Ultimate Learning Guide To Machine Learning
Machine learning is a branch of Artificial Intelligence that focuses on the concept of machines making use of data entered into them and improving their performance based on specific patterns. Its main aim is to empower machines to an extent that they can make certain, basic decisions with as little human intervention as possible.
A little history
It would not be incorrect to say that machine learning has greatly evolved in the past few years. How machine learning was developed was based on researchers’ interest to enable computers to learn from data. Pattern recognition was where it all started. Machine learning of today has enabled computers to learn from manually entered data and then adapt independently. In everyday life, we come across various examples of machine learning that we unconsciously use to add value to our lives and make tasks easier. One of the biggest examples of machine learning is the automated recommendations we receive on Netflix and/or Amazon.
The link between machine learning and artificial intelligence
It is common practice to often confuse machine learning with artificial intelligence. Artificial Intelligence is the broader concept that enables machines to act in a “smart” way and perform various human-like tasks. Machine Learning, on the other hand, is a branch of artificial intelligence with the main focus on training machines how to learn from data. Machine Learning operates on the aim to come up with the most solutions to a certain problem whereas artificial intelligence works to develop the best and most efficient solution.
Why is machine learning important in today’s world?
Having access to technology that can process and analyze large quantities of data has advantages of its own. Businesses in the corporate world, as well as online, have countless applications of machine learning that range from developing a better production process to providing the best customer service and online experience to their customers. Through the processing and analyzing of huge amounts of data at superb speeds, businesses have a better chance at identifying and availing the best opportunities that present themselves and to also avoid risks that might harm the organization in any way.
The most common industries that use machine learning
Industries that deal with large quantities of data on a daily basis can’t help but recognize the effectiveness and the necessity of incorporating machine learning into their systems. Financial institutions and organizations such as banks and insurance companies have two uses of the technology of machine learning; preventing fraud and identifying insights. These insights are crucial to help banks and investors learn about better opportunities that come their way.
Another major use of machine learning is by the government. Because of the need to provide public services and keep the records for millions of people, the government makes the most use of machine learning for analyzing sensor data and also preventing and detecting fraud and identity theft.
The different types of machine learning
While there are several customized and not widely known machine learning methods, the two most commonly used and adopted methods are supervised and unsupervised machine learning.
Supervised learning is based on algorithms that operate on inputs and outputs that are already known. How this works is that the input and the desired output is fed into the machine and once the machine processes the input, it automatically compares it to the desired output value. If there are any discrepancies, the machine identifies the error and then improves the model accordingly.
This is the type of machine learning that is best used in industries where there are predictions involved. The greatest example is that of historical data being used to predict the most likely events in the future. While this can be used to predict natural disasters, it can also be used to anticipate which of the insurance policyholders is most likely to file a claim. Again, these are just a couple of examples.
On the other hand, Unsupervised Learning is used in areas and industries where there is no use of historical labels. This means that there is “no right answer”. This type of machine learning is best used in the business world where customers that have similar activity and interests can be grouped into one category that can be then used to target them in marketing campaigns. This is what is called transactional data. Some more examples of using this technology are automatic maps and GPS tracking.
Another type of machine learning that is somewhat commonly used is Semisupervised learning. This is the technology that makes the use of both unlabelled and labeled data for the training of the machines. Labeling is usually a costly process which is why semisupervised learning is a middle-ground that can be used for companies and organizations that do not have much finance in the IT department. Semisupervised can be best explained with the example of facial recognition through webcams.
How machine learning works
What is important to understand about machine learning is that it is not sufficient to only implement this technology without the necessary steps to take with it. In order to make the best use of machine learning, you have to understand that there needs to be a thorough understanding and application of relevant algorithms, processes and tools. One of the biggest advantages of machine learning is that it improves over time and has the ability to develop the best solutions and outputs to the data with minimal human intervention. With the fewest skills and manual labor required, machine learning can help an organization greatly improve production processes and even the provision of services with automated tasks and fast data processing. Incorporating machine learning into an organization is the most progressive development you can make which can help your business develop better customer service, customer relation and also stay in-line with competitors in the ever-changing business world.
About This Machine Learning Guide
This guide offers the most insightful articles, educational videos, expert insights, specialist tips and best free tutorials about machine learning from around the internet. The learning guide is split into four levels: introduction, basics, advanced and expert. You can learn at your own pace. Each item shows an estimated reading or watching time, allowing you to easily plan when you want to read or watch each item. Below you’ll find a table of contents that enables you to easily find a specific topic you might be interested in.
