Machine Learning in Guduvancheri
Learn Machine Learning in Guduvancheri at Prince Infotech!!!
Machine learning is a field of computer science that often uses statistical techniques to give computers the ability to “learn” with data, without being explicitly programmed.
Machine Learning Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter sub field focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
MACHINE LEARNING Syllabus
Introduction to programming
Concepts of Python programming. Configuration of development environment. Standard library functions. Variables and strings. Functions, control flows and loops. Structured data: list and for loops; how to fix the problem
Research methods and visualization of data. Concentration trends. Variability and standardization. Normal distribution and sampling distribution. Statistical tests: hypothesis test, T test, ANOVA, chi-square test. Regression and correlation
Data analysis process: Learn how to use data to answer questions. NumPy and Pandas operations for one-dimensional data. NumPy and Pandas operations for two-dimensional data. Data modelling: Understand the basic types of data and learn how to handle data sets