem or question to be addressed.
  • ◦ Identify the target variable and relevant features.
    • Data Collection and Preparation:
  • ◦ Gather relevant data from various sources.
  • ◦ Clean, preprocess, and transform the data.
  • ◦ Split the data into training, validation, and testing sets.
    • Model Selection and Training:
  • ◦ Choose appropriate ML algorithms based on the problem type and data characteristics.
  • ◦ Train the models using the training data.
  • ◦ Tune hyperparameters to optimize model performance.
    • Model Evaluation and Validation:
  • ◦ Evaluate the models using appropriate metrics (e.g., accuracy, precision, recall, F1-score).
  • ◦ Validate the models using the validation set.
  • ◦ Identify and address any overfitting or underfitting issues.
    • Model Deployment and Monitoring:
  • ◦ Deploy the trained model to a production environment.
  • ◦ Monitor model performance and retrain as needed.
  • ◦ Integrate the model with existing systems or applications.
    • Results Interpretation and Communication:
  • ◦ Interpret the model's results and communicate them to stakeholders.
  • ◦ Provide insights and recommendations based on the findings.
  • ◦ Document the entire process.

    • FAQ

    Supervised, unsupervised, and reinforcement learning.

    AI is a broader field, while machine learning is a subset that focuses on learning from data.

    Linear regression, logistic regression, decision trees, random forests, support vector machines,1 and neural networks.

    A subfield of machine learning that uses deep neural networks with multiple layers.

    The process of cleaning, transforming, and preparing data for machine learning models.

    Using metrics like accuracy, precision, recall, F1-score, and AUC-ROC.