How can I begin to learn more about machine learning?
Machine Learning
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Machine learning is one of the most exciting and rapidly evolving domains of computer science. However, machine learning can also be daunting, as it can require a combination of mathematical, computational and domain-specific skills. How can someone just starting to learn about machine learning navigate this complex and diverse field, and build a solid foundation for further practice? Here are some of the areas that machine learning beginners should focus on.
1. Become familiar with concepts and terminology: Before diving into the details of any specific machine learning subfield, it is important to have a general understanding of what it is. This can include reading more about the common components of a machine learning project, the key metrics for evaluating machine learning models or the biggest limitations of machine learning.?
2. Learn the essential mathematical and statistical foundations: Machine learning is built upon various mathematical and statistical concepts and tools, such as linear algebra, statistics and probability. These concepts are key to understanding how algorithmic models work, how to design them and how to analyze their results. Therefore, it can pay off to have a solid background in these areas first before diving into any machine learning project.?
3. Develop the practical skills for programming and implementation: Machine learning is not only a theoretical field, but also a practical one. This means that to use machine learning, you will benefit from being able to code, implement and deploy models using various programming languages and frameworks. The most popular and widely used programming language for machine learning is Python. Learning Python can help you perform various machine learning tasks, from data manipulation to feature engineering. Online courses can help you begin to learn Python and other programming languages.?
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4. Explore the applications and domains for machine learning: Machine learning can be used to solve a number of real-world problems across industries like healthcare and education. Learning about the applications of machine learning can help you gain insights into the relevance, impact and potential of the technology, as well as the specific challenges and opportunities in each domain. You can begin to explore these applications by reading case studies, articles or blogs on how machine learning is used and implemented in different contexts.?
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This article was edited by LinkedIn News Editor Felicia Hou and was curated leveraging the help of AI technology.
Assistant Professor at SIU-C
1 年Step-1: concept of Data and Dataset in excel; Step-2: statistics, visualization, and SQL in R, Matlab, or Python; Step-3: you have already passed half of machine learning journey!
Biomedical Engineering Faculty and Consultant, Senior IEEE Member
1 年Matlab tutorials are a very good start, it starts from the very beginning and applies simple models on real world projects. Andrew NG courses and videos are very easy to understand. A beginner should focus on the very big picture of the process (data preparation, model training, and assessment). Should start with just how things work, and then should dive deeper on how to optimize and tune the model.
Data Science Leader @Intel Foundry |Ex GE & Cisco | AI Advisor | Supply Chain innovation Award for AI Solution CSCMP Edge | ML Book Author| Sr. Member IEEE | NeurIPS/AISTATS Reviewer
1 年Building an intuitive understanding of the popular ML algorithms would definitely help to choose what models to use in what use cases. Ability to effectively code in Python is important as most of the ML libraries and apis are pythonic. For a citizen data scientist ( such an business SME but little background in Computer science or math),low code to no code platforms can come in very handy to apply machine learning to solve business problems
Senior Data Scientist, Global Strategy, Sales & Marketing Advanced Analytics at Cisco
1 年Understanding inferential and descriptive statistics , basic data type knowledge and data manipulation operations (through pandas / sql) like aggregation functions is generally a very basic step before learning machine learning