Machine learning is a rapidly growing field. In recent years, it has been used in all types of applications from simple search engines to complex automatic driving cars says Peter DeCaprio. With the advent of deep neural networks, machine learning has reinvented itself and become even more accurate and useful than before. For nearly three decades, data scientists and researchers have tried their best to build an intelligent machine that can predict future outcomes regardless of its complexity. It just couldn’t be done because we did not know how to train machines until recently with the birth of artificial intelligence (AI), computer science, and deep neural networks. The entire concept revolves around teaching machines through multiple layers which mimic the human brain’s working mechanism so computers get closer reasoning like humans do without actually thinking.
We know that there are many types of algorithms and technologies available in the market to build intelligent machines such as Artificial Neural Networks (ANNs), Deep Learning, Support Vector Machines (SVMs), Recommender Systems, Classification, and Regression Analysis and Decision Trees. All these processes work on a single aim: To understand and learn from data in order to find hidden patterns and insights. This will enable them to predict unknown outcomes and also make informed decisions based on the results. Some example use cases of Machine Learning are Facial Recognition, Customer Segmentation, Targeted Marketing Campaigns, and Stock Market Prediction, etc.
Machine learning is demonstrated by the image recognition process where images can be identified whether they represent a cat or not.
The key skills of a data scientist are being able to look at a large-scale dataset, use machine learning techniques to find insights, and communicate the results clearly.
Learning about Machine Learning is very useful these days as it will give you an edge over your competitors with enhanced accuracy of decision making. Here are some tools that can help you build an empire. Using various types of machine learning applications with real-time implementation:
Tools for Data Scientists
Data Scientists face many challenges when dealing with millions of datasets. They often require different sets of skills depending on the problem they are solving says Peter DeCaprio. We have listed down some tools that can be in use by Data Scientists to solve problems quicker with ease.
1) R Programming –
R is a programming language and software environment for statistical analysis, graphics representation, and reporting. Data scientists can use it to build predictive models quickly with its various libraries. Like Python, R is a good high-level programming language for Machine Learning. And is mostly used by the data scientist’s communities.
2) SAS –
SAS stands for Statistical Analysis System which is a software suite for analyzing and managing the data. That comes from both structured as well as unstructured sources. Such as phone calls or social networking websites like Facebook or Twitter etc. Peter DeCaprio says it provides various types of charts and graphs. To represent the data in an easy manner such as pie charts, bar graphs, etc. SAS also offers educational resources such as courses, books on machine learning on their official website.
3) Julia –
Julia is a relatively new programming language that uses for technical computing. It’s relatively easier to use than R and Python. Which makes it most popular among the data science community these days. Although the main drawback of this language is its small community support. As compared to other programming languages like C++ or Java.
4) Hadoop –
Hadoop was built by Doug cutting based on Google’s MapReduce paper. To meet his company’s need for storing and processing large volumes of data quickly. It has become a defacto standard in the Big Data World. With its set of projects such as HDFS, Hive, etc.
5) Apache Spark –
A powerful open-source framework is written in Scala, Java, and Python, which can integrate with Hadoop. It is an engine for large-scale data processing and acts as a replacement for MapReduce.
6) MATLAB –
MATLAB stands for Matrix Laboratory and is mostly use by scientists, engineers, and mathematicians to analyze and visualize data. It has various tools such as image processing, signal processing, etc which can be use to build intelligent systems quickly. It’s proprietary software but it also offers a free community edition. With limited features like other commercial software such as SPSS or Minitab.
7) IBM Watson –
IBM Watson is one of the most powerful machine learning tools available today at present time. It uses DeepQA technology which divides the queries into separate semantic classes and stores them in a knowledge base. The answer is delivering in the form of evidence which is use to calculate the most relevant hypothesis.
8) Google Prediction API –
Google Prediction API allows businesses to make smarter decisions by predicting an outcome based on historical data and learning. It helps developers build intelligent apps with features like fraud detection, prediction of click-through rate, etc.
Conclusion:
Machine learning is a powerful tool for solving problems with the help of Automated Intelligence. It is the art of building smart systems which can learn from data. And use it to make predictions or take action accordingly. As per Peter DeCaprio, it is very important for organizations to invest in this tool as its growing day by day. And businesses using machine learning tools outperform their competitors making more profits than expected.
