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Introduction To Machine Learning:

Undoubtedly, Machine Learning is the most in-demand technology in todays market. Its applications range from self-driving cars to predicting deadly diseases such as ALS. The high demand for Machine Learning skills is the motivation behind this blog. In this blog on Introduction To Machine Learning, you will understand all the basic concepts of Machine Learning and a Practical Implementation of Machine Learning by using the R language.

To get in-depth knowledge on Data Science, you can enroll for liveData Science Certification Trainingby Edureka with 24/7 support and lifetime access.

The following topics are covered in this Introduction To Machine Learning blog:

Ever since the technical revolution, weve been generating an immeasurable amount of data. As per research, we generate around 2.5 quintillion bytes of data every single day! It is estimated that by 2020, 1.7MB of data will be created every second for every person on earth.

With the availability of so much data, it is finally possible to build predictive models that can study and analyze complex data to find useful insights and deliver more accurate results.

Top Tier companies such as Netflix and Amazon build such Machine Learning models by using tons of data in order to identify profitable opportunities and avoid unwanted risks.

Heres a list of reasons why Machine Learning is so important:

Importance Of Machine Learning Introduction To Machine Learning Edureka

To give you a better understanding of how important Machine Learning is, lets list down a couple of Machine Learning Applications:

These were a few examples of how Machine Learning is implemented in Top Tier companies. Heres a blog on the Top 10 Applications of Machine Learning, do give it a read to learn more.

Now that you know why Machine Learning is so important, lets look at what exactly Machine Learning is.

The term Machine Learning was first coined by Arthur Samuel in the year 1959. Looking back, that year was probably the most significant in terms of technological advancements.

If you browse through the net about what is Machine Learning, youll get at least 100 different definitions. However, the very first formal definition was given by Tom M. Mitchell:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.

In simple terms, Machine learning is a subset of Artificial Intelligence (AI) which provides machines the ability to learn automatically & improve from experience without being explicitly programmed to do so. In the sense, it is the practice of getting Machines to solve problems by gaining the ability to think.

But wait, can a machine think or make decisions? Well, if you feed a machine a good amount of data, it will learn how to interpret, process and analyze this data by using Machine Learning Algorithms, in order to solve real-world problems.

Before moving any further, lets discuss some of the most commonly used terminologies in Machine Learning.

Algorithm: A Machine Learning algorithm is a set of rules and statistical techniques used to learn patterns from data and draw significant information from it. It is the logic behind a Machine Learning model. An example of a Machine Learning algorithm is the Linear Regression algorithm.

Model: A model is the main component of Machine Learning. A model is trained by using a Machine Learning Algorithm. An algorithm maps all the decisions that a model is supposed to take based on the given input, in order to get the correct output.

Predictor Variable: It is a feature(s) of the data that can be used to predict the output.

Response Variable: It is the feature or the output variable that needs to be predicted by using the predictor variable(s).

Training Data: The Machine Learning model is built using the training data. The training data helps the model to identify key trends and patterns essential to predict the output.

Testing Data: After the model is trained, it must be tested to evaluate how accurately it can predict an outcome. This is done by the testing data set.

What Is Machine Learning? Introduction To Machine Learning Edureka

To sum it up, take a look at the above figure. A Machine Learning process begins by feeding the machine lots of data, by using this data the machine is trained to detect hidden insights and trends. These insights are then used to build a Machine Learning Model by using an algorithm in order to solve a problem.

The next topic in this Introduction to Machine Learning blog is the Machine Learning Process.

The Machine Learning process involves building a Predictive model that can be used to find a solution for a Problem Statement. To understand the Machine Learning process lets assume that you have been given a problem that needs to be solved by using Machine Learning.

Machine Learning Process Introduction To Machine Learning Edureka

The problem is to predict the occurrence of rain in your local area by using Machine Learning.

The below steps are followed in a Machine Learning process:

Step 1: Define the objective of the Problem Statement

At this step, we must understand what exactly needs to be predicted. In our case, the objective is to predict the possibility of rain by studying weather conditions. At this stage, it is also essential to take mental notes on what kind of data can be used to solve this problem or the type of approach you must follow to get to the solution.

