Introduction to Machine Learning
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The document introduces machine learning, defining it as a science that involves programming systems to learn from data and improve over time. It discusses machine learning algorithms, highlighting the significance of choosing the right algorithm based on factors like data size and objective, and distinguishes between supervised and unsupervised learning techniques. Additionally, it covers programming languages commonly used in machine learning, such as R and Python, and provides resources for further exploration.
Introduction to Machine Learning
- 1. Eng Teong Cheah MVP Visual Studio & Development Technologies Introduction to Machine Learning
- 2. Agenda What is machine learning Machine learning algorithms Machine learning languages
- 4. What is machine learning Machine learning is a branch of science that deals with programming the systems in such a way that they automatically learn and improve with experience. Here, learning means recognizing and understanding the input data and making wise decisions based on the supplied data.
- 5. What is machine learning It is very difficult to cater to all the decisions based on all possible inputs. To tackle this problem, algorithms are developed. These algorithms build knowledge from specific data and past experience with the principles of statistics, probability theory, logic, combinatorial optimization, search, reinforcement learning, and control theory.
- 7. Machine learning algorithms What machine learning algorithm should I use? It depends. It depends on the size, quality, and nature of data. It depends on what you want do to with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. It depends on how much time you have.
- 8. Flavors of machine learning There are several ways to implement machine learning techniques, however the most commonly used ones are supervised and unsupervised learning.
- 9. Supervised Learning Supervised learning deals with learning a function from available training data. A supervised learning algorithm analyzes the training data and produces an inferred function, which can use for mapping new examples.
- 10. Unsupervised Learning Unsupervised learning makes sense of unlabeled data without having any predefined dataset for its training. Unsupervised learning is an extremely powerful tool for analyzing available data and look for patterns and trends. It is most commonly used for clustering similar input into logical groups.
- 12. R in machine learning R is a workhorse for statistical analysis and by extension machine learning. It is the platform to use to understand and explore your data using statistical methods and graphs. It has an enormous number of machine learning algorithms, and advanced implementations too written by the developers of the algorithm
- 13. Python in machine learning Python if a popular scientific language and a rising star for machine learning. I’d be surprised if it can take the data analysis mantle from R, but matrix handling in NumPy may challenge MATLAB and communication tools like IPython are very attractive and a step into the future of reproducibility.
- 14. Demo Quantile Regression: Car price prediction
- 15. Resources TutorialsPoint Microsoft Docs Lecture Collection | Convolutional Neural Networks for Visual Recognition(Spring 2017) Python Numpy Tutorial Image Credits: @ashleymcnamara
- 16. Thank you Eng Teong Cheah Microsoft MVP Visual Studio & Development Technologies Twitter: @walkercet Github: https://github.com/ceteongvanness Blog: https://ceteongvanness.wordpress.com/ Youtube: http://bit.ly/etyoutubechannel