Machine Learning

So what is machine learning?

Machine learning is a type of Artificial Intelligence (AI) with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

The process of machine learning is similar to data mining. Both search through data to look for patterns. But instead of extracting data as in the case of data mining applications, machine learning uses the patterns in them and adjust program actions accordingly.

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Building a business around Machine Learning APIs

Machine learning is categorized as :

  • Supervised Machine Learning 
  • Unsupervised Machine Learning

 

Supervised Machine Learning:

In supervised machine learning the ‘categories’ are known. Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. In supervised systems, the data as presented to a machine learning algorithm is fully labeled. That means: all examples are presented with a classification that the machine is meant to reproduce. For this, a classifier is learned from the data, the process of assigning labels to yet unseen instances is called classification.

Unsupervised Machine Learning: 

Unsupervised learning seems much harder: the goal is to have the computer learn how to do something that we don’t tell it how to do! There are actually two approaches to unsupervised learning.

  • The first approach is to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success. Note that this type of training will generally fit into the decision problem framework because the goal is not to produce a classification but to make decisions that maximize rewards. This approach nicely generalizes to the real world, where agents might be rewarded for doing certain actions and punished for doing others.
  • The second type of unsupervised learning is called clustering. In this type of learning, the goal is not to maximize a utility function, but simply to find similarities in the training data. The assumption is often that the clusters discovered will match reasonably well with an intuitive classification. For instance, clustering individuals based on demographics might result in a clustering of the wealthy in one group and the poor in another.
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TensorFlow is Google Brain’s second generation machine learning system, with a reference implementation released as open source software on November 9, 2015.

Google’s TensorFlow had recently been open-sourced. TensorFlow is an open source software library for machine learning. But now Google focuses on something known as deep learning. Google also uses this AI engine to recognize spoken words,translate from one language to another, improve Internet search results, and more. It’s the heart of Google’s Photos app! We will have a detailed look at data mining and deep learning in the future.

So to conclude this blog post, we’ve put some insights on Machine Learning. If you like this post leave a like and also leave a comment for more information and posts on related topics!

Update 2.0:

Google today announced a new machine learning platform for developers at its NEXT Google Cloud Platform user conference in San Francisco. As Google chairman Eric Schmidt stressed during today’s keynote, Google believes machine learning is “what’s next.” With this new platform, Google will make it easier for developers to use some of the machine learning smarts Google already uses to power features like Smart Reply in Inbox.

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Google’s Cloud Machine Learning platform basically consists of two parts: one that allows developers to build machine learning models from their own data, and another that offers developers a pre-trained model. To train these machine learning models (which takes quite a bit of computing power), developers can take their data from tools like Google Cloud Dataflow, Google BigQuery,Google Cloud Dataproc, Google Cloud Storage, and Google Cloud Datalab.

“Machine learning. This is the next transformation,” Schmidt says. “I’m a programmer who sort of got lucky at Google. But the programming paradigm is changing. Instead of programming a computer, you teach a computer to learn something and it does what you want.”

Prerequisites for machine learning:

  1. Linear algebra
  2. Probability theory
  3. Calculus
  4. Calculus of variations
  5. Graph theory
  6. Optimization methods (Lagrange multipliers)

( via Quora )

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