AI & Machine Learning

Every firm is unique. Even within narrow verticals, the overlap in what two different companies need is smaller than you might think. Don’t try to fit your business into a box. If your business has a large amount of data and you are asking yourself, “How can I use AI to build something smart from our data?” Machine learning is just a tool to automate pattern discovery and then make smart predictions based on those patterns. Most of the time, it’s about improving an existing process by making it a little bit smarter.

So you want to build a ML model. No Machine Learning is easier to manage than no Machine Learning. Figuring a way to use high-level services could save you weeks of work, maybe months. In this series of posts, we’ll discuss how to train ML models and deploy them to production, from humble beginnings to world domination. Along the way, we’ll try to take justified and reasonable steps, fighting the evil forces of over-engineering.

The adoption of AI is rapidly growing in the workplace; however, to take full advantage of AI’s opportunities, businesses must understand and overcome lingering doubts from their customers and employees. There’s no question that businesses are at an inflection point with their use of AI. To achieve greater impact, we must change the narrative about lingering concerns. It is critical to educate both employees and customers about AI’s potential and enable them with tools to take advantage of its benefits. 

A soft skill that keeps coming to the forefront is the ability to explain complex machine learning algorithms to a non-technical person. An algorithm is the mathematical life force behind a model. What differentiates models are the algorithms they employ, but without a model, an algorithm is just a mathematical equation hanging out with nothing to do. An algorithm is what is used to train a model, all the decisions a model is supposed to take based on the given input, to give expected output. 

Want to learn more about how your retail business can efficiently use Machine Learning and Data Science? Data Science and Big Data analytics is not a magic pill that can solve all your problems. However, it’s a strong competitive advantage as it gives you knowledge and a better sense of control. What you need and can do is to have a vivid picture of what is going on in your business, as the more info you have at hand, the more clearly you can see that something is going wrong and needs fixing.

Often a classifier will have some confidence value in each category. These are most often generated by probabilistic classifiers. Sometimes, we threshold the probability values. In computer vision, this happens a lot in detection. The optimal threshold varies depends on the tasks. Some performance metrics are sensitive to the threshold.  This post is going to cover some very basic concepts in machine learning, from linear algebra to evaluation metrics. It serves as a nice guide to newbies looking to enter the field.

Predictive analytics can save lives, and Majid Khattak demonstrates how to do just that by using an index of the El-Nino-Southern Oscillation phenomenon to predict weather patterns that lead to severe storms. 

What is Automated Machine Learning? It is, quite simply, the automated process of features and algorithm selection that supports planning. Business users can leverage machine learning and assisted predictive modeling to achieve the best fit and ensure that they use the most appropriate algorithm for the data they wish to analyze. Business users can take advantage of AutoML tools to explore patterns in data and receive suggestions to help them gain insight – all without dependence on IT or data scientists.

Now is time to start to review the basic concepts of neural networks. This post will present some basic concepts of neural networks, reducing theoretical concepts as much as possible, with the aim of offering the reader a global view of a specific case to facilitate the reading of the subsequent posts where different topics in the area will be dealt with in more detail. A brief intuitive explanation of how a single neuron works to fulfill its purpose of learning from the training dataset can be helpful for the reader. 

A human recruiter, likely to be juggling various tasks, can’t always give their full attention to every single potential candidate. Chatbots can be used to automate the top of funnel interactions, ensuring that each candidate gets timely, personalised responses at each stage of the recruitment process. Given the high volume of emails, calls, messages, pings, and alerts that recruiters have to try and stay on top of, chatbots make good personal assistants. They are nowhere near to replicating human capabilities beyond administrative responsiveness – but this in and of itself is already helping recruiters do better business.

Today, deep learning has become pivotal to many of the applications we use every day such as content recommendation systems, translation apps, digital assistants, chatbots and facial recognition systems. Deep learning has also helped create advances in many special domains such as healthcare, education, and self-driving cars. The fame of deep learning has also led to confusion and ambiguity over what it is and what it can do. Here’s a brief breakdown of what deep learning and neural networks are, what they can (and can’t do) and what are their strengths and limits.