Step 2: Data Gathering

At this stage, you must be asking questions such as,

Once you know the types of data that is required, you must understand how you can derive this data. Data collection can be done manually or by web scraping. However, if youre a beginner and youre just looking to learn Machine Learning you dont have to worry about getting the data. There are 1000s of data resources on the web, you can just download the data set and get going.

Coming back to the problem at hand, the data needed for weather forecasting includes measures such as humidity level, temperature, pressure, locality, whether or not you live in a hill station, etc. Such data must be collected and stored for analysis.

Step 3: Data Preparation

The data you collected is almost never in the right format. You will encounter a lot of inconsistencies in the data set such as missing values, redundant variables, duplicate values, etc. Removing such inconsistencies is very essential because they might lead to wrongful computations and predictions. Therefore, at this stage, you scan the data set for any inconsistencies and you fix them then and there.

Step 4: Exploratory Data Analysis

Grab your detective glasses because this stage is all about diving deep into data and finding all the hidden data mysteries. EDA or Exploratory Data Analysis is the brainstorming stage of Machine Learning. Data Exploration involves understanding the patterns and trends in the data. At this stage, all the useful insights are drawn and correlations between the variables are understood.

For example, in the case of predicting rainfall, we know that there is a strong possibility of rain if the temperature has fallen low. Such correlations must be understood and mapped at this stage.

Step 5: Building a Machine Learning Model

All the insights and patterns derived during Data Exploration are used to build the Machine Learning Model. This stage always begins by splitting the data set into two parts, training data, and testing data. The training data will be used to build and analyze the model. The logic of the model is based on the Machine Learning Algorithm that is being implemented.

In the case of predicting rainfall, since the output will be in the form of True (if it will rain tomorrow) or False (no rain tomorrow), we can use a Classification Algorithm such as Logistic Regression.

Choosing the right algorithm depends on the type of problem youre trying to solve, the data set and the level of complexity of the problem. In the upcoming sections, we will discuss the different types of problems that can be solved by using Machine Learning.

Step 6: Model Evaluation & Optimization

After building a model by using the training data set, it is finally time to put the model to a test. The testing data set is used to check the efficiency of the model and how accurately it can predict the outcome. Once the accuracy is calculated, any further improvements in the model can be implemented at this stage. Methods like parameter tuning and cross-validation can be used to improve the performance of the model.

Step 7: Predictions

Once the model is evaluated and improved, it is finally used to make predictions. The final output can be a Categorical variable (eg. True or False) or it can be a Continuous Quantity (eg. the predicted value of a stock).

In our case, for predicting the occurrence of rainfall, the output will be a categorical variable.

So that was the entire Machine Learning process. Now its time to learn about the different ways in which Machines can learn.

A machine can learn to solve a problem by following any one of the following three approaches. These are the ways in which a machine can learn:

Supervised learning is a technique in which we teach or train the machine using data which is well labeled.

To understand Supervised Learning lets consider an analogy. As kids we all needed guidance to solve math problems. Our teachers helped us understand what addition is and how it is done. Similarly, you can think of supervised learning as a type of Machine Learning that involves a guide. The labeled data set is the teacher that will train you to understand patterns in the data. The labeled data set is nothing but the training data set.

Supervised Learning Introduction To Machine Learning Edureka

Consider the above figure. Here were feeding the machine images of Tom and Jerry and the goal is for the machine to identify and classify the images into two groups (Tom images and Jerry images). The training data set that is fed to the model is labeled, as in, were telling the machine, this is how Tom looks and this is Jerry. By doing so youre training the machine by using labeled data. In Supervised Learning, there is a well-defined training phase done with the help of labeled data.

Unsupervised learning involves training by using unlabeled data and allowing the model to act on that information without guidance.

Think of unsupervised learning as a smart kid that learns without any guidance. In this type of Machine Learning, the model is not fed with labeled data, as in the model has no clue that this image is Tom and this is Jerry, it figures out patterns and the differences between Tom and Jerry on its own by taking in tons of data.

