Mastering Machine Learning

| April 18, 2018

article image
This ebook builds on Machine Learning with MATLAB, which reviewed machine learning basics and introduced supervised and unsupervised techniques. Using a heart sounds classifier as an example, we take you through the complete workflow for developing a real-world machine learning application, from loading data to deploying a trained model. For each training phase, we demonstrate the techniques that are critical to achieving accurate models, and help you master the more challenging training tasks, including selecting algorithms, optimizing model parameters, and avoiding overfitting. In this ebook you’ll also learn how to turn a model into a predictive tool by training it on new data, extracting features, and generating code for deployment on an embedded device.

Spotlight

InVenture Capital Corp.

Traditional credit doesn't work in emerging markets; mobile does. Our technologies are unlocking trillions of dollars in purchasing power in the world’s fastest growing economies. InVenture is a mobile technology and data science company that is flipping the traditional credit scoring model by putting power into the hands of newly empowered consumers in emerging markets. Through our mobile app and unique channel, we gather an average of 10,000 discrete data points per user to provide personalized offers and deliver real-time credit.

OTHER ARTICLES

How Machine Learning Can Take Data Science to a Whole New Level

Article | December 21, 2020

Introduction Machine Learning (ML) has taken strides over the past few years, establishing its place in data analytics. In particular, ML has become a cornerstone in data science, alongside data wrangling, and data visualization, among other facets of the field. Yet, we observe many organizations still hesitant when allocating a budget for it in their data pipelines. The data engineer role seems to attract lots of attention, but few companies leverage the machine learning expert/engineer. Could it be that ML can add value to other enterprises too? Let's find out by clarifying certain concepts. What Machine Learning is So that we are all on the same page, let's look at a down-to-earth definition of ML that you can include in a company meeting, a report, or even within an email to a colleague who isn't in this field. Investopedia defines ML as "the concept that a computer program can learn and adapt to new data without human intervention." In other words, if your machine (be it a computer, a smartphone, or even a smart device) can learn on its own, using some specialized software, then it's under the ML umbrella. It's important to note that ML is also a stand-alone field of research, predating most AI systems, even if the two are linked, as we'll see later on. How Machine Learning is different from Statistics It's also important to note that ML is different from Statistics, even if some people like to view the former as an extension of the latter. However, there is a fundamental difference that most people aren't aware of yet. Namely, ML is data-driven while Statistics is, for the most part, model-driven. This statement means that most Stats-based inferences are made by assuming a particular distribution in the data, or the interactions of different variables, and making predictions based on our mathematical models of these distributions. ML may employ distributions in some niche cases, but for the most part, it looks at data as-is, without making any assumptions about it. Machine Learning’s role in data science work Let’s now get to the crux of the matter and explore how ML can be a significant value-add to a data science pipeline. First of all, ML can potentially offer better predictions than most Stats models in terms of accuracy, F1 score, etc. Also, ML can work alongside existing models to form model ensembles that can tackle the problems more effectively. Additionally, if transparency is important to the project stakeholders, there are ML-based options for offering some insight as to what variables are important in the data at hand, for making predictions based on it. Moreover, ML is more parametrized, meaning that you can tweak an ML model more, adapting it to the data you have and ensuring more robustness (i.e., reliability). Finally, you can learn ML without needing a Math degree or any other formal training. The latter, however, may prove useful, if you wish to delve deeper into the topic and develop your own models. This innovation potential is a significant aspect of ML since it's not as easy to develop new models in Stats (unless you are an experienced Statistics researcher) or even in AI. Besides, there are a bunch of various "heuristics" that are part of the ML group of algorithms, facilitating your data science work, regardless of what predictive model you end up using. Machine Learning and AI Many people conflate ML with AI these days. This confusion is partly because many ML models involve artificial neural networks (ANNs) which are the most modern manifestation of AI. Also, many AI systems are employed in ML tasks, so they are referred to as ML systems since AI can be a bit generic as a term. However, not all ML algorithms are AI-related, nor are all AI algorithms under the ML umbrella. This distinction is of import because certain limitations of AI systems (e.g., the need for lots and lots of data) don't apply to most ML models, while AI systems tend to be more time-consuming and resource-heavy than the average ML one. There are several ML algorithms you can use without breaking the bank and derive value from your data through them. Then, if you find that you need something better, in terms of accuracy, you can explore AI-based ones. Keep in mind, however, that some ML models (e.g., Decision Trees, Random Forests, etc.) offer some transparency, while the vast majority of AI ones are black boxes. Learning more about the topic Naturally, it's hard to do this topic justice in a single article. It is so vast that someone can write a book on it! That's what I've done earlier this year, through the Technics Publications publishing house. You can learn more about this topic via this book, which is titled Julia for Machine Learning(Julia is a modern programming language used in data science, among other fields, and it's popular among various technical professionals). Feel free to check it out and explore how you can use ML in your work. Cheers!

Read More

HOW THE CORONAVIRUS (COVID-19) MIGHT BE STOPPED BY DATA SCIENCE

Article | March 16, 2020

We know that data and analytics play a role in everyday products from recommendations on what music we might like to hear to automated re-routing by our GPS system. But how might the power of analytics be brought to bear on a disease that is currently threatening the health and economic welfare of people across the globe?If we rewind the clock to the 1850s, there are two significant examples of how early pioneers in data science made incredible impacts on the world that can provide some insight into what we might see happen next.

Read More

Automotive DevOps Rules of the Road Ahead

Article | March 13, 2020

DevOps will provide over-the-air (OTA), seamless software updates which would allow important and immediate updates without affecting the car’s capabilities through Liquid Software liquid software. OTA updates will enable automakers to fix engine and automotive malfunctions, as well as implement safety standards directly into the program. Tesla is one of the pioneers of over-the-air updates but while its’ cars are off. In total, Tesla’s updates are usually about 30 minutes. Since 2012, hundreds of OTA updates have been sent out by the company to adjust things like speed limit settings, acceleration, battery issues, and even braking distance. Most car manufacturers are behind when it comes to over-the-air software updates.

Read More

CYBERSECURITY STRATEGIES TO MAKE IT NETWORKS MORE RESILIENT TO CYBERATTACKS

Article | February 28, 2020

The increasing use of advanced technologies and the internet have created an attack surface for malicious attackers. With these progressions, businesses’ IT systems are now more vulnerable which has led them to leverage innovative cybersecurity strategies that can thwart and make their networks more resilient to cyberattacks. Cybercriminals can use a variety of attacks against individuals or businesses like accessing, changing or deleting sensitive data; extracting payment; interfering with business processes and more.These kinds of attacks present an evolving danger to organizations, employees and consumers, and can cost them reputation, finances and personal lives to some extent. So, in order to protect IT networks from cyberattacks, it is significant to be aware of the various aspects of cybersecurity.

Read More

Spotlight

InVenture Capital Corp.

Traditional credit doesn't work in emerging markets; mobile does. Our technologies are unlocking trillions of dollars in purchasing power in the world’s fastest growing economies. InVenture is a mobile technology and data science company that is flipping the traditional credit scoring model by putting power into the hands of newly empowered consumers in emerging markets. Through our mobile app and unique channel, we gather an average of 10,000 discrete data points per user to provide personalized offers and deliver real-time credit.

Events