What is Machine Learning
This 8-minute Machine Learning basics video will help you understand what is Machine Learning, what are the types of Machine Learning – supervised, unsupervised & reinforcement learning, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves.
Why Machine Learning Matters
Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.
All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
History of Machine Learning
The first case of neural networks was in 1943, when neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper about neurons, and how they work. They decided to create a model of this using an electrical circuit, and therefore the neural network was born.
In 1950, Alan Turing created the world-famous Turing Test. This test is fairly simple – for a computer to pass, it has to be able to convince a human that it is a human and not a computer.
Human Learning vs Machine Learning
Machine learning has become a rapidly growing subset of artificial intelligence research. The application of so-called neural network software, mimicking functions of the human brain coupled with the availability of low-cost massive computational hardware resources provides opportunities to solve problems which so far have relied on human brain-power.
Teaching Machines to Think Like Humans
Artificial Intelligence is a collective endeavor that relies on many Giants, from very different fields, that will all need to work closely together if we are to ever achieve the end goal. Kiela is a postdoctoral researcher at Facebook Artificial Intelligence Research (FAIR) in New York. His primary research interests lie in computational linguistics, specifically natural language understanding, and machine learning.
Teaching Machines To Learn Languages
When IBM was making mainframe computers in the 1960s, an idea took hold. The idea was that if computers could be taught to process language— human spoken English, French, German, Japanese, not just lines of computer code—great things would follow. So language processing became a substantial discipline in computer engineering.
The Man-Machine Framework
For systems to succeed, especially systems for data analysis, they must be consciously designed to enhance the capabilities and thinking of people, not to replace them. A man-machine framework must be adopted, where man is in charge and the algorithm is an extension.
Business Challenges With Machine Learning
While there are significant opportunities to achieve business impact with machine learning, there are a number of challenges too. Many of these issues are related to the sudden and dramatic rise in awareness of machine learning. I’ll talk about some of these challenges in this article and how to overcome them.
Working with Big Data
Finding real value in data is critical to every business today. But before we mine it for business insights, we need to access this data from all of our relevant sources accurately, safely, and quickly. How? With a foundation that integrates multiple data sources and can transition workloads across on-premise and cloud boundaries.
Machine Learning Will Revolutionize Big Data
The implementation of Big Data and AI technologies for quantum computing is complex, and this transformation will not happen overnight. It is a journey: many enterprise organizations first embarked on this journey with machine learning and then quantum computing.
Big Data Characteristics, Types, Benefits & Examples
There are certain basic tenets of Big Data that will make it even simpler to answer what is Big Data:
— It refers to a massive amount of data that keeps on growing exponentially with time.
— It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques.
— It includes data mining, data storage, data analysis, data sharing, and data visualization.
— The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data.
Artificial intelligence vs Machine Learning vs Deep Learning
Nowadays many misconceptions are there related to the words machine learning, deep learning and artificial intelligence(AI), most of the people think all these things are same whenever they hear the word AI, they directly relate that word to machine learning or vice versa, well yes, these things are related to each other but not the same.
The Difference Between Business Intelligence vs Business Analytics
Business analytics software is either a child or parent (depending on who you ask) of the business intelligence category. Like BI, it is primarily used to analyze historical data, but with the intention of predicting business trends. It also usually has an eye toward improvement and preparation for change.
Challenges of Predictive Analytics
Predictive analytics is a branch of analytics that uses historical data, machine learning, and Artificial Intelligence (AI) to help users act preemptively. Predictive analytics answers this question: “What is most likely to happen based on my current data, and what can I do to change that outcome?”
Machine Learning Methods
A machine learning algorithm, also called model, is a mathematical expression that represents data in the context of a problem, often a business problem. The aim is to go from data to insight. For example, if an online retailer wants to anticipate sales for the next quarter, they might use a machine learning algorithm that predicts those sales based on past sales and other relevant data. Similarly, a windmill manufacturer might visually monitor important equipment and feed the video data through algorithms trained to identify dangerous cracks.
The ten methods described offer an overview — and a foundation you can build on as you hone your machine learning knowledge and skill.
Steps To Write Any Machine Learning Algorithm From Scratch
Some algorithms are just more complicated than others, so start with something simple, such as the single-layer Perceptron.
I’ll walk you through the following 6-step process to write algorithms from scratch, using the Perceptron as a case-study:
1) Get a basic understanding of the algorithm
2) Find some different learning sources
3) Break the algorithm into chunks
4) Start with a simple example
5) Validate with a trusted implementation
6) Write up your process
Applying Machine Learning to Business Problems
In this article, we’ll break down categories of business problems that are commonly handled by ML, and we’ll also provide actionable advice to begin an ML initiative with the right approach and perspective (even it’s the first such project you’ve undertaken at your company).
