Machine Learning

 

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Machine Learning

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Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience The process of learning begins with observations or large sums of 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

 

Develop systems that can automatically adapt and customize themselves to individual users. – Personalized Persona lized news or mail filter

Discover new knowledge from large databases (data mining). – Marke Markett basket analysis (e.g. diapers and beer)

Why Machine Learning

Ability to mimic human and replace certain monotonous tasks tasks - which require some intelligence

Develop systems that are too difficult/exp difficult/expensive ensive to construct manually because they require specific detailed skills or knowledge tuned to a specific task

 

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Need of Machine Learning

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Machine lear Machine learning ning allows allows user to analyse analyse bigger bigger, more complex data and deliver faster, more accurate results Help develop develop self-dr self-driving iving cars, cyber fraud fraud detection,, online rrecommen detection ecommendation dation en engines gines from Facebook, Netflix, and Amazon Machine Learning has also changed the way data extraction and interpretations are done by automating generic methods/algorithms, thereby replacing traditional statistical techniques

 

The primary purpose of machine learning is to discover patterns userand data and then make predictions basedin onthe these intricate patterns for answering business questions and solving business problems •

Objective Objectives Machine s of Learning

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Financial services – services – machine learning can help spot investment trends, prevent fraud Governmental – It can help identify ways for Governmental – cost savings to be made so improving efficiency and maximising budgets Health – Health  – data can be analysed to identify

trends and improve diagnosis Retail – Retail  – the objective of machine learning is usually to help retailers understand their customers better and personalise their interactions

 

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Objective Objectives s of Machine Learning

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Oil and gas - Finding Finding new new energy energy sources. Analysing minerals in the ground. Predicting refinery sensor failure Transportation - Analysing Analysing d data ata to identify patterns and trends is a key to the transportation industry Fraud – the increased use of systems Fraud – and activities such as online shopping

and financial transactions increases fraudulent fraudu lent behaviour behaviour,, so it helps organisations combat losses through fraud.

 

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Methodology •

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Supervised learning - The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs Unsupervised learning - No labels are given to the learning algorithm, leaving it on its own to find structure in its input: Clustering - You ask the computer to separate similar data into clusters, this is essential in research and science High Dimension Visualization - Use the computer to help us visualize high dimension data Generative ModelsAfter a model captures the probability distribution of your input data, it will be able to generate more data. This can be very useful to make your classifier more robust

 

Methodology

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On the basis of “output” desired from a machine learned system

Classification: Inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multilabel classification) of these classes Regression: It is also a supervised learning problem, but the outputs are continuous rather than discrete. For example, predicting the stock prices using historical data.

 

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Challenges of Machine learning

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Data - Memory Data Memory network networkss requir require e large workin working g memory to store data Security - Howev However er,, gathering data is not the only concern. Once a company has the data, security is a very prominent aspect that needs to be taken care of  Understanding dee Understanding deep p nets training training - Although ML has come very far, we still don’t know exactly how deep nets training work Infelexible Inf elexible Business Business models - Machine learnin learning g requires requir es a business to be agile in their policies. Implementing Machine Learning efficaciously requires one to change their infrastructure, their mind-set, and also requires proper and relevant skill-set

 

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Challenges of Machine learning

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Infrastructure Infrastructur e - There is a need for proper proper infrastructure which can aid the testing of different tools. Frequent tests should also be allowed to develop the best possible and desired outcomes, which in turn, can assist in creating better, stout, and manageable results Cost - There is a virtue in knowing the these se values if you’re looking to implement Machine Learning, because if you’re applying Machine Learning, you will require Data Engineers, a Project Manager with a sound technical background. In essence, a full data science team isn’t something newer companies or start-ups can afford

 

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In conclusion, employing a Machine Learning method be extremely but can also serve as can a revenue chargertedious, for a company. However, this is only possible by implementing Machine Learning in newer and more innovative ways. Machine Learning is only beneficial if there

Conclusion

are different plans, so regardless of one plan not performing up to the desired standards, the other can be put into action. Getting a glimpse into which Machine Learning algorithm would suit an organization is the only issue that one needs to get by. Once you get the best algorithm with which you’re achieving the required outcomes, you shouldn’t stop experimenting and trying to find better and more innovative algorithms.

 

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