Basics of Machine Learning #100DaysOfML

AI vs ML vs DL:

• Artificial Intelligence (AI):

  • Definition: AI simulates human intelligence to perform tasks and make decisions.

• Machine Learning (ML):

  • Definition: ML is a subset of AI that uses algorithms to learn patterns from data.

• Deep Learning (DL):

  • Definition: DL is a subset of ML that employs artificial neural networks for complex tasks.

Definition of Machine Learning (ML):

• ML enables computers to learn from data and previous experiences without being explicitly programmed.

Types of Machine Learning (ML):

1. Supervised Learning:

   • Definition: In supervised learning, machines are trained using labeled datasets, where inputs are mapped to corresponding outputs.

   • Example: Identifying objects in images based on features like shape, size, and color.

   • Categories:

     • a) Classification: Predicting categorical outputs like spam vs. non-spam emails.

     • b) Regression: Predicting continuous outputs such as stock prices.

• Advantages:

  • Effective for classification and regression tasks.

  • Requires less computational power compared to other types.

• Disadvantages:

  • Relies heavily on labeled data.

  • May overfit if not enough diverse data is available.

2. Unsupervised Learning:

   • Definition: In unsupervised learning, machines are trained using unlabeled datasets, finding patterns and categories without supervision.

• Example: Clustering similar data points together based on their similarities.

• Categories: Clustering, Association, Dimensionality Reduction.

• Advantages:

  • Useful for clustering and anomaly detection.

  • Doesn't require labeled data, making it versatile.

• Disadvantages:

  • Harder to evaluate results due to lack of ground truth labels.

  • Can be computationally intensive for large datasets.

3. Semi-supervised Learning:

   • Definition: Semi-supervised learning uses a mix of labeled and unlabeled data during training, offering a middle ground between supervised and unsupervised learning.

   • Example: Classifying images with a combination of labeled and unlabeled data.

• Advantages:

  • Combines benefits of both supervised and unsupervised learning.

  • Useful for scenarios with limited labeled data.

• Disadvantages:

  • Requires careful selection of labeled and unlabeled data.

  • Performance may be affected if the balance between labeled and unlabeled data is not maintained.

4. Reinforcement Learning:

   • Definition: Reinforcement learning operates on a feedback-based process, where agents learn by trial and error, receiving rewards for desirable actions.

   • Example: Teaching a computer program to play chess or navigate a maze.

• Advantages:

  • Ideal for sequential decision-making tasks.

  • Learns through trial and error, mimicking human learning.

• Disadvantages:

  • Prone to high variance and instability during training.

  • Requires extensive computational resources and time for training.

Summary:

• Supervised learning relies on labeled data for predicting outputs, while unsupervised learning finds patterns in unlabeled data.

• Semi-supervised learning combines labeled and unlabeled data for training, offering efficiency and flexibility.

• Reinforcement learning learns through interaction with the environment, receiving rewards for desirable actions.