What is Machine Learning

Understanding The Machine Learning

What is Machine learning from Coding compiler – Understanding the Machine Learning – This is a data analysis technique that teaches computers to do what comes naturally to humans to learn from experience. Machine learning algorithms use advanced computational methods to “learn” information directly from the data without relying on a predetermined equation as a model.

The algorithms optimize their performance when the number of samples available for learning increases.

Machine Learning Methods

Machine learning

The use of machine learning methods is especially evident when it is necessary to predict certain scenarios or to identify a particular behavioral pattern, and all this from a data collection that exists in the organization.

The processes in machine learning are similar to those of data mining and predictive models. Both require data exploration to detect patterns and accordingly a plan of action. Simplistically, many people are actually exposed to machine learning from online shopping, for example, when the site allows customers to get information about similar products, or recommend buying similar products.

This is because recommendation engines use Machine learning to customize online ad serving to the surfer. Beyond custom marketing, other common machine learning cases include fraud detection, spam filtering, network security, forecasting maintenance, and more.

In what areas are Machine Learning can be used?

If we remember that today we are in the era of Big Data , then with the increase in the amount of data and especially with the different nature of the information entering the organization and yet the ability to investigate unstructured data, the field of machine learning has become a central technique for solving problems in areas such as:

  • Financial financing – for credit management and algorithmic trading.
  • Image processing – for face recognition, motion recognition, and object recognition.
  • Computational biology – for tumor detection, drug discovery and DNA sequencing.
  • Energy production – Sourcing price and consumption.
  • Vehicles – in aviation and aerospace, for forecasting and maintenance.
  • Natural Language Processing (NLP) – for voice recognition and text exploration applications.

More data, more questions – better answers with Machine Learning

Machine learning algorithms find natural patterns in data and thus help insights that are obtained and help make better decisions on a forecast basis.

They are used daily to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more.

For example, media sites rely on machine learning to sift through millions of options and give the surfer recommendations about a song or movie. Retailers use Machine Learning to gain insight into consumer behavior when buying.

So, how does Machine Learning work?

Machine learning algorithms are often classified into two categories: supervised and unsupervised.

Algorithms supervised require scientist Data ( Data Scientist ) or Data Analyzer ( Data Analyst ) will be with the skills Machine learning to control their debriefing, providing both the input and the desired output, as well as provide feedback on the accuracy of forecasts after made the running of blowfish several times.

Data scientists determine which variables, or attributes, should be analyzed and which model should be used to build forecasts. Once the algorithmic learning process has been completed and the algorithmic nature of the algorithms has been studied, it is possible to apply what is learned about the new data.

An unsupervised algorithm should not undergo a controlled learning process. These algorithms detect hidden patterns in input data. In fact, they use an iterative approach called deep learning to review data and reach conclusions.

An unsupervised algorithm – also called neural networks – is used for more complex processing tasks, including image processing, speech-to-text analysis, and language analysis. Neural networks work by scanning millions of samples of sample data and automatically identifying connections between many variables.

After this learning process, the algorithm can use a set of connections it has created for itself to answer new data. These algorithms have become useful in Big Data environments since they require huge amounts of data to run a learning process to create a large enough collection of connections and then be able to apply this knowledge to new data.

Classification of Machine Learning Techniques

There are several types of models that match the need and are suitable for running with one or another algorithm.

What is Machine Learning

Supervised machine learning will use the techniques of classification and regression in the construction of the model.

Classification techniques classify and classify input data. For example, applications for handwriting recognition use classification to identify letters and numbers; Image processing, object recognition, and image splitting; To check if the email is credible or spam, or if the tumor is cancerous or benign.

Typical applications include medical imaging, speech recognition, and credit level. The algorithms used here are support vector machine (SVM), decision trees,  k -nearest neighbor, Naive Bayes, discriminant analysis, logistic regression, neural networks.

Regression techniques predict continuous responses – for example, changes in temperature or fluctuations in power consumption. Typical applications include power load forecasting and algorithmic trading. 

This technique is used when working with a data range or if the nature of the result is a realistic number, such as temperature or time to malfunction for item equipment. The algorithms used here are: linear model, nonlinear model, regularization, stepwise regression, decision trees, neural networks, adaptive neuro-fuzzy learning.

For Unsuccessful Learning, Clustering techniques will be used to delineate data to simulate undisclosed patterns or to identify groups in data. Common examples of this technique include DNA sequence analysis, market research, object identification. 

For example, if the cellular company wants to be more efficient in locating locations where they set up cellular antennas, they can use machine learning to estimate the number of user groups based on data from the antennas. 

At a given moment, communication between a cellular device and an antenna can only take place in one location, and so a research team will use this data to locate the best location of antennas to improve reception quality for their client groups.

