Understanding Machine Learning Algorithms
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Machine learning algorithms enable computers to learn from data, identify patterns, and make decisions with minimal human intervention. These algorithms drive innovations in various fields, including healthcare, finance, and marketing, transforming data into actionable insights.
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Understanding Machine Learning Algorithms
- 2. • • • • • Introduction to Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Comparing ML Algorithms Agenda www.iabac.org
- 3. Introduction to Machine Learning Machine learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It involves the use of algorithms and statistical models to analyze and draw inferences from patterns in data. ML is crucial in today’s technology landscape as it powers a wide range of applications, from recommendation systems and image recognition to autonomous vehicles and natural language processing. As data continues to grow exponentially, the ability of ML to process and make sense of this data becomes www.iabac.org
- 4. Labeled Data Training and Testing Common Algorithms In supervised learning, the algorithm is trained on a dataset that includes both input data and the corresponding correct output. This labeled data guides the learning process. The dataset is divided into training and testing sets. The model learns from the training set and is evaluated on the testing set to determine its accuracy and performance. Popular supervised learning algorithms include Linear Regression, Decision Trees, Support Vector Machines, and Neural Networks. These algorithms are used for tasks like classification and regression. Supervised Learning www.iabac.org
- 5. Unsupervised Learning Clustering algorithms group data points into clusters based on their similarities, commonly used for market segmentation, social network analysis, and image compression. Unsupervised learning works with data that has no labeled responses, seeking to find hidden patterns or intrinsic structures within the data. Dimensionality reduction techniques like PCA reduce the number of variables under consideration, useful for visualization and increasing computational efficiency. Clustering Unlabeled Data Dimensionality Reduction www.iabac.org
- 6. Reinforcement Learning Balancing the need to explore new actions and exploit known rewarding actions. Essential for finding the optimal strategy in uncertain environments. A policy defines the agent's way of behaving at a given time. It maps states to actions, guiding the agent's decisions in the learning process. Value functions estimate the expected reward of states or actions. They help the agent evaluate the long-term benefits of its current choices. Policy Value Functions Exploration vs. Exploitation www.iabac.org
- 7. • • • Supervised learning provides high accuracy and clear guidance from labeled data. Unsupervised learning discovers hidden patterns and structures in data without labeled examples. Reinforcement learning optimizes actions through trial and error, making it powerful for decision-making tasks. • • • Supervised learning requires a large amount of labeled data, which can be time-consuming and expensive to obtain. Unsupervised learning can be less accurate and harder to evaluate due to the lack of labeled data. Reinforcement learning can be computationally expensive and requires a well-defined environment for training. Pros Cons Comparing ML Algorithms www.iabac.org