The Connection Between Machine Learning and Statistics

In terms of methodology, machine learning and statistics are very similar, yet their main goals are different: Machine learning finds generalizable predictive models, while statistics generate inferences from a sample for analysis or interpretation. In statistics, machine learning concepts, from methodological principles to theoretical tools, have a lengthy history. Furthermore, the word "data science" is a catch-all term for the entire field. Separate the statistical modeling paradigm: the combination of data models and machine learning algorithms has resulted in a single field of statistical learning that machine learning methods have accepted.

Statistics and Machine Learning give tools for describing, analyzing, and modeling data. For descriptive data analysis, you can use descriptive statistics, visualization and clustering; you can also fit probability distributions to data, generate random numbers for Monte Carlo simulations, and test hypotheses. Using the Classification and Regression Learner programs, you may interactively infer from data and develop predictive models using regression and classification algorithms.

Principal Component Analysis (PCA), regularization, dimensionality reduction, and feature selection methods are available in the toolbox for multidimensional data analysis and feature extraction, allowing you to define variables with the highest predictive value.

The toolbox provides supervised, semi-supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), augmented decision trees, k-means, and other clustering methods. You can apply interpretability techniques such as partial dependency plots and LIME and automatically generate C/C++ code for embedded distribution. Many toolbox algorithms can be used on datasets that are too large to be stored in memory.

Machine Learning and Math

To acquire useful results, you'll need good mathematical intuitions regarding some broad machine learning concepts and the inner workings of individual algorithms. With a solid mathematical foundation;

·???????Correct algorithms are selected for the problem.

·???????Good choices are made about parameter settings, validation strategies.

·???????Over- or under-compliance is recognized.

·???????Insufficient or unclear results are eliminated.

·???????Appropriate confidence or uncertainty limits are set for the results.

·???????A better job is done on coding algorithms or they are made more complex.

In machine learning, there are three processes of developing hypotheses or models:

?????????Model creation

?????????Model testing

?????????Applying the model

Different approaches in Machine Learning:

?????????Concept and Classification Learning

?????????Symbolic vs Statistical Learning

?????????Inductive Vs Analytical Learning

The two techniques of Machine Learning are:

?????????Genetic Programming

?????????Inductive Learning

===========================================================

Please find also Semantic Layer Summit 2023 Detail for registration.

The 2nd annual Semantic Layer Summit is here! Registration is now open for this FREE one-day virtual event feat. 30+ Enterprise Data Leaders & Technologists. Register here:?https://bit.ly/3WPdy2B

?This one-day virtual thought leadership event is being organized to share advice and best practices on how to deliver actionable insights for everyone in your organization from top data leaders, technologists, & industry experts.

Featuring 30+ Enterprise Data Leaders & Top Industry Technologists