A Complete Machine Learning Guide 2020 for Beginners

Machine learning is focused on enabling machines and robotics to perform practical tasks and actions that are being carried out by humans. From autonomous cars to deliver packages to the right shelf, machine learning is using the power of artificial intelligence to create powerful and efficient work environments. But these are all general explanations about machine learning. How are we supposed to perceive machine learning, and how is it related to AI? What is the future of machine learning in practical applications? These are some questions that we are going to explore in this guide about machine learning, and it’s relation with AI. What is Machine Learning (ML)? The general definition of machine learning explains it as a process of enabling the computer systems and machines to make predictive moves according to the environment, based on the stored data and instructions. The predictive movements can be related to any field of life. The goal of the machine can be differentiating males and females from the given database or counting the number of people in a room. From small actions like writing captions for YouTube videos to predict the sale growth, machine learning can do anything if programmed intelligently. You might be wondering that how machine learning programs are different from simple computer programs. Well, a simple computer program is based on different scenarios to differentiate males from females. But in the case of machine learning, the system is fed with huge data with different attributes of both genders. The data can be millions of pictures of other factors like weight, height, and body shape to differentiate males and females. Machine learning empowers the systems and machines to learn from the given data and instructions. The more availability of data and information makes machine learning more reliable and accurate. Difference between Machine Learning and Artificial Intelligence You can consider machine learning as the sub-branch of AI. The founding idea of AI was to create such machines that can think and act like a human mind. Most of the AI-powered systems are capable of: • Learning • Planning • Reasoning • Problem-solving • Perception • Manipulation • Motion Machine learning also exhibits all of these attributes with some additional traits like evolutionary computation. These intelligent algorithms use different sets of information and data to make decision-based on the situations. Machine learning mimics the thinking approach of the human mind. The best examples of machine learning in practical life are autopilot for planes and autonomous cars. Categories of Machine Learning At a higher level, machine learning is divided into two main categories. Both categories use different approaches to feed machines and systems with relevant data and information. 1. Supervised Learning In supervised learning, machines are taught to perform specific actions and movements by using different examples. Imagine if you want a system that can identify different digits, the system will be fed with several labeled images. Machine learning will analyze different patterns and pixels to identify each and every digit from the given data. Based on the analysis, the system will be able to distinguish different digits like 2, 4, and 9. You will have to provide a massive amount of data to teach a machine. In some cases, millions of examples are required to enable the system to accurately differentiate the objects. Labeling the data for the understanding of the machines is a time taking process. Some services use manual ways to label data, but Facebook uses publically available data to teach the systems. ImageNet is a huge collection of images, and this database was labeled by 50,000 people over a long period of two years. 2. Unsupervised Learning Instead of using different examples to operate, unsupervised learning uses different algorithms and patterns to categorize the given databases on their similarities or differences. The categorization of houses available for rent on Airbnb is the perfect example of unsupervised learning. Google News puts different relevant news in one category, is also an example of unsupervised learning. The smart algorithms are the base of unsupervised learning that enables the machine to sort out any type of data on the basis of their anomalies. What is Deep Learning? Machine learning is further broken down to another branch, deep learning. Computer vision and voice recognition are the perfect examples of deep learning where neural networks are expanded into the system. Recurrent neural networks are generally used in the systems for voice recognition, and convolutional neural networks are generally utilized in the image recognition systems. Why is Machine Learning Successful? Machine learning and AI are not new techniques. These concepts have been around for a long time, but people have started taking a lot of interest in the practical applications of these technologies for the last few years. In the sectors of computer vision, image recognition, and voice recognition, machine learning did miracles. The availability of the massive amount of data and efforts of researchers are the main reasons behind the successful applications of machine learning in different fields. The resources to train systems with machine learning are available online for the public. You can use image databases of Google and Amazon to teach your systems. Companies have started the production of machine learning systems at the industrial level. Conclusion Machine learning and AI are the pillars of the modern internet. Various smart technologies and tools use machine learning and AI for efficient and accurate working of machines. The suggested products on your Amazon profile and recommended series on Netflix are the results of machine learning. These systems analyze the user’s activity and intent to suggest the best suitable products. Google uses machine learning in the searching algorithms to serve users with the most accurate and relevant data according to his intent and requirements. Although recent developments in machine learning have made systems more accurate and reliable, it will take some more time to replace humans from industries. Researchers are working on expanding the scope of machine learning and AI for the public, and the results are going to be astonishing in the near future.