Machine Learning: Transforming Data into Insights, Slides of Computer science

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Machine Learning

Presented by: Abigail Atiwag

Machine learning (ML) is a subset of artificial intelligence (AI)

that focuses on developing algorithms and statistical models

that enable computers to learn from and make predictions or

decisions based on data. Here are key topics related to

machine learning:

Types of Machine Learning

Supervised Learning : Learning from labeled data with input-

output pairs. Algorithms learn to map inputs to outputs based

on training examples (e.g., classification, regression).

Unsupervised Learning : Learning from unlabeled data to find

patterns, structures, or clusters in the data (e.g., clustering,

dimensionality reduction, anomaly detection).

Types of Machine Learning

Semi-Supervised Learning : Combining labeled and unlabeled

data for training, leveraging the benefits of both supervised

and unsupervised learning.

Reinforcement Learning : Learning through trial and error by

interacting with an environment, receiving rewards or penalties

based on actions (e.g., game playing, robotics, optimization).

Machine Learning Algorithms Classification Algorithms : Algorithms for predicting discrete class labels (e.g., logistic regression, decision trees, support vector machines, k-nearest neighbors, random forests). Regression Algorithms : Algorithms for predicting continuous numerical values (e.g., linear regression, polynomial regression, support vector regression, decision tree regression).

Machine Learning Algorithms Clustering Algorithms : Algorithms for grouping similar data points into clusters (e.g., k-means clustering, hierarchical clustering, DBSCAN). Dimensionality Reduction Algorithms : Techniques for reducing the number of features or variables in data (e.g., principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE)).

Machine Learning Algorithms Anomaly Detection Algorithms : Algorithms for detecting unusual patterns or outliers in data (e.g., isolation forest, one-class SVM, local outlier factor). Association Rule Learning : Algorithms for discovering relationships or patterns in data (e.g., Apriori algorithm, frequent pattern mining).

Machine Learning Algorithms Neural Networks and Deep Learning : Deep learning algorithms, including artificial neural networks (ANNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep reinforcement learning models.

Feature Engineering

Feature Selection : Identifying and selecting relevant features

or variables that contribute most to the predictive power of

the model.

Feature Extraction : Transforming raw data into meaningful

features or representations (e.g., text preprocessing, image

feature extraction, signal processing).

Feature Engineering Feature Scaling : Normalizing or standardizing features to ensure consistent scales and improve model performance (e.g., min-max scaling, z-score normalization).

Model Evaluation and Validation Training and Testing Data : Splitting data into training and testing sets for model training and evaluation. Cross-Validation: Techniques such as k-fold cross-validation to assess model performance and generalization across multiple subsets of data.

Model Evaluation and Validation Metrics : Evaluation metrics for classification (e.g., accuracy, precision, recall, F1 score, ROC-AUC) and regression (e.g., mean squared error, R-squared, mean absolute error).

Hyperparameter Tuning and Optimization Hyperparameters : Parameters that control the learning process (e.g., learning rate, regularization, number of layers in neural networks). Grid Search and Random Search : Techniques for searching and optimizing hyperparameter combinations to find the best- performing model.

Model Deployment and Production

Model Training : Training machine learning models on training

data using appropriate algorithms and techniques.

Model Deployment : Deploying trained models into production

environments for real-time predictions or decision-making

(e.g., APIs, web services, containerization).

Model Deployment and Production Monitoring and Maintenance : Monitoring model performance, data drift, and model degradation over time, and updating models as needed.

Ethical and Responsible AI

Bias and Fairness : Addressing biases in data and models,

ensuring fairness, transparency, and accountability in machine

learning applications.

Privacy and Security : Protecting sensitive data, complying with

privacy regulations (e.g., GDPR, HIPAA), and securing machine

learning systems against attacks.

Applications of Machine Learning Natural Language Processing (NLP) : Text analysis, sentiment analysis, language translation, chatbots, named entity recognition, text summarization. Computer Vision : Image classification, object detection, image segmentation, facial recognition, image generation (e.g., GANs).

Applications of Machine Learning Recommendation Systems : Collaborative filtering, content- based filtering, personalized recommendations (e.g., movie recommendations, product recommendations). Predictive Analytics : Demand forecasting, sales prediction, risk assessment, fraud detection, churn prediction, healthcare diagnostics.

Applications of Machine Learning Autonomous Systems : Self-driving cars, robotics, autonomous drones, automated decision-making systems.

Machine learning is a rapidly evolving field with applications across various industries, including healthcare, finance, e-commerce, manufacturing, transportation, and entertainment. Understanding machine learning concepts, algorithms, techniques, and best practices is essential for data scientists, machine learning engineers, AI researchers, and anyone working with data- driven solutions.

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