Machine Learning is a popular field of software engineering that utilizations factual methods to enable PC frameworks to "learn" (i.e., dynamically enhance execution on a particular errand) with information, without being expressly modified.
Machine Learning has received much attention of late, and for good reason. With the rise in Big Data, machine learning has become a key technique for solving problems in areas such as:
Computational finance :
For credit scoring and algorithmic trading
Image processing and computer vision :
For face recognition, motion detection and object detection
Computational biology:
For tumor detection, drug discovery and DNA sequencing
Energy production:
For price and load forecasting
Automotive, aerospace and manufacturing : For predictive maintenance Natural language processing: For voice recognition applications Businesses and organizations working with large amounts of data recognize the benefits of Machine Learning technology. By gaining insights from Big Data (often in real time), industries work more efficiently and can gain advantages over their competition.
Automotive, aerospace and manufacturing : For predictive maintenance Natural language processing: For voice recognition applications Businesses and organizations working with large amounts of data recognize the benefits of Machine Learning technology. By gaining insights from Big Data (often in real time), industries work more efficiently and can gain advantages over their competition.
Some of the organizations focusing on Machine Learning include:
- Financial services
- Government
- Health care
- Marketing and sales
- Oil and Gas
- Transportation
Some of the topics covered include:
- The Basics of Machine Learning
- Machine Learning Techniques, Tools and Algorithms
- Data and Data Science
- Review of Terminology and Principles
- Applied Artificial Intelligence (AI) and Machine Learning
- Popular Machine Learning Methods
- Introduction to Deep Learning
- Applying Machine Learning
- Principles of Neural Networks
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