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Machine Learning with Core ML

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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.
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. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Some machine learning methods:

1. Supervised machine learning algorithms
2. Semi-supervised machine learning algorithms
3. Reinforcement machine learning algorithms


Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

Understand with example:
Facebook's News Feed, for example, uses machine learning to personalize each member's feed. If a member frequently stops scrolling to read or "like" a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user's data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend's posts, that new data will be included in the data set and the News Feed will adjust accordingly.

So what exactly is machine learning? My small experiment…

Just to make sure I don’t over-estimate (or under-estimate) the capability of the target audience, I got hold of 10 people who were completely new to analytics. None of them had heard about machine learning before (yes, there are people like that!). Here is what they said:
  • I don’t know, may be learning from machines?
  • Making machines learn something a.k.a. programming machine software
  • Learning with help of computers
  • Learning through online courses (!!!)

That was fun! Perfect group to explain machine learning to. Here is how I started explaining to these people:
Machine Learning refers to the techniques involved in dealing with vast data in the most intelligent fashion (by developing algorithms) to derive actionable insights.
By this time, they were looking at me as if I have spoken a few things in front of people from Mars!


Now the common question – How is machine learning different from X?

The minute you start reading about machine learning, you see various rockets bombarding you with high velocity. These are jargons used loosely in the industry. Here are some of them: Artificial Intelligence, Deep Learning, Data Mining and Statistics.
For your clear understanding, I have explained these terms below in the simple manner. You will also understand the importance of these terms in context of machine learning:

X = Artificial Intelligence(AI):

It refers to the procedure of programming a computer (machine) to take rational. Ah! what is rational? Rational is the basis of taking a decision.
I mentioned ‘rational’ instead of intelligence (as expected) because we human beings tend to take decisions which are high on being rational and feasible rather than being explicitly intelligent. This is because all intelligent decisions needn’t be rational and feasible (my hypothesis). Hence, the central motive behind using AI is to achieve the computer (machine) behave in a dandy fashion in lieu of human guidance instead of being doltish!
AI may include programs to check whether certain parameters within a program are behaving normally. For example, the machine may raise an alarm if a parameter say ‘X’ crosses a certain threshold which might in turn affect the outcome of the related process.



One can create or train own data model using TensorFlow,  Confused what is that? no issue, please see, 
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organisation for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.





Where from here? please have a look into https://www.tensorflow.org

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