How to learn machine learning
— What is your background, what did you do before machine learning? How
— I run Sethi's service businesstechnologies. We provide our clients with solutions based on machine learning and artificial intelligence. Over the past two years, we have worked with some of the largest Fortune 500 companies.
I've always been fascinated by data.This determined my choice - after that I began to look for knowledge, skills and experience in the field of machine learning through project-based learning. This gave me the opportunity to become a machine learning expert in the Education Ecosystem, a decentralized learning ecosystem that teaches professionals and college students to build real products.
“If data, automation and algorithms are of interest, then machine learning is a profitable career choice”
How do people start learning machine learning? Is this not an area where fundamental knowledge and many years of education are needed?
— Fundamental knowledge in the fieldprogramming are an added advantage, otherwise the learning curve will be too steep. Machine learning is also the main component of the most rapidly developing areas - Big Data, Predictive Analytics, Data Mining and Computational Statistics.
If data, automation and algorithms callinterest, then machine learning is a profitable career choice. Taking a structured program or course is one of the best ways to learn machine learning from scratch. The high demand in this industry has resulted in hundreds of face-to-face and online courses.
— What can you advise developers and analysts who want to develop in this area?
– Machine learning has the potential to makeapplications more powerful and more responsive to user needs. Developers who want to implement machine learning into applications need to know a few key things that will help them succeed:
- The more data an algorithm has, the more accurate it becomes, so avoid subsampling whenever possible.
- Choosing the best machine learning method for a problem is key and often determines success or failure.
- Machine learning models can only be good when the data is good.
- Understanding data features and improving them (by creating new ones and removing existing ones) has a big impact on predictability.
- Where can you learn it? Maybe in courses or schools?
— Fortunately, today there are many platformsonline learning such as the Education Ecosystem where you can learn different concepts of machine learning and artificial intelligence. At the Education Ecosystem, you can learn from expert developers through projects that include tutorials and project resources. For example, I created several projects like this:
- Image Retrieval by Similarity using Tensorflow and Keras
- Neural Style Transfer Using Keras And Tensorflow
- How To Do Face Detection Using OpenCV Haar Cascades
Which business needs and which does not need AI
— How do you “sell” AI and machine learning to companies and how do they improve their work? Why do you think business has become more scientific?
— Machine learning algorithms can repeatedlylearn based on the provided data set, comprehend patterns, behavior. This process is iterative and constantly improving, which helps companies to constantly change to meet the needs of business and customers.
"Machine learning algorithms can learn iteratively from a given set of data"
What companies will and won't it suit? What problems can be solved with their help?
— Most of all, machine learning is needed by business,which deals with image classification, text parsing or predictive modeling. For other types of business, algorithms can be trained to recommend something to the user, collect data, use deep learning and neural networks. In the service industry, algorithms can be trained like a help desk manager through natural language processing based on common customer complaints.
— In this area, something new appears almost every day. How to keep track of what is happening, what to pay special attention to?
— A recent Indeed report found that job openingsMachine learning engineers are ahead of everyone else in salary, demand and growth. The document also noted that demand for machine learning engineers increased by 344%.
This area is so important because itallows businesses to see trends in customer behavior and business operating patterns, promotes the development of new products. Many of the leading companies such as Facebook, Google and Uber are making machine learning a central part of their operations. Continuous professional development will help professionals take advantage of high demand and low supply in this industry.
— Machine learning is often used in big data analysis. What breakthrough products will appear here?
Big data has become important as manyorganizations, both public and private, collect vast amounts of information in specific areas. Merging machine learning and big data is a never-ending process. We will see how machine learning algorithms are applied to every element of working with big data, including segmentation, data analytics, and modeling.
— What free market niches are associated with the development of machine learning and AI?
- Artificial intelligence is a breakthroughrecent technology. There are many niche areas where AI is making a significant impact. There are other niche applications that are not covered in the media, but they are in scientific publications. In the coming years, they will receive the greatest development, these are education, construction and planning, entertainment and sports analytics.
— How do you see the development of machine learning? How can it help people, businesses, states?
— Machine learning helps businessesuse preventive maintenance to reduce equipment breakdowns and increase profits. As the demand for large and complex data processing capabilities grows, machine learning will help businesses use consumer data to build useful customer profiles, increase sales, and build brand loyalty.
Machine learning is just beginning to develop. All the most interesting things are ahead
What are the biggest misconceptions about big data and machine learning?
- The biggest misconception isthat machine learning models can solve all the problems of this world. One of the most famous quotes about machine learning comes from Dave Waters: “A baby learns to crawl, walk, and then run. In the field of machine learning, we are in the crawl phase.”
In the process of machine learning there will always beperson involved. But there is a caveat here. With improved algorithms, we will be able to eliminate human involvement completely after training a specific machine learning model.
- Not everyone keeps up with breakthroughs in this area - what should we pay attention to?
— Latest developments in the field of machinelearning today is Automated Machine Learning (AutoML), Machine Learning Operationalization Management (MLOps), No-Code Machine Learning and Low-Code Machine Learning Development. These are concepts that will lead to very promising projects in the coming years.
— What are the short and long term problems of ML? What about developer bias, bad intentions, and ethical standards that can't be written down and formalized?
— The biggest challenges in machine learning —it is a lack of qualified resources, a lack of quality data, and an understanding of what processes need to be automated. Until we have clean and reliable data, machine learning professionals will continue to face challenges in developing algorithms and systems that meet the exact needs for which they were created.
- When and in what area will artificial intelligence show itself in the most interesting way?
— Artificial intelligence is shaping the futurehumanity in almost all sectors. It is already a major driver of emerging technologies such as big data, robotics and IoT, and will continue to be a technology innovator for the foreseeable future. Today, it is difficult to pick one specific area, given that all industries today work with large amounts of data and have different automation needs.
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