The algorithms used here are k-means and k-medoids, hierarchical clustering, Gaussian mixture models, hidden Markov models, self-organizing maps, fuzzy c-means clustering, subtractive clustering.

So how do you decide which model/algorithm to use?

Choosing the right algorithm can be a crucial step – there are dozens of machine learning algorithms, and each takes a different approach to the question. There is no best method. Finding the right algorithm is often a process of trial and error – even highly experienced data scientists can not know in advance if a particular algorithm will work without trying it on concrete data. What can be said with certainty that the choice of algorithm depends also on the quantity and type of data being investigated, the insights that you want to receive from the data, and how you will use these insights.

Choose supervised learning if the model needs to learn about the already existing estimate to predict – for example, the future value of a continuous variable, such as temperature or stock price, or to categorize the data, for example, to identify certain car scenarios from video camera measurements.

And choose unsupervised learning if you need to investigate the data and the model should learn what is going on within the data itself, after dividing into categories/segments within the data and then reaching conclusions.

Who Should Learn Machine Learning

The field of Machine Learning has been hot for the last several years, and many professionals are interested in moving or moving to this area. Not everyone and every one is suitable.

But it is undoubtedly an intensive training process that requires high technical and analytical capabilities, as well as experience and background in the field of data. The candidate does not have to be a mathematician or a veteran programmer to learn Machine Learning, but surely he must come up with core competencies in these areas.

The good news is that once a candidate has relevant preliminary knowledge, everything else will be relatively easy. Simply put, Machine Learning is an area that applies the concept of concepts in statistics and computer science to the data.

Skills Required For Machine Learning

Knowledge of development:

You can work with Python, R, Matlab or other languages. Choosing one language or another is a personal decision by a professional or according to the organization’s decision. But without knowledge of programming language, it is not possible to advance to building machine learning models – a programming language is a central tool.

Knowledge in Statistics:

Machine Learning is not a statistic, but the advance knowledge of statistics and the ability to predict through different techniques will help to understand how to build a model and make learning faster and deeper.

Mathematics knowledge:

A necessary condition to better understand the mechanism of algorithms, how they are built and what exactly is done with the data.

Theoretical Aspects of Machine Learning

To get a very strong foundation in the field to advance and learn new and additional techniques, you have to spend a lot of time studying the theoretical aspects of the field, understanding the methodology, understanding the flow of processes, and familiarizing the common techniques that exist today in the field. 

And if you ask why you have to study so much theory if you do not intend to do a thorough study but are interested in using ready models to investigate the data, then here too the answer is quite complex:

  • Planning and data collection – Data collection can be an expensive process and take a long time. What types of data should be collected? How much data do you need? Is this challenge possible?
  • Data processing – Different algorithms have different hypotheses about input data. How to prepare the data in advance? Should we normalize them? Is the model immune to the lack of data? What about exceptions?
  • Interpreting model results – The idea is that Machine Learning is a kind of “black box” wrong. Yes, not all results can be interpreted directly, but the data scientist must be able to test the models to improve them. How can you know in advance whether the model is good or not? How can its results be explained to managers? Is there room for improvement and what does improvement mean?
  • Improving and improving models – It is very rare in first-time data to reach a good and appropriate model must understand the nuances of different parameters, techniques for model optimization and a perfect fit for each case. How can the model be fixed? Should I spend more time working on certain features ( feature-engineering ) or collecting data? Is it possible and necessary to build a model from scratch?
  • Bringing business value –Machine Learning processes are not cut off from business reality. Any organization based on decision making will want to adopt Machine Learning techniques. But does the data scientist really know the tools at his disposal and can maximize their efficiency and contribution to the business side? What are the most important results for optimization? Are there other algorithms that work better in one case or another? Are there any situations or business problems that Machine Learning is not the right solution?

The key to success in learning Machine Learning

  • Pay attention to the big picture and always ask “Why?”.

Whenever a new idea comes up, ask “why?” Why use Decision Tree instead of Regression in some cases? Why regulate parameters? Why split the data array? When you understand why each tool is used, you will become a real data scientist.

  • Remember that you do not always remember everything.

Do not get excited if a subject requires you to repeat it 3 times. This is the way, the learning, you will have to go back and review certain concepts again and again until you successfully implement the principles.

  • Go ahead and do not despair.

On the other hand, try not to dwell on certain concepts too long. There are concepts or topics that are difficult to explain, try to understand the principle and find a way to apply in practice – so everything will connect.

  • Implement the lesson.

Find a way to apply what you learn, and practice as much as possible. Find an interesting project and use his dataset to see how everything works. International competition in the field of Data Science – Kaggle – can be a great platform for ideas and projects that you can also do. Focused practice will sharpen your skills in the field.

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