Unsupervised Learning Introduction To Machine Learning Edureka

For example, it identifies prominent features of Tom such as pointy ears, bigger size, etc, to understand that this image is of type 1. Similarly, it finds such features in Jerry and knows that this image is of type 2. Therefore, it classifies the images into two different classes without knowing who Tom is or Jerry is.

Reinforcement Learning is a part of Machine learning where an agent is put in an environment and he learns to behave in this environment by performing certain actions and observing the rewards which it gets from those actions.

Panic? Yes, of course, initially we all would. But as time passes by, you will learn how to live on the island. You will explore the environment, understand the climate condition, the type of food that grows there, the dangers of the island, etc. This is exactly how Reinforcement Learning works, it involves an Agent (you, stuck on the island) that is put in an unknown environment (island), where he must learn by observing and performing actions that result in rewards.

Reinforcement Learning is mainly used in advanced Machine Learning areas such as self-driving cars, AplhaGo, etc.

To better understand the difference between Supervised, Unsupervised and Reinforcement Learning, you can go through this short video.

So that sums up the types of Machine Learning. Now, lets look at the type of problems that are solved by using Machine Learning.

Type of Problems Solved Using Machine Learning Introduction To Machine Learning Edureka

Consider the above figure, there are three main types of problems that can be solved in Machine Learning:

Heres a table that sums up the difference between Regression, Classification, and Clustering.

Regression vs Classification vs Clustering Introduction To Machine Learning Edureka

Now to make things interesting, I will leave a couple of problem statements below and your homework is to guess what type of problem (Regression, Classification or Clustering) it is:

Dont forget to leave your answer in the comment section.

Now that you have a good idea about what Machine Learning is and the processes involved in it, lets execute a demo that will help you understand how Machine Learning really works.

A short disclaimer: Ill be using the R language to show how Machine Learning works. R is a Statistical programming language mainly used for Data Science and Machine Learning. To learn more about R, you can go through the following blogs:

Now, lets get started.

Problem Statement: To study the Seattle Weather Forecast Data set and build a Machine Learning model that can predict the possibility of rain.

Data Set Description: The data set was gathered by researching and observing the weather conditions at the Seattle-Tacoma International Airport. The dataset contains the following variables:

The target or the response variable, in this case, is RAIN. If you notice, this variable is categorical in nature, i.e. its value is of two categories, either True or False. Therefore, this is a classification problem and we will be using a classification algorithm called Logistic Regression.

Even though the name suggests that it is a Regression algorithm, it actually isnt. It belongs to the GLM (Generalised Linear Model) family and thus the name Logistic Regression.

Follow this, Comprehensive Guide To Logistic Regression In R blog to learn more about Logistic Regression.

Logic: To build a Logistic Regression model in order to predict whether or not it will rain on a particular day based on the weather conditions.

Now that you know the objective of this demo, lets get our brains working and start coding.

Step 1: Install and load libraries

R provides 1000s of packages to run Machine Learning algorithms and mathematical models. So the first step is to install and load all the relevant libraries.

Each of these libraries serves a specific purpose, you can read more about the libraries in the official R Documentation.

Step 2: Import the Data set

Lucky for me I found the data set online and so I dont have to manually collect it. In the below code snippet, Ive loaded the data set into a variable called data.df by using the read.csv() function provided by R. This function is to load a Comma Separated Version (CSV) file.

Step 3: Studying the Data Set

Lets take a look at a couple of observations in the data set. To do this we can use the head() function provided by R. This will list down the first 6 observations in the data set.

Now, lets look at the structure if the data set by using the str() function.

In the above code, you can see that the data type for the DATE and RAIN variable is not correctly formatted. The DATE variable must be of type Date and the RAIN variable must be a factor.

Step 4: Data Cleaning

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Machine Learning services in the cloud are a critical area of the modern computing landscape, providing a way for organizations to better analyze data and derive new insights. Accessing these service via the cloud tends to be efficient in terms of cost and staff hours.

