Machine Learning Technique (Class Work)

Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Deep learning is a specialized form of machine learning.

Why Machine Learning Matters

  1.  Computational finance, for credit scoring and algorithmic trading

  2. Image processing and computer vision, for face recognition, motion detection, and object detection

 3. Computational biology, for tumor detection, drug discovery, and DNA sequencing

 4. Energy production, for price and load forecasting

 5. Automotive, aerospace, and manufacturing, for predictive maintenance

 6.   Natural language processing, for voice recognition applications

How Machine Learning Works

Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.

How Do You Decide Which Machine Learning Algorithm to Use?

Choosing the right algorithm can seem overwhelming—there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learning.There is no best method or one size fits all. Finding the right algorithm is partly just trial and error—even highly experienced data scientists can’t tell whether an algorithm will work without trying it out. But algorithm selection also depends on the size and type of data you’re working with, the insights you want to get from the data, and how those insights will be used.