What is machine learning and how does it work | IABAC
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The document provides an overview of machine learning, including its working process, key applications in industries such as healthcare and finance, and challenges like data quality and overfitting. It also discusses future trends, including the integration of quantum computing and advancements in automated machine learning. Overall, it highlights the significance and evolving nature of machine learning in various sectors.
What is machine learning and how does it work | IABAC
- 1. What Is Machine Learning and How Does It Work? www.iabac.org
- 2. • • • • • Introduction to Machine Learning How Machine Learning Works Applications of Machine Learning Challenges in Machine Learning Future of Machine Learning Agenda www.iabac.org
- 3. How Machine Learning Works General Process of Machine Learning ● ● ● ● ● Data Collection: Gather and aggregate large volumes of data from various sources to create a dataset. Data Preparation: Clean and preprocess the data to handle missing values, remove duplicates, and normalize the data for analysis. Deployment: Implement the model in a real-world environment to make predictions or automate decisions based on new data. Model Training: Use algorithms to recognize patterns within the data and build a predictive model. Model Evaluation: Assess the model's performance using various metrics such as accuracy, precision, and recall to ensure it meets the desired criteria. www.iabac.org
- 4. Applications of Machine Learning Key Applications Across Industries ● ● ● Healthcare: Machine learning algorithms are used for early diagnosis of diseases, personalized treatment plans, and predicting patient outcomes. Finance: Fraud detection systems, risk assessment models, and algorithmic trading rely heavily on machine learning for accurate and real-time analysis. Transportation: Autonomous vehicles, traffic pattern analysis, and predictive maintenance of infrastructure are driven by machine learning technologies. www.iabac.org
- 5. Challenges in Machine Learning Key Challenges ● ● ● Data Quality: Ensuring the availability of clean, relevant, and comprehensive data is critical for training effective models. Overfitting: Developing models that generalize well to new, unseen data rather than just memorizing the training data. Interpretability: Making machine learning models transparent and understandable to humans, especially in critical applications like healthcare and finance. www.iabac.org
- 6. Future of Machine Learning Future Trends and Developments ● ● ● ● ● Integration of quantum computing to solve complex machine learning problems more efficiently. Expansion of machine learning applications in personalized medicine, smart cities, and autonomous systems. Development of more energy-efficient algorithms to reduce the carbon footprint of machine learning processes. Increased Use of Automated Machine Learning (AutoML) tools to simplify the model creation process. Advancements in Explainable AI (XAI) to improve transparency and trust in machine learning models. www.iabac.org