Machine Learning for Big Data
Machine Learning for Big Data is a session that focuses on the application of machine learning techniques to large and complex datasets. Participants will learn about the principles of machine learning and how to apply them to solve real-world problems in big data analytics.The session covers a range of machine learning topics, including supervised learning, unsupervised learning, and reinforcement learning. Participants will explore different machine learning algorithms and models, such as decision trees, support vector machines, and neural networks, and learn how to select the most appropriate model for a given problem.Through hands-on exercises and case studies, participants will gain practical experience in building and evaluating machine learning models using big data. By the end of the session, participants will have the skills and knowledge needed to apply machine learning techniques to big data analytics projects effectively.
Related Conference of Machine Learning for Big Data
10th World Congress on Computer Science, Machine Learning and Big Data
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Machine Learning for Big Data Conference Speakers
Recommended Sessions
- Big Data Analytics in Finance and Banking
- Big Data Security and Privacy
- Big Data Technologies and Tools session
- Case studies and best practices in Big Data Analytics
- Clustering and Association Rule Mining
- Data Cleaning and Preprocessing
- Deep Learning for Big Data Applications
- Exploratory Data Analysis (EDA)
- Foundations of Big Data Analysis
- Future Trends in Big Data Analysis and Data Mining
- Graph Mining and Network Analysis
- Machine Learning for Big Data
- Privacy-Preserving Data Mining
- Real Time Big Data Processing
- Recommender Systems and Personalization
- Social Network Analysis
- Stream Data Mining and Sensor Data Analysis
- Text Mining and Natural Language Processing
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