Machine Learning (often abbreviated as ML) is a subset of Artificial Intelligence (AI) and attempts to 'learn' from data sets in several different ways, including both supervised and unsupervised learning. There are many different technologies that can be used for machine learning, with a variety of commercial tools as well as open source framework.s

While organizations can choose to deploy machine learning frameworks on premises, it is typically a complex and resource intensive exercise. Machine Learning benefits from specialized hardware including inference chips and optimized GPUs. Machine Learning frameworks can also often be challenging to deploy and configure properly. Complexity has led to the rise of Machine Learning services in the cloud, that provide the right hardware and optimally configured software to that enable organizations to easily get started with Machine Learning.

There are several key features that are part of most machine learning cloud services.

AutoML - The automated Machine Learning feature automatically helps to build the right model.Machine Learning Studio - The studio concept is all about providing a developer environment where machine learning models and data modelling scenarios can be built.Open source framework support - The ability to support an existing framework such as TensorFlow, MXNet and Caffe is important as it helps to enable model portability.

When evaluating the different options for machine learning services in the cloud, consider the following criteria:

In this Datamation top companies list, we spotlight the vendors that offer the top machine learning services in the cloud.

Value proposition for potential buyers: Alibaba is a great option for users that have machine learning needs where data sets reside around the world and especially in Asia, where Alibaba is a leading cloud service.

Value proposition for potential buyers: Amazon Web Services has the broadest array of machine learning services in the cloud today, leading with its SageMaker portfolio that includes capabilities for building, training and deploying models in the cloud.

Value proposition for potential buyers: Google's set of Machine Learning services are also expansive and growing, with both generic as well as purpose built services for specific use-cases.

Value proposition for potential buyers: IBM Watson Machine learning enables users to run models on any cloud, or just on the the IBM Cloud

Value proposition for potential buyers: For organizations that have already bought into Microsoft Azure cloud, Azure Machine Learning is good fit, providing a cloud environment to train, deploy and manage machine learning models.

Value proposition for potential buyers: Oracle Machine learning is a useful tools for organizations already using Oracle Cloud applications, to help build data mining notebooks.

Value proposition for potential buyers: Salesforce Einstein is a purpose built machine learning platform that is tightly integrated with the Salesforce platform.

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Top Machine Learning Services in the Cloud - Datamation

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One Technologies Partners with Arrikto to Uniquely Tailor its ScoreSense Consumer Credit Platform to Each Individual Customer

DALLAS, Jan. 30, 2020 /PRNewswire/ --To provide customers with the most personalized credit experience possible, One Technologies, LLC has partnered with data management innovator Arrikto Inc. (https://www.arrikto.com/)to incorporate Machine Learning (ML) into its ScoreSense credit platform.

ScoreSense, http://www.ScoreSense.com (PRNewsfoto/One Technologies, LLC)

"To truly empower consumers to take control of their financial future, we must rely on insights from real datanot on assumptions and guesswork," said Halim Kucur, Chief Product Officer at One Technologies, LLC. The innovations we have introduced provide data-driven intelligence about customers' needs and wants before they know this information themselves."

"ScoreSense delivers state-of-the-art credit information through their ongoing investment in the most cutting-edge machine learning products the industry has to offer," said Constantinos Venetsanopoulos, Founder and CEO of Arrikto Inc. "Our partnership has been a big success because One Technologies aligns seamlessly with the most forward-looking developers in the ML space and understands the tremendous value of data for serving customers better."

ScoreSense (https://www.scoresense.com) serves as a one-stop digital resource where consumers can access credit scores and reports from all three main credit bureausTransUnion, Equifax, and Experianand comprehensively pinpoint the factors which are most affecting their credit.

About One Technologies

One Technologies, LLC harnesses the power of technology, analytics and its people to create solutions that empower consumers to make more informed decisions about their financial lives. The firm's consumer credit products include ScoreSense, which enables members to seamlessly access, interact with, and understand their credit profiles from all three main bureaus using a single application. The ScoreSense platform is continually updated to give members deeper insights, personalized tools and one-on-one Customer Care support that can help them make the most sense of their credit.

One Technologies is headquartered in Dallas and was established in October 2000. For more information, please visit https://onetechnologies.net/.

