Machine Learning using R and Python

Learn how to download and install R, set up the R environment, and manage packages—install, attach, and load libraries while understanding dependencies and using help and search features.

Explore variables, operators, and data types in R, including relational, logical, and mathematical operations, dynamic typing, and data type conversions, with practical examples from installation to evaluation.

Master constants in R by exploring the sequence function, vector indexing by position, logical and negative indices, and using c to subset and manipulate data for analytics.

Explore list manipulation in R, including sub-setting by position and name, accessing elements with dollar notation, and merging multiple lists to build complex data structures.

Master matrix accessing in R: create matrices with proper row and column names, access elements by row and column indices or names, and handle dimension and recycling rules.

Learn how to bind data frames by columns and rows using cbind and rbind, expand data frames by adding columns or rows, and manage strings and factors.

Explore how to bind and merge data frames in R, using cbind and rbind, and merge by common columns with by, by.x, by.y, including sorting.

Explore string manipulation in R using base functions and the stringi and stringr packages, including installing and loading them, splitting text into words, sentences, and characters, and replace operations.

Explore parsing, formatting, and arithmetic with date and time in R, including ISO formats, time zones, and POSIXct/posixlt objects using string and date-time functions.

Learn to extract data from clipboard, URLs, XML and JSON files, plus Excel and CSV sources, and convert them into data frames for analysis in R.

explain data versus information, define databases and dbms, overview six models (file, hierarchical, network, relational, object-relational, and object-oriented relational), and discuss relational rules and table relationships.

Learn data definition language (DDL) commands to define and modify databases and tables, including create, alter, rename, truncate, and drop, plus primary and foreign keys with cascade rules.

Explore subqueries and constraints in SQL, including inner and outer queries, any, exists, union and intersection, and primary, unique, not null, and foreign key constraints.

Explore aggregate functions such as count, sum, max, and min; learn about distinct counts, group by with having, and how simple and complex views operate on single-table and multi-table data.

Connect to relational databases with a database interface, read data into R data frames, and manipulate with the DPlyr package using queries, updates, inserts, and deletes.

Discover how to use the tiny package to combine and split columns, create data frames, and perform cross tables for categorical data, interpreting row, column, and total proportions.

Explores performing statistical observations in R using base and stats packages, covering min, max, mean, median, and quantiles, with NA handling and the summary function for descriptive statistics.

Learn to create bar plots and density plots in R, work with real-time data, and combine multiple plots with custom colors, legends, and axis options.

Learn to visualize data with MatPlot, ECDF, and box plots using the iris data set, including multi-series plots, density estimates, and distribution insights.

Explore data visualization with qplot and violin plots in R, and apply correlation analysis to assess linear relationships using real datasets like diamonds.

Explore data exploration and visualization in R and Python, using Iris dataset examples, box plots, scatter plots, 3D plots, heat maps, and multivariate visual techniques to uncover relationships among variables.

Explore k-nearest neighbor classification on the cancer dataset, using Euclidean distance, with steps for data collection, normalization, train/test split, and model evaluation in R including malignant and benign labels.

Explore knn classification on a cancer dataset, including data preprocessing, normalization, train-test split, and tuning k to improve accuracy with benign and malignant labels.

Apply naive bayes classification to an SMS spam dataset and explore text mining workflows, including data cleaning, corpus creation, and feature extraction using R and text mining tools.

Explore market basket analysis with association rules on groceries data; compute support, confidence, and lift to reveal frequent item patterns and actionable rules.

Explore Python libraries for data science, including pandas for data frames and series, statistics with statsmodels, visualization with matplotlib, and tools for supervised and unsupervised learning.