Machine Learning Fundamentals

Machine Learning Basics

• AI and ML are valuable in problem-solving and decision-making
• AI and ML capabilities can increase human intelligence and creativity

Machine Learning Definition

• ML is a subset of AI
• Algorithms and models are developed to enable computers to learn
• Programs adapt from experience without explicit programming.
• ML systems predict outcomes, identify patterns, and optimize processes
• Minimal human intervention is required

Types of Machine Learning

• Three primary types of machine learning: Supervised, Unsupervised, and Reinforcement Learning
• Each learning method is suited to different problems and datasets and offers unique advantages

Supervised Learning

• Supervised learning involves training algorithms on datasets containing input-output pairs, known as labeled data.
• Algorithms utilize annotated examples with correct outputs
• Algorithms make predictions or decisions based on new, unseen data

Supervised Learning Goal

• The goal is to create a model that generalizes to new instances
• A model generalises well by minimizing error between predicted and actual outputs

Supervised Learning Applications

• Image recognition identifies objects, people, or scenes in images
• Spam detection filters unwanted emails
• Fraud detection identifies suspicious financial activities
• Medical diagnosis predicts the presence/absence of conditions based on patient data
• Language translation converts text from one language to another

Training a Model with Labeled Data Steps

• Data collection and preprocessing are the first steps
• Model selection is the second step
• Model training is the third step
• Model evaluation is the fourth step
• Model tuning and optimization are the final steps

Data Collection and Preprocessing

• Consists of gathering a dataset containing input-output pairs
• Datasets must be large and diverse to represent the problem space accurately
• Data may need cleaning, normalizing, or transformation so algorithms can process efficiently

Model Selection

• Consists of choosing an appropriate ML algorithm based on the problem at hand
• Popular supervised learning algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVM), and Neural Networks (ANN)

Model Training

• The chosen algorithm is trained on a labeled dataset during this phase
• Algorithms adjust parameters to minimize errors between predictions and outputs
• The process is often iterative, with the model improving performance through data passes

Model Evaluation

• Model performance is evaluated on a separate dataset that has not been seen before.
• A model's ability to generalize to new instances is thus determined
• Standard evaluation metrics include accuracy, precision, recall, and F1 score

Model Tuning and Optimization

• Model parameters may need fine-tuning or the algorithm may need optimizing to achieve better results if model performance is unsatisfactory
• Adjusting the learning rate, changing the model's complexity, or employing regularization techniques helps prevent overfitting

Overfitting

• It is a common problem in machine learning
• The model learns training data too well and captures noise/random fluctuations instead of underlying patterns.
• Models perform exceptionally well on training data, but poorly on unseen test data
• Overfitting leads to poor generalization

Polynomial Regression

• Underfitting is when a simple linear regression (straight line) fails to capture the pattern.
• Good Fit refers to a polynomial of moderate degree that fits the data well and generalizes to new points.
• Overfitting refers to a high-degree polynomial which perfectly fits every training data point, but oscillates wildly for new inputs
• A high-degree polynomial fits noise in training data, causing poor predictions on new data

Overfitting Prevention

• More data is preferable
• Simplifying the model
• Applying regularization
• Using cross-validation
• Dropout / Early Stopping for deep learning
• Feature selection & ensemble methods

Cross-Validation

• Splitting data into training, validation, and test sets to check performance

Regularization

• Penalizes overly complex models

Pruning

• Specifically for Trees
• Limit tree depth or remove unnecessary branches

Dropout

• Specifically for Neural Networks
• Randomly drop some neurons during training to prevent reliance on specific patterns

More Training Data

• If possible, an increase in data size leads to better generalization

Unsupervised Learning

• A technique allowing machines to discover hidden patterns and structures within data
• No prior guidance or labeled information is required

Unsupervised Learning Example

• You are presented with a large dataset containing information about various fruits
• Supervised learning provides labeled data, such as the fruit's name, color, and size
• Unsupervised learning provides no labels
• The task is to identify underlying patterns or structures, such as clustering similar fruits or finding relationships between attributes
• Customer segmentation in marketing and anomaly detection in cybersecurity are real-world applications

Clustering

• Data points are grouped together based on similarity or proximity
• Natural groupings within the data can be identified using Clustering algorithms, such as K-means or hierarchical clustering
• Clusters can be further analyzed to reveal insights or inform decision-making processes

Dimensionality Reduction

• It involves Principal Components Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE)
• It reduces dimensions in data while preserving its essential structure
• This action can reveal hidden patterns and simplify the analysis process
• High-dimensional data is difficult to analyze/visualize