Media Contact

Laura MarvinJConnelly for One Technologies646-922-7774 OT@jconnelly.com

View original content to download multimedia:http://www.prnewswire.com/news-releases/scoresense-leverages-machine-learning-to-take-its-customer-experience-to-the-next-level-300995934.html

SOURCE One Technologies, LLC

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This post is part of AI education, a series of posts that review and explore educational content on data science and machine learning.

With data science and machine learning skills being in high demand, theres increasing interest in careers in both fields. But with so many educational books, video tutorials and online courses on data science and machine learning, finding the right starting point can be quite confusing.

Readers often ask me for advice on the best roadmap for becoming a data scientist. To be frank, theres no one-size-fits-all approach, and it all depends on the skills you already have. In this post, I will review three very good introductory books on data science and machine learning.

Based on your background in math and programming, the two fundamental skills required for data science and machine learning, youll surely find one of these books a good place to start.

Data scientists and machine learning engineers sit at the intersection of math and programming. To become a good data scientist, you dont need to be a crack coder who knows every single design pattern and code optimization technique. Neither do you need to have an MSc in math. But you must know just enough of both to get started. (You do need to up your skills in both fields as you climb the ladder of learning data science and machine learning.)

If you remember your high school mathematics, then you have a strong base to begin the data science journey. You dont necessarily need to recall every formula they taught you in school. But concepts of statistics and probability such as medians and means, standard deviations, and normal distributions are fundamental.

On the coding side, knowing the basics of popular programming languages (C/C++, Java, JavaScript, C#) should be enough. You should have a solid understanding of variables, functions, and program flow (if-else, loops) and a bit of object-oriented programming. Python knowledge is a strong plus for a few reasons: First, most data science books and courses use Python as their language of choice. Second, the most popular data science and machine learning libraries are available for Python. And finally, Pythons syntax and coding conventions are different from other languages such as C and Java. Getting used to it takes a bit of practice, especially if youre used to coding with curly brackets and semicolons.

Written by Sinan Ozdemir, Principles of Data Science is one of the best intros to data science that Ive read. The book keeps the right balance between math and coding, theory and practice.

Using examples, Ozdemir takes you through the fundamental concepts of data science such as different types of data and the stages of data science. You will learn what it means to clean your data, normalize it and split it between training and test datasets.

The book also contains a refresher on basic mathematical concepts such as vector math, matrices, logarithms, Bayesian statistics, and more. Every mathematical concept is interspersed with coding examples and introduction to relevant Python data science libraries for analyzing and visualizing data. But you have to bring your own Python skills. The book doesnt have any Python crash course or introductory chapter on the programming language.

What makes the learning curve of this book especially smooth is that it doesnt go too deep into the theories. It gives you just enough knowledge so that you can make optimal uses of Python libraries such as Pandas and NumPy, and classes such as DataFrame and LinearRegression.

Granted, this is not a deep dive. If youre the kind of person who wants to get to the bottom of every data science and machine learning concept and learn the logic behind every library and function, Principles of Data Science will leave you a bit disappointed.

But again, as I mentioned, this is an intro, not a book that will put you on a data science career level. Its meant to familiarize you with what this growing field is. And it does a great job at that, bringing together all the important aspects of a complex field in less than 400 pages.

At the end of the book, Ozdemir introduces you to machine learning concepts. Compared to other data science textbooks, this section of Principles of Data Science falls a bit short, both in theory and practice. The basics are there, such as the difference between supervised and unsupervised learning, but I would have liked a bit more detail on how different models work.

The book does give you a taste of different ML algorithms such as regression models, decision trees, K-means, and more advanced topics such as ensemble techniques and neural networks. The coverage is enough to whet your appetite to learn more about machine learning.

As the name suggests, Data Science from Scratch takes you through data science from the ground up. The author, Joel Grus, does a great job of showing you all the nitty-gritty details of coding data science. And the book has plenty of examples and exercises to go with the theory.

The book provides a Python crash course, which is good for programmers who have good knowledge of another programming language but dont have any background in Python. Whats really good about Gruss intro to Python is that aside from the very basic stuff, he takes you through some of the advanced features for handling arrays and matrices that you wont find in general Python tutorial textbooks, such as list comprehensions, assertions, iterables and generators, and other very useful tools.

