_Disclaimer: This is an opinion piece. I’d love to hear your thoughts and counter arguments in the comments._There’s a lot of doom and gloom in the field.Hiring is frozen.
…some hypothesize that investors will lose hope in AI altogether. Google has freezed hiring for ML researchers. Uber laid off the research half of their AI team…there will be far more people with ML skills than ML jobs.
_- _Chip Huyen
**We have a recession.People are talking about an AI Winter.**It makes sense that artificial intelligence (AI), machine learning (ML) and data science (DS) are the first to go in a crunch. They’re luxuries for most businesses.But that doesn’t mean the future isn’t bright.If you create value.
An AI winter is a period of decreased funding and interest in AI research.But most of us don’t do research. We read papers, get ideas and innovate… but we use existing techniques.Additionally, the popularity of building ML-powered products doesn’t necessarily correlate with the volume of research coming out.If anything, there’s an increasing amount of research that’s not being applied. Anecdotally, industry is still catching up in its implementation of machine learning invented decades ago.“AI-powered” products are more popular now because ML is more approachable, not because of new research.
The opposite is true.Classic algorithms + domain knowledge + niche datasets are going to solve most real problems, not deep neural nets. Most of us aren’t working on self-driving cars.I wrote about this in “Democratizing AI is irrelevant, Data is siloed, And how to build an AI company anyway_”._In my opinion, focusing on extreme technical competency is overrated outside large tech companies, in contrast to a problem-solving mentality and general development skills.Outside of tech, there’s still a tonne of boring/manual work that should have been automated a long time ago. And it doesn’t require breakthroughs.
When you solve a problem (any problem), everyone wins.Silicon Valley has deluded us to believe we should be taking moonshots rather than improving our local communities and the lives of people we know.I love Uber, and it has changed the world. But if keeping Uber alive costs $5 billion per quarter, maybe something is wrong.Yes, some companies are long term plays and will affect 7 billion people. But simpler improvements like reducing data entry mistakes in a “boring” industry also creates value.
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Big data skills are crucial to land up data engineering job roles. From designing, creating, building, and maintaining data pipelines to collating raw data from various sources and ensuring performance optimization, data engineering professionals carry a plethora of tasks. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig, and to name a few.
Here, we list some of the important skills that one should possess to build a successful career in big data.
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These are the 10 highest paying jobs you can learn without needing a college degree. Jobs that pay $75,000 and higher.
📺 The video in this post was made by Andrei Jikh
The origin of the article: https://www.youtube.com/watch?v=QIGDA2JRz8w
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.
It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.
To help you understand this topic I will give answers to some relevant questions about machine learning.
But before we answer these questions, it is important to first know about the history of machine learning.
You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”.
In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the Perceptron Model.
In 1959, Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines.
In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition.
Gerald DeJonge in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information.
During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyse large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning.
Now that we know about the origin and history of ml, let us start by answering a simple question - What is Machine Learning?
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You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
Before we dive into the machine learning world, you should take a step back and think, what is stopping you from getting started? If you think about it, most of the time, we presuppose things about ourselves and assume that to be true without question.
The most normal presumption that we make about ourselves is that we need to have prior knowledge before getting started. Get a degree, complete a course, or have a good understanding of a particular subject.
The truth is that most of the time, this is a lie, the prior knowledge you think you need is most of the time not required or is so big that even experts from the field don’t fully understand it. The Seek of this prior knowledge is a trap that will make you run in circles, which leads us to the next presumption.
The perfect condition, you can’t wait for the ideal environment or situation to get started, things will never be 100% ready, try and fail, then try again. It takes a lot of time to get good at machine learning; you won’t learn all at once and especially at the beginning.
Instead of trying to acknowledge everything before getting started, do a little bit every day; you can make significant progress by creating small things every day for a considerable amount of time. The perfect condition will never exist, do it in your path, be consistent with it, and the results will come.
After you start making little progress every day, you probably will end up having a struggle with something or failing to achieve your goal at a certain point. This feeling is tough; it’s hard to see yourself not making any progress, not having any sense of gratification, and then still not give up.
Machine learning is hard, it might take you a few weeks, months or even years to see progress in a certain point but isn’t any harder than any other technical skill, it requires repetition and dedication to get where you want, you need to test it, make a mistake and learn from i
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