What is Machine Learning in Simple Terms and Why It Matters Today | IABAC
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Machine learning is a branch of artificial intelligence that enables computers to learn from data and improve over time without being explicitly programmed. It powers applications like recommendations, image recognition, and predictive analytics across various industries.
What is Machine Learning in Simple Terms and Why It Matters Today | IABAC
- 1. What is Machine Learning ? www.iabac.org
- 2. • • • • • • Introduction to Machine Learning Types of Machine Learning How Machine Learning Works Applications of Machine Learning Benefits and Challenges of Machine Learning Future of Machine Learning Agenda www.iabac.org
- 3. It involves the use of algorithms and statistical models to analyze and draw inferences from patterns in data. Machine learning is crucial in modern technology, driving innovations in fields such as healthcare, finance, and autonomous driving. Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. Introduction to Machine Learning www.iabac.org
- 4. Types of Machine Learning Involves training a model on labeled data, where the input-output pairs are known. Common algorithms include linear regression and support vector machines. Involves training a model on data without labeled responses. The goal is to find hidden patterns or intrinsic structures. Common algorithms include k- means clustering and principal component analysis. Involves training a model to make sequences of decisions by rewarding desired behaviors and/or punishing undesired ones. Common algorithms include Q-learning and deep Q-networks (DQN). Supervised Learning Unsupervised Learning Reinforcement Learning www.iabac.org
- 5. Data Collection Gather relevant data from various sources. This data serves as the foundational input for training the model. Data Preprocessing Clean and transform the collected data to make it suitable for analysis. This includes handling missing values, normalization, and data splitting. Model Training Model Evaluation Assess the performance of the trained model using various metrics. This helps in determining the model's accuracy and generalization capability. Raw Data Files Data Repositories APIs for Data Collection Cleaned Data Sets Normalized Data Training and Testing Data Sets Use the preprocessed data to train the machine learning model. This involves choosing an algorithm and optimizing it to learn patterns from the data. Trained Model Optimization Parameters Training Logs Evaluation Metrics Performance Reports Validation Curves How Machine Learning Works www.iabac.org
- 6. Applications of Machine Learning Machine learning models diagnose diseases, predict patient outcomes, and personalize treatment plans. Algorithms detect fraud, automate trading, and assist in risk management and customer service. Self-driving cars, route optimization, and predictive maintenance are powered by machine learning. Healthcare Finance Transportation www.iabac.org
- 7. Benefits and Challenges of Machine Learning • • • Automates repetitive tasks, increasing efficiency and productivity. Enhances decision-making through data-driven insights and predictive analytics. Enables personalized user experiences in various applications, such as recommendations and targeted advertising. • • • High-quality data is required, which can be difficult and expensive to obtain. Complex models can be challenging to interpret and explain, leading to trust issues. Potential for bias in algorithms, which can result in unfair or unethical outcomes. Benefits Challenges + × www.iabac.org
- 8. Increased integration of machine learning in everyday devices for personalized user experiences. Wider adoption of machine learning in industries such as healthcare, finance, and education, transforming these fields. Advanced AI systems capable of performing tasks requiring human-like intelligence and decision-making. Ethical considerations and regulations will become more prominent as AI systems become more sophisticated. Future of Machine Learning www.iabac.org