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 THANK YOU