Unsupervised learning is a branch of machine learning where the models learn patterns from the available data rather than provided with the actual label. We let the algorithm come up with the answers. In unsupervised learning, there are two main techniques; clustering and dimensionality reduction. The clustering technique uses an algorithm to learn the pattern to segment the data. In contrast, the dimensionality reduction technique tries to reduce the number of features by keeping the actual information intact as much as possible.

If you want to be a data scientist in 2023, there are several new skills you should add to your roster, as well as the slew of existing skills you should have already mastered. Part of the problem is job scope creep. Nobody knows what a data scientist is, or what one should do, least of all your future employer. So anything that has data gets stuck in the data science category for you to deal with. You're expected to know how to clean, transform, statistically analyze, visualize, communicate, and predict data.

This one is a no-brainer. We've had ChatGPT, Google Bard and god knows what else has come out of the woodwork in the past month. So what is Natural Language Processing (NLP) and why did I mention ChatGPT and Google Bard? NLP is the process of helping computers understand text data. Learning a language is already difficult for us humans, so you can imagine how difficult it is to teach a computer to understand text data.

There are all sorts of options for learning modern natural language processing, notably those options with neural networks techniques. For example, there is freely-available course material from world class universities such as Stanford's Natural Language Processing with Deep Learning, among others. There are also courses, paid and otherwise, from independent non-university sources such as Coursera and fast.ai. There is a wide variety of quality books that have been published over the recent few years which are topical and up-to-date. Today, if you want to learn modern natural language processing techniques, there is no excuse for not doing so.

Machine learning and artificial intelligence seem to be growing at a rapid rate that some of us can even keep up with. As these machine-learning models get better at what they do, they will require better infrastructure and hardware support to keep them going. The advancement of machine learning has a direct lead to scaling computing performance. TPU stands for Tensor Processing Unit and they were designed for machine learning and deep learning applications. TPU was invented by Google and was constructed in a way that it has the ability to be able to handle the high computational needs of machine learning and artificial intelligence. When Google designed the TPU, they created it as a domain-specific architecture, which means they designed it as a matrix processor, instead of it being a general-purpose processor so that it specializes in neural network workloads.

If you just think about the last 5 years alone, how your conversations have changed between your family and friends. Some of you may not speak about technology at all, but we can admit that it's hard not to consider it is around us. The recent release of ChatGPT and now GoogleBard are taking the world by storm with their amazing capabilities. You start to look at these tools and figure out how they can improve your work life, the company's process, your personal life, and more. Artificial Intelligence is automating tasks that were once only capable of being done by humans.

Over the years there has been exponential growth in data science applications, fueled by data collected from a wide variety of sources. In the last 10 years alone we have seen the implementation of data science, machine learning and deep learning. Although we hear a lot more about machine learning and deep learning, it is the core data science technique that a lot of companies focus on as this is where they make money and save a lot of money. However, studies show that 68% of data studies go unused and 90% of data is left unstructured. This is due to companies failing to focus on the data analytical processing phase, as it can take a lot of time, money and resources.

ETL and ELT are data integration pipelines that transfer data from multiple sources to a single centralized source and perform some transformation and processing steps to it. The difference between these two is ETL transforms the data before loading, and ELT transforms the data after loading. But before diving deeply into them, let's first understand the meaning of E, L, and T. T for Transform - Transforming the data is a process of cleaning and modifying the data in a format so that it can be used for business analysis. L for Loading - It involves loading data to a target system, which may be a data warehouse or a database. ETL is the first standardized data integration method that emerged in the 1970s due to the evolution of disk storage.

Most notably, text-to-image models (AI art) became extremely popular. Search engines were swapped for sophisticated chatbots such as ChatGPT. With open-source alternatives such as PaLM RLHF on the horizon, AI and machine learning will become more accessible to neophyte developers. However, becoming a true machine learning engineer requires more skill than just scripting or coding. As such, more people are beginning to consider it as a potential career path.

Note: The Coursera courses mentioned in the blog can be audited for free, meaning that you have access to all the course content without any cost. Programming is an essential part of your data science journey. If you know how to code in R, Python, or Julia, it will be quite easy for you to translate algorithms into functions. Moreover, you will learn better techniques to create a program or data reports. I will highly recommend you start with Python and learn the basic syntax and advanced functionalities.