Machine Learning Basics

What is the Machine Learning?

Machine Learning is the new science technology that getting computers to learn like humans do,  and improve their learning overtime in autonomous fashion , by feeding them data and information in the proper way. 

Machine learning methods!!!

Two of the widely using machines learning methods are supervised learning and unsupervised learning, but there also other methods in machine learning.

• o   Supervised learning: This method is used when we are working with the labeled target value, such as an input where the desired output is known.
• o   Unsupervised learning: This method is used when our dataset has no historical labels. Here the goal is to explore the data and find some structure within.
• o   Semi supervised learning: This method is used for the same application as supervised learning. But it uses both labeled and unlabeled data for training, typically a small amount of labeled data with large amount of unlabeled data.
• o   Reinforcement learning: This method is mostly used in robotics and gaming navigation. With this method, the algorithm discovers through trial and error with actions yield the greatest rewards.

What are the requirements for the good machine learning systems?

• ·         Data preparation capabilities.
• ·         Algorithms
• ·         Automation and iterative process.
• ·         Scalability
• ·         Integrity

Important Factors!!!

• ·         In Machine learning, a target is called a label.
• ·         A variable in statistics is called a feature in machine learning

Some Machine Learning Algorithms

• ·         Linear Regression
• ·         Logistic Regression
• ·         Naïve Bayes
• ·         K-Nearest Neighbors
• ·         Support Vector Machines
• ·         Random Forest