Advantages of Unsupervised Learning

• Working with unlabeled data can uncover previously unknown patterns or relationships
• There are also challenges and limitations
• Large volumes of unlabeled data can be assessed
• Obtaining labeled data can be time-consuming, expensive, or impossible in many real-world scenarios
• A solution is to allow machines to learn from data without explicit guidance

Limitations of Unsupervised Learning

• It can be more difficult to evaluate and interpret than supervised learning
• Challenging to determine a model's accuracy/effectiveness, since there are no predefined labels or ground truth
• A lack of guidance can sometimes lead to irrelevant patterns being discovered, which are not helpful

Reinforcement Learning

• Learning through Interaction

Reinforcement Learning Principle

• This area focuses on training algorithms to make decisions based on their interactions with an environment
• An agent (learning algorithm) takes actions within a given environment to achieve a specific goal
• Agents receive feedback through rewards or penalties
• Agents adjust behavior, improve decision-making depending on the rewards or penalties

Reinforcement Learning Objective

• To enable the agent to learn an optimal policy
• Optimal policy is a set of rules that dictate the best course of action in any given situation
• Agents maximize cumulative reward over time
• A balance is struck between exploration (trying new actions) and exploitation (choosing the best-known action)

Key Components of Reinforcement Learning

• Agent: Learning algorithm that interacts with the environment and makes decisions
• Environment: Context in which the agent operates and takes actions
• State: Current situation within the environment
• Action: Decision made by the agent that affects the environment
• Reward: Feedback provided to the agent based on the consequences of its actions
• These components work in a continual, cyclical process
• Agents continually observe the environment, take actions, receive rewards, and update knowledge

Applications of Reinforcement Learning

• Robotics: Algorithms teach robots to walk, grasp objects, and navigate complex environments
• Finance: Reinforcement learning optimizes trading strategies and manages investment portfolios
• Healthcare: Treatment plans for patients with chronic conditions like diabetes and cancer are personalized
• Gaming: AI agents defeat human players in games like Go, Chess, and Poker

Reinforcement Learning vs. Supervised and Unsupervised Learning

• Supervised learning relies on labeled data for explicit feedback
• Reinforcement learning uses rewards and penalties for indirect feedback based on agent actions
• Reinforcement learning requires balancing exploration and exploitation, which is not present in supervised or unsupervised learning
• Reinforcement learning focuses on training agents to make decisions
• Supervised and unsupervised learning primarily involve pattern recognition and data analysis.

Choosing the Right Learning Method

• Understanding the distinctions between methods and how to choose the right one for the coding project is critical
• It is important to understand strengths and weaknesses so the most suitable approach is selected

Supervised Learning: Training with Labeled Data

• Supervised Learning excels when there is a clear relationship between input and output data
• Labeled data is readily available
• Supervised Learning is effective for classification, regression, and prediction tasks
• A weakness includes its reliance on labeled data
• Obtaining and maintaining a large dataset with accurate labels can be time-consuming and expensive
• Models struggle to generalize to new, unseen data if training data needs to be more diverse
• Choose if there is a well-defined problem with a clear input-output relationship and access to ample, labeled data.

Unsupervised Learning: Discovering Hidden Patterns

• Use when labeled data is scarce or nonexistent
• Ideal for uncovering hidden patterns, structures, and relationships within the data
• Great for clustering, dimensionality reduction, and anomaly detection tasks
• A precise output may be challenging to understand and validate
• More computational resources and time may be needed when processing large datasets
• Choose when there is a large amount of unlabeled data and an interest in discovering underlying patterns or structures

Reinforcement Learning: Learning Through Interaction

• It is well-suited for problems that make decisions and interacts with an environment
• Through trial and error, one can learn optimal strategies and actions
• Thus, this approach is ideal for game playing, robotics, and autonomous systems
• A weakness is that it is computationally expensive and may require significant time to converge on an optimal solution
• Also, it can be difficult to design an appropriate reward function that effectively guides the learning process
• Choose to use when working on a problem involving sequential decision-making and interaction with an environment coupled with the desire to invest time and resources in fine-tuning the learning process

Adopting The Power of AI and ML

• The power of AI and ML transforms how problems are approached and decisions are made
• Using these technologies, coders create more efficient, intelligent, and adaptable software solutions that learn and grow over time.
• Enhancements to user experience let developers tackle increasingly complex challenges with ease and precision
• Understanding ML principles and selecting the most appropriate method are key to success
• Choosing the right learning method depends on the nature of the problem, the availability of labeled data, and the desired outcome
• Understanding strengths and weaknesses makes informed decisions
• The ability of AI and ML transforms problem-solving and decision-making
• Coders stay informed and adapt to the evolving filed of ML to remain relevant in technology