How does machine learning work? All you need to know

Many of us may hear about machine learning before. However, you might not have the full understandings of the way it works. Today we will discuss this topic in more details.

Machine learning can be the modern science which gets computers the ability to act, but they can be explicitly programmed. Previously, it gave us a throughout understanding of our human genome, self-driving cars, effective web search, and practical speech recognition. Nowadays, machine learning can be so pervasive that you can use it several times per day without having full knowledge about it. Also, many researchers think this can be the critical way for the human to make the significant progress towards AI at the human level.

How does machine learning work?

To obtain the most valuable result from methods of machine learning, we might need to know the way to pair its best algorithms and let them fit the right processes and tools. SAS comprises sophisticated, rich heritage in term of statistics as well as data mining which comes along with new advances regarding the architecture to ensure the models run at its fastest speed – even in the huge and complex enterprise environments.

In term of algorithms, the SAS graphical interfaces for users may assist you much in building the models of machine learning and implementing the iterative process of machine learning. You do not have to know everything about statistics. Our comprehensive algorithms selection of this field may help you get value quickly from the big data. Such components can be included in a full range in different SAS products.

Following we will discuss SAS algorithms of machine learning include:

• Sequential covering building of rules
• Neural networks
• Gaussian mixture models
• Decision trees
• Singular value decomposition
• Random forests
• Principal analysis of components
• Sequence and associations discovery
• Gradient boosting and bagging
• Nearest-neighbor mapping
• Support vector machines
• K-means clustering
• Kernel density estimation
• Self-organizing maps
• Bayesian networks
• Optimization for local search techniques (such as genetic algorithms)
• Multivariate adaptive splines of the regression
• Expectation maximization

Processes and Tools

Up to now, we may know that it is not only the algorithms created the whole circumstance. Ultimately, we may have a chance to diagnose the secret of getting the valuable things from the big data when pairing the algorithms for all handy tasks with:

• An end-to-end, integrated platform for all dedicated automation process of the decision comprising from all necessary data;
• Comprehensive data management and quality;
• GUIs designed for process flows and building models; 
• Easy deployment of models so you may get reliable, repeatable results quickly;
• Interactive exploration of data and model results visualization; 
• Automated ensemble evaluation of models to identify and recognize the perfect performers;
• Comparisons of various different models of machine learning to identify the most suitable one quickly. 

A training set of machine learning

If you want to learn much about the effective techniques of machine learning, you may start by gaining and implementing them in practice before getting them directly to work, especially for yourself. However, the more important thing is that you may understand about the theoretical learning underpinnings as well as gain the know-how practice needed to powerfully and quickly apply such techniques to solve new problems. Then, you will learn about the best practices of Silicon Valley in innovation provided that it pertains well to Al and machine learning.

The full course can provide you a broad and comprehensive introduction to statistical pattern recognition, machine learning, and datamining. Topics you may consider include:

• Best practices of machine learning (variance /bias theory; the innovation process of Al and machine learning);
• Unsupervised learning (deep learning, clustering, recommender systems, and dimensionality reduction);
• Supervised learning (non-parametric/ parametric algorithms, support vector machines, neural networks, and kernels).

If you complete this course, you may draw your own lesson from numerous applications and case studies. In addition, you can also learn the way to apply all learning algorithms smoothly to building all smart robots (control, perception), database mining, text understanding (anti-spam, web search), computer vision, audio, medical informatics, and so on.

An application of machine learning - Natural-sounding robotic voices

The “robotic voice” term can be defined clearly soon with the blooming text-to-speech system performance. Today, we may think of the speech synthesis which is an example of complement. Occasionally, this application of machine learning is indeed a strong competitor to all human voice-over announcers and talents.

The publications mentioning DeepVoice, WaveNet, and Tacotron are considered as important milestones for us to continue to build the acoustic forms passing of all Turing tests. However, training the speech synthesizer can be a resource-intensive, time-consuming task which is outright frustrating sometimes. The demos and issues published on the Github repositories can focus more on replicating research results. They are indeed a truthful testimony to the above fact.

Adversely, all platforms of cloud computing covered in the series —  IBM Watson, Amazon Web Services, Microsoft Azure, and Google Cloud — create the available text-to-speech conversion at the service call. This fact opens up many exciting opportunities which can develop rapidly to engage the conversational applications directly with increasingly natural-sounding and flexible voices.

Conclusion

In short, this article did provide you helpful information regarding how machine learning works, its processes and tools, a training set, and an application of machine learning. In the case that you need basic guidance stated on which algorithm of machine learning to utilize for, you may want to read additional articles in our website.