AI & Machine Learning

What is AI?

You can think of deep learning, machine learning and artificial intelligence as a set of Russian dolls nested within each other, beginning with the smallest and working out. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth.

Here are a few other definitions of artificial intelligence

• A branch of computer science dealing with the simulation of intelligent behavior in computers.
• The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.
• The capability of a machine to imitate the intelligent human behavior 
• A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. It is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.

The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.

How Machine Learning Works?

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. It is also similar to data mining and predictive modeling.

Some machine learning methods: Machine learning algorithms are often categorized as supervised or unsupervised.

• Supervised Algorithm: Supervised algorithms require a data scientist or data analyst with machine learning skills to provide both input and desired output. It can apply on what has been learned in the past to new data using labeled examples. The system is able to provide targets for any new input after sufficient training.
• Unsupervised Algorithm: Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. Unsupervised learning algorithms -- also called neural networks -- are used for more complex processing tasks than supervised learning systems, including image recognition, speech-to-text and natural language generation.   
• Semi-supervised: somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiringunlabeled data generally doesn’t require additional resources.
• Reinforcement Algorithm: This area of deep learning involves models iterating over many attempts to complete a process. Steps that produce favorable outcomes are rewarded and steps that produce undesired outcomes are penalized until the algorithm learns the optimal process.

Free Online Artificial Intelligence  

• Google’s Free Machine Learning Course
• Andrew NG’s Free AI Course on Coursera
• Free Udacity Online AI Course