Best of all, we’ll reference real business use cases, along with quotes and perspectives about “how to solve business problems with ML” from our network of AI researchers and executives. By the end of this article, you’ll have a good idea as to whether any of your present business challenges might be handled well with ML.
Beginner’s Guide to Machine Learning with Python
If your aim is growing into a successful coder, you need to know a lot of things. But, for Machine Learning & Data Science, it is pretty enough to master at least one coding language and use it confidently. So, calm down, you don’t have to be a programming genius.
For successful Machine Learning journey, it’s necessary to choose the appropriate coding language right from the beginning, as your choice will determine your future.
How Google is Using Machine Learning & AI
Google has been a powerful force in championing the use of deep learning – a technology now so prevalent in cutting edge applications that its name is pretty much synonymous with artificial intelligence. There’s a simple reason for this – it works. Putting deep learning to work has enabled data scientists to crack a number of difficult cases which had proved challenging for decades, such as speech and image recognition, and natural language generation.
Examples Of AI And Machine Learning In Practice
There are so many amazing ways artificial intelligence and machine learning are used behind the scenes to impact our everyday lives and inform business decisions and optimize operations for some of the world’s leading companies. Here are 27 amazing practical examples of AI and machine learning.
Use-Cases of Machine Learning in Business Applications
In B2B and B2C businesses, this capability is proving to be particularly useful in identifying patterns across large swaths of customer and user data and helping drive better company outcomes: more influential content creation, a larger number of paid converters, saved marketing costs, and the list goes on.
In this article, we spotlight four machine learning vendors and explore how their applications are being used by four B2B companies to help drive business decisions and better reach and serve their customers.
The Future of Machine Learning and Artificial Intelligence
Gartner has also predicted that by 2020, AI will become one of the top five investment priorities for at least 30 percent of Chief Information Officers. Global software vendors are after this new gold rush. Unfortunately, though the promise of new revenue has pushed software business owners to invest in AI technologies, the truth is that most organizations do not have skilled staff to embrace AI.
Types of Machine Learning Algorithms and Use Cases
In this article, we will study the various types of machine learning algorithms and their use-cases. We will study how Baidu is using supervised learning-based facial recognition for intelligent airport check-in and how Google is making use of Reinforcement Learning to develop an intelligent platform that would answer your queries.
Guide to Creating R and Python Libraries
R and Python are the bread and butter of today’s machine learning languages. R provides powerful statistics and quick visualizations, while Python offers an intuitive syntax, abundant support, and is the choice interface to today’s major AI frameworks.
In this article, we’ll look at the steps involved in creating libraries in R and Python. This is a skill every machine learning practitioner should have in their toolbox. Libraries help us organize our code and share it with others, offering packaged functionality to the data community.
Starting with Machine Learning
The benefit of machine learning are the predictions and the models that make predictions.
To have skill at applied machine learning means knowing how to consistently and reliably deliver high-quality predictions on problem after problem. You need to follow a systematic process.
Below is a 5-step process that you can follow to consistently achieve above-average results on predictive modeling problems.
Free Machine Learning Tutorials
This is a library of more than 90 free tutorials about machine learning, spread in three different levels:
1) Fresh start with Machine Learning tutorials and become a pro.
2) Develop the understanding of ML with new Deep Learning chapters.
3) Put your Machine Learning and Python Skills to work.
Further Reading: Best Machine Learning Books
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
The Hundred-Page Machine Learning Book. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests.
Machine Learning For Absolute Beginners: A Plain English Introduction. This book has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.
Machine Learning: 4 Books in 1: A Complete Overview for Beginners to Master the Basics of Python Programming and Understand How to Build Artificial Intelligence Through Data Science. Created with the beginner in mind, this powerful bundle delves into the fundamentals behind Python and Machine Learning, from basic code and mathematical formulas to complex neural networks and ensemble modeling. Inside, you’ll discover everything you need to know to get started with Python and Machine Learning
Further Learning: Best Machine Learning Courses
Machine Learning Course by Stanford University. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.
Data Science: Machine Learning Course by Harvard University. In this course, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, and how to use a set of data to discover potentially predictive relationships.
Machine Learning Course by the University of Washington. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
Machine Learning A-Z: Hands-On Python & R In Data Science. This course has been designed by two professional Data Scientists so that they can share their knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. They will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.