Machine Learning using Python and R

Data can be stored in various sources. Traditionally organizations store data in RDBMS databases. In this module, you will learn how to extract tables from traditional database MySQL using Python and R. You also will learn about the various functions and packages involved in Data Extraction.

Understand the holistic view of Machine Learning concepts. Learn about the Project Management of Machine Learning projects and various concepts for performing Descriptive Analytics, like EDA, Imputation, Visualizations, etc. As part of this module, participants will learn about the basics of Python and R. They will also learn the basic concepts of scripting Machine Learning algorithms.

Learn about inferential statistics and understand sampling. Learn about various sampling techniques as part of this module. In this module, you will also learn about Balanced Datasets. In the real world, data sets are largely imbalanced. Learn to deal with Imbalanced Datasets using advanced Data Analytics techniques.

Descriptive analytics will explain the historical events that have occurred. Learn to perform statistical calculations on data, to find patterns and anomalies, to derive insights for the business. Understand the various functions and packages used to perform statistical computing using Python and R. Learn how Data visualizations can help tell stories using plots. In this module, you will also learn about various visualization techniques for Uni-variate and Bi-variate Data Analysis.

Machine Learning is used for performing predictions on data. To develop an appropriate model, discerning underlying patterns in the data is of paramount importance. As part of descriptive analytics, you will learn about various data manipulation techniques to preprocess the data. These techniques will help you identify patterns in the data. Learn about Imputation techniques, Feature Engineering, Data Wrangling, Data Modification techniques like transformations and normalization using Python and R.

Machine learning is a process of training the machine to understand a pattern and define a mathematical equation that explains the relationship between dependent and independent variables. Python and R are functional oriented programming languages that use packages or libraries that provide functions to perform training on the business data. In this module, you will learn about various functions and libraries which are used to perform Descriptive and Predictive Analytics.

Data Mining is a process of analyzing data using mathematical concepts to uncover hidden facts from data. Use Python and R Programming to develop a wide variety of Machine Learning algorithms. Understand the implementation and evaluation of ML techniques by understanding measures of Error (RMSE). Learn about problems like Heteroscedasticity, Overfitting and Underfitting, Imbalance in the data and solving them using Python and R functions.

Learn about the interpretable techniques to perform predictive analytics. Understand how to perform Regression analysis on numeric dependent variables. Learn to decode the relation between dependent and independent variables using correlation analysis and scatter diagram. Understand why Causation cannot be estimated using Correlation. Learn about the packages used in Python and R for training Regression models. Learn about model accuracy improvement techniques. In this module, you will learn about the Confusion matrix and conditions of confusion matrix which are used to evaluate the binary classification logistic regression model.

The most commonly used rules-based algorithms, Decision tree, and Random forest are used for classifying dependent variables. In this tutorial, learn about the nodes used to create rules in Decision trees. Understand the information calculation using entropy to create pure classes in Decision tree analysis. Learn about pruning and its necessity. Understand the ensemble techniques for Decision Tree examples and learn about the Random forest algorithm.