Data Scientist, Machine Learning Expert, Algorithm Engineer, Deep Learning Researcher -- whatever your title might be, if using advanced concepts of Machine Learning is part of your career, then keeping up to date with the latest innovations is also a part of your everyday tasks. But in order to be on-top of all the latest ingenuities and truly understand how they work, we must also be familiar with the building blocks and foundations they rely on. The field of Deep Learning is moving fast, breaking and setting new records in each and every possible metric exists. And as it evolves, it creates new fundamental concepts, allowing new architectures and concepts never seen before. While I tend to assume all modern ML-practitioners are familiar with the basics fundamentals, such as CNN, RNN, LSTM and GAN, some of the newer ones are occasionally missed or left out.

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. These days, you find thousands of job openings for the position of a Data Scientist. The AI and machine learning has surely taken this world by storm. Though AI was invented several decades ago, we started seeing its practical usefulness in just last few years. With applications as simple as predicting house prices to sophisticated ones like person detections in real time videos for surveillance, real time traffic monitoring and those based on textual data like ratings the hotels on the basis of their past customer reviews to areas like topic modeling, real time language translations and so on.

A large ecommerce company has allocated a hefty budget for marketing activities before its upcoming sale event--its biggest in the year. The marketing team needs to come up with the messaging to be delivered during the marketing campaign to attract maximum customers. The big question is what should that messaging be? Should it be, "Get the best discounts," or should it be, "Get the best international brands," or will "Get best quality at best rates" work better? Should all three messages be employed?

Venture Capitalists are hoping to find the next superstar tech unicorn, AI startup founders dreaming of creating the next unicorn, and corporates adopting AI need to consider their data growth strategy in order to be able to scale their AI-enabled services or products. The past decade has been one of explosive growth in digital data and AI capabilities across the digital media and e-commerce space. And it is no accident that the strongest AI capabilities reside in the Tech majors. The author argues that there will be no AI winter in the 2020s as there was in 1974 and 1987 as the internet (social media and e-commerce) are so dependent upon AI capabilities and so too with being the Metaverse, and the era of 5G enabled Edge Computing with the Internet of Things (IoT). Furthermore, the following infographics illustrate how many people globally use social media and hence how central these channels have become to the everyday lives of people. Likewise, the size of the e-commerce market is vast. Although the era of standalone 5G networks may enable a window of opportunity for a new wave of consumer-facing applications in the business to consumer (B2C) in relation to e-commerce and perhaps even new digital media platforms that may challenge the current incumbents, after all the arrival of 4G provided a window for the likes of Airbnb, Uber, and leading social media platforms such as Facebook, Instagram, etc. to scale.

Venture Capitalists are hoping to find the next superstar tech unicorn, AI startup founders dreaming of creating the next unicorn, and corporates adopting AI need to consider their data growth strategy in order to be able to scale their AI-enabled services or products. The past decade has been one of explosive growth in digital data and AI capabilities across the digital media and e-commerce space. And it is no accident that the strongest AI capabilities reside in the Tech majors. The author argues that there will be no AI winter in the 2020s as there was in 1974 and 1987 as the internet (social media and e-commerce) are so dependent upon AI capabilities and so too with being the Metaverse, and the era of 5G enabled Edge Computing with the Internet of Things (IoT). Furthermore, the following infographics illustrate how many people globally use social media and hence how central these channels have become to the everyday lives of people. Likewise, the size of the e-commerce market is vast. Although the era of standalone 5G networks may enable a window of opportunity for a new wave of consumer-facing applications in the business to consumer (B2C) in relation to e-commerce and perhaps even new digital media platforms that may challenge the current incumbents, after all the arrival of 4G provided a window for the likes of Airbnb, Uber, and leading social media platforms such as Facebook, Instagram, etc. to scale.

If you think you would like to become a DATA SCIENTIST, then you're at the perfect place to get all the skills that actual data scientists hold. In layman's terms, a rocket scientist is a person who has knowledge of (and amazing experience in) rocket science. Becoming a data scientist is not that difficult. Becoming a jet pilot isn't rocket science, but it still requires lots of effort to become a jet pilot.) After talking with many data scientists on LinkedIn, I am writing this blog as a collection of more than 30 years of experience from someone else's life.

Artificial Intelligence (AI) and Machine Learning are projected to become mainstream technologies in the coming years, and are clearly already having a significant impact across many industries. How exactly is this happening? How are Data Scientists using their skills to develop better Machine Learning algorithms? Where are these innovative technologies going in the future? With the rise in the implementation and usage of once revolutionary technologies/trends like Big Data, the Internet of Things (IoT), or the Cloud, Machine Learning (ML) and now Deep Learning (DL) are gradually moving into mainstream business corridors.