Data Science – Machine Learning

Machine Learning (ML) is an advanced form of artificial intelligence ( AI) which enables software programs to be able to make statistical predictions without being directly programmed to do this. Machine Learning uses data acquired from experimentation, experiments and experience in running various operations that can be categorized into different areas such as optimization. It can also be applied to common business applications such as product forecasting, sales forecasting, brand name generation, product analysis, decision making and so on. Machine Learning algorithms are used to extract the needed information from the inputs.

Many machine learning algorithms have been proven to work in a wide variety of domains. For instance, some popular Machine Learning tools for large scale data analysis can forecast future performance of the stock market based on trends in the real stock market; they make reliable and testable predictions on parameters such as date, time, price, volume and other quantities. Other machine learning models are capable of making general or domain specific predictions.

There are three types of Machine Learning: supervised learning, unsupervised learning and reinforcement learning. In supervised learning, an algorithm learns a certain procedure on the basis of previous results. The most popular supervised Machine Learning technique is the Backpropagation method. In supervised learning, the process of training the system is continuous and relies on feedback provided by the user. The algorithm learns from the actions of the user and the modifications made by the user. The main drawback of this technique is the lack of creativity and the difficulty of designing and running the training models.

Unsupervised Machine Learning is quite similar to supervised learning with the exception that it does not depend on feedback from the user and the designer of the system. This type of Machine Learning has been very useful for generating signals which can be used for business decisions such as choosing the stock to invest in. Unlike supervised learning, in unsupervised machine learning the system generates random predictions. This drawback makes this type of Machine Learning more inefficient and less useful for making decisions.

Regression trees are another example of Machine Learning algorithm. These trees take data sets and partitions them into smaller pieces. The smaller pieces then get further subdivided until the desired result is achieved. Regression trees can also be used to analyze data sets for relationships between many different factors. Data scientists often use these Machine Learning Algorithms for Machine Learning projects like feature extraction, neural networks, data cleansing and other Machine Learning processes.

One of the most common uses of Machine Learning techniques is the development of artificial neural network. Such a machine learning algorithm can take in an input image and produce an output image using one or many artificial neural networks. For example, if an author wants to create a photo book, he can feed in a photo and generate an image-processing program which will turn the photo into an excellent text. Such programs are made up of multiple artificial neural network nodes which when trained on the input image become a network of images, texts and so on.

supervised learning and unsupervised learning both produce very similar results. In supervised learning, a student will have to work in teams to find the solution for a problem. In supervised learning the student will get help from his teammates in order to achieve a solution for a problem. However, in unsupervised learning there is no one to check the solution against the given output and therefore the student relies only on his own judgment in order to achieve the solution.

So far we have discussed two Machine Learning techniques used by data scientists in the quest for better solutions. While they both share similarities in concept, they differ in their implementation. Unsupervised machine learning requires a data set to be labeled prior to its usage, and supervised learning requires a data set to be labeled while it is being used. Also, data scientists may either work with unsupervised or supervised learning depending on the kind of problems they need to solve.