Introduction to Machine Learning (Notes)
Introduction to Machine Learning (Notes)

Machine Learning is basically, to train machines (computers) by feeding them with huge amount of data. As a result they can predict/extract useful information based on previously available data. For example in order for a computer to recognize hand writing, we need to train that computer by feeding it with large amount of different handwriting samples. Machine learning helps to maximize profit and minimize cost by adding business intelligence in previously available data.

Machine learning evolved from pattern recognition and applying algorithms that can learn from data and then make predictions, and it’s closely related to computational statistics (thank you Wikipedia). Some examples of machine learning are character recognition in handwriting, facial recognition, automatic spam filtration etc.

“Machine Learning is about using the data you already have to make predictions. This sounds really fancy, but most of the time, the ‘prediction’ is really just a label,” says Hillary (Data Scientist).

Have you wondered when you see an AD related to a product you recently searched for? Were you amazed to see recommendations list about your favorite clothing brand? This is machine learning. The companies, organizations, or brands keep track of activities, behavior, likes and dislikes of their customers to train machines (computers) at their back offices. Now based on these data instances they can recommend related products to customers and thus maximizing their profit.

According to Forbes, “Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn. Data scientist roles have grown over 650% since 2012, but currently, 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles.”

The language used as the basis for many machine learning algorithms is Python. It’s powerful, easy for beginners and has well-supported documentation.

Important Machine Learning Concepts

  1. Association Rules
  2. Classification
  3. Pattern Recognition
  4. Outlier Detection
  5. Compression
  6. Regression
  7. Supervised Learning
  8. Unsupervised Learning
  9. Document Clustering
  10. Density Estimation
  11. Reinforcement Learning
  12. Probably Approximately Correct Learning (PAC Learning)
  13. Learning Multiple Classes
  14. Model Selection
  15. Optimization Procedure
  16. Geometric Model
  17. Simple Linear Classifier
  18. Nearest Neighbor Classifier
  19. Clustering
  20. Probabilistic Model
  21. Feature Extraction
  22. Feature Selection
  23. Bayes Rule


Leave a Comment

Your email address will not be published. Required fields are marked *