Machine Learning Basics

Machine Learning (ML) is a crucial area within Artificial Intelligence that enables systems to learn from data without being explicitly programmed. It covers various learning paradigms, including supervised and unsupervised learning, the training and evaluation of models, as well as addressing the bias-variance trade-off. Mastering these principles is fundamental for creating effective machine learning systems that can generalize well to new data.

Sections

  • 6

    Machine Learning Basics

    This section introduces the foundational concepts of Machine Learning, covering its types, components, and evaluation methods.

    Learning Practice

  • 6.1

    Introduction To Machine Learning

    Machine Learning is a subset of Artificial Intelligence that enables systems to learn and make decisions based on data, without explicit programming.

    Learning Practice

  • 6.1.1

    Why Machine Learning?

    Machine Learning automates decision-making based on data, enabling systems to learn and adapt over time, crucial for various applications.

    Learning Practice

  • 6.1.2

    Key Components Of An Ml System

    This section outlines the fundamental components of machine learning systems, defining data, models, algorithms, and predictions.

    Learning Practice

  • 6.2

    Supervised Vs Unsupervised Learning

    This section differentiates between supervised and unsupervised learning techniques in machine learning.

    Learning Practice

  • 6.2.1

    Supervised Learning

    Supervised learning involves algorithms learning from labeled data to map inputs to corresponding outputs.

    Learning Practice

  • 6.2.2

    Unsupervised Learning

    Unsupervised learning is a machine learning paradigm where algorithms analyze unlabeled data to find patterns and structures without prior guidance.

    Learning Practice

  • 6.2.3

    Other Learning Paradigms

    This section introduces semi-supervised and reinforcement learning paradigms, highlighting their unique characteristics and applications in machine learning.

    Learning Practice

  • 6.3

    Model Evaluation And Training

    This section covers essential processes for training machine learning models and evaluating their performance using various metrics.

    Learning Practice

  • 6.3.1

    Training Process

    The training process in machine learning involves inputting data, adjusting model parameters, and evaluating performance to reduce errors.

    Learning Practice

  • 6.3.2

    Evaluation Metrics

    This section covers essential evaluation metrics used to assess machine learning models, focusing on classification and regression metrics.

    Learning Practice

  • 6.3.3

    Cross-Validation

    Cross-validation is a technique used to assess how well a model generalizes to an independent dataset, particularly through methods like k-fold cross-validation.

    Learning Practice

  • 6.4

    Bias-Variance Trade-Off

    The Bias-Variance Trade-off represents the balance between two sources of error in machine learning models: bias, which leads to underfitting, and variance, which leads to overfitting.

    Learning Practice

  • 6.4.1

    What Is Bias And Variance?

    Bias is error from overly simplistic assumptions, while variance is error from model sensitivity to data fluctuations.

    Learning Practice

  • 6.4.2

    The Trade-Off

    The trade-off in machine learning revolves around balancing model complexity to avoid underfitting and overfitting.

    Learning Practice

  • 7

    Conclusion

    The conclusion underscores the importance of understanding fundamental machine learning principles crucial for developing effective AI applications.

    Learning Practice

References

Chapter 6_ Machine Learning Basics.pdf

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What we have learnt

  • Machine Learning is a subfi...
  • Supervised learning involve...
  • The training process includ...

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