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Introducing Machine learning & Studying Statistics

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Submitted by pal7mentor
40 day(s) ago (Server time: 19.01.2021 11:29:22)

Introducing Machine learning & Studying Statistics

With Machine Learning (ML) courses gaining popularity in the computer science field, many young professionals and students choose to study the subject by enrolling in universities. But like many other courses, ML can also be learned sitting at home with just a laptop and some books. Anyone who has experience in coding and knows some programming languages can become an ML Engineer by studying various online courses and specializations. But even for such beginners, certain steps can be followed to master this subject.

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  1. Learn a new language: Python or R
  2. There are several languages that one can use for ML. The most commonly used is Python, and then R. various Python libraries are directly used in ML and concepts in both languages that will prove useful later. There are various online courses available to learn these languages.
  3. Studying Calculus
  4. Although many in this field have already learned calculus basics, it is necessary to dig deeper. Linear Algebra and Multivariate Calculus are essential for research and development in ML.

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  1. Studying Statistics
  2. Apart from these, there is a need to learn descriptive and inferential statistics. Concepts like probability distributions, statistical significance, hypothesis testing, regression, conditional probability, maximum likelihood, etc. should also be known. Resources for these can be easily found online.
  3. Data Preparation
  4. Along with this, it is essential to learn how to clean and prepare data like variable identification, missing data treatment, outlier treatment, etc. which will enhance the quality of the work one will do in the future.
  5. Introduction to Machine Learning
  6. After these four steps, one is ready to be introduced to ML's basics and its concepts. There are many free or paid ML courses, and specializations offered online by top universities taught by data scientists and researchers. One will learn various ML terminologies like a model, prediction, feature, training, target, etc. Different types of ML-like supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Through these courses, one will learn data collection, integration, pre-processing in ML, various ML algorithms and models, and work on real-world problems and understand how to interpret the results.

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  1. Advanced Machine Learning
  2. Once the basics of ML are understood well, the next step is to explore advanced ML to understand each aspect of this field. Based on artificial neural networks with representation learning, deep learning helps one turn predictions into actionable results using knowledge-based predictions and pattern discovery. Many online courses can be accessed freely on this subject. One should also learn ensemble modeling that can add a lot of power to their models. They should also be able to effectively apply these learnings to big datasets and be comfortable enough to create practical solutions that can be applied with real-time data. To become an expert, one should also be able to use ML algorithms like spam detection, fraud detection, recommendation system, etc. on big data.
  3. Participate in Competitions
  4. Lastly, after learning all the concepts and algorithms in ML and advanced ML, one can test their abilities by participating in competitions. This will help in becoming more proficient in ML by applying theoretical knowledge to practical implementation. There are many competitions taking place online that test skills and experience in both ML and Python.
  5. After participating and winning competitions and challenges, one can easily be called a Machine Learning Engineer. But they should keep practicing their abilities by creating bigger and more difficult ML projects and keep learning new concepts since this field is ever-growing and making inventions daily

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