Prior to Machine Learning

1. PRIOR TO MACHINE LEARNING 01 Powered by CETPA INFOTECH

2. Machine learning refers to the process of enabling computer systems to learn with data using statistical techniques without being explicitly programmed. It is the process of active engagement with algorithms in order to enable them to learn from and make predictions on data. Machine learning is closely associated with computational mathematical optimization, and data learning. It is associated with predictive analysis, which allows producing reliable and fast results by learning from historical trends. statistics, 02

3. Things you should know prior to ML Linear algebra Calculus Probability theory Programming Optimization theory 03

4. Linear Algebra Although  linear algebra  is integral to the field of machine learning, the tight relationship is often left unexplained or explained using abstract concepts such as specific  matrix  operations.The use of  linear algebra structures when working with data, such as tabular datasets and images. 04 vector spaces or

5. CALCULUS All the trig you'll ever used in Machine Learning will likely be covered in good calculus class ,which should include analytical geometry.Calculus is a branch of mathematics which helps us understand changes between values that are related by a function. 05

6. PROBABILITY Probability theory is at the foundation of many machine learning algorithms. Machine Learning is an interdisciplinary field that uses statistics, probability, algorithms to learn from data and provide insights which can be used to build intelligent applications. 06

7. PROGRAMMING Machine implemented in  code. Programmers like implementing algorithms really understand how an algorithm works. This can also be required to get the most from an algorithm as is tailored for  a  given problem. learning  algorithms are 07 themselves to

8. OPTIMIZATION THEORY The interplay between   optimization  and   machine learning   is one of the most important developments in modern computational science.It also devotes attention to newer themes such as regularized robust  optimization, subgradient methods, splitting techniques, and second-order methods. 08 optimization, gradient and

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