Moreover, the Second Edition of Data Science from Scratch, published in 2019, leverages some of the advanced features of Python 3.6, including type annotations (which youll love if you come from a strongly typed language like C++).

What makes Data Science from Scratch a bit different from other data science textbooks is its unique way to do everything from scratch. Instead of introducing you to NumPy and Pandas functions that will calculate coefficients and, say, mean absolute errors (MAE) and mean square errors (MSE), Grus shows you how to code it yourself.

He does, of course, remind you that the books sample code is meant for practice and education and will not match the speed and efficiency of professional libraries. At the end of each chapter, he provides references to documentation and tutorials of the Python libraries that correspond to the topic you have just learned. But the from-scratch approach is fun nonetheless, especially if youre one of those I-have-to-know-what-goes-on-under-the-hood type of people.

One thing youll have to consider before diving into this book is, youll need to bring your math skills with you. In the book, Grus codes fundamental math functions, starting from simple vector math to more advanced statistic concepts such as calculating standard deviations, errors, and gradient descent. However, he assumes that you already know how the math works. I guess its okay if youre fine with just copy-pasting the code and seeing it work. But if youve picked up this book because you want to make sense of everything, then have your calculus textbook handy.

After the basics, Data Science from Scratch goes into machine learning, covering various algorithms, including the different flavors of regression models and decision trees. You also get to delve into the basics of neural networks followed by a chapter on deep learning and an introduction to natural language processing.

In short, I would describe Data Science with Python as a fully hands-on introduction to data science and machine learning. Its the most practice-driven book on data science and machine learning that Ive read. The authors have done a great job of bringing together the right data samples and practice code to get you acquainted with the principles of data science and machine learning.

The book contains minimal theoretical content and mostly teaches you by taking you through coding labs. If you have a decent computer and an installation of Anaconda or another Python package that has comes bundled with Jupyter Notebooks, then you can probably go through all the exercises with minimal effort. I highly recommend writing the code yourself and avoiding copy-pasting it from the book or sample files, since the entire goal of the book is to learn through practice.

Youll find no Python intro here. Youll dive straight into NumPy, Pandas, and scikit-learn. Theres also no deep dive into mathematical concepts such as correlations, error calculations, z-scores, etc., so youll need to get help from your math book whenever you need a refresher on any of the topics.

Alternatively, you can just type in the code and see Pythons libraries work their magic. Data Science with Python does a decent job of showing you how to put together the right pieces for any data science and machine learning project.

Data Science with Python provides a solid intro to data preparation and visualization, and then takes you through a rich assortment of machine learning algorithms as well as deep learning. There are plenty of good examples and templates you can use for other projects. The book also gives an intro on XGBoost, a very useful optimization library, and the Keras neural network library. Youll also get to fiddle around with convolutional neural networks (CNN), the cornerstone of current advances in computer vision.

Before starting this book, I strongly recommend that you go through a gentler introductory book that covers more theory, such as Ozdemirs Principles of Data Science. It will make the ride less confusing. The combination of the two will leave you with a very strong foundation to tackle more advanced topics.

These are just three of the many data science books that are out there. If youve read other awesome books on the topic, please share your experience in the comments section. There are also plenty of great interactive online courses, like Udemys Machine Learning A-Z: Hands-On Python & R In Data Science (I will be reviewing this one in the coming weeks).

While an intro to data science will give you a good foothold into the world of machine learning and the broader field of artificial intelligence, theres a lot of room for expanding that knowledge.

To build on this foundation, you can take a deeper dive into machine learning. There are plenty of good books and courses out there. One of my favorites is Aurelien Gerons Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow (also scheduled for review in the coming months). You can also go deeper on one of the sub-disciplines of ML and deep learning such as CNNs, NLP or reinforcement learning.

Artificial intelligence is complicated, confusing, and exciting at the same time. The best way to understand it is to never stop learning.

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3 books to get started on data science and machine learning - TechTalks

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