Machine Learning using R and Python

Explore how machine learning uses past data to train models and predict outcomes, contrasting it with traditional programming.

Explore introduction to R programming within machine learning and data science. Learn how R differs from traditional programming, its open-source tools, and essential packages for data analysis.

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.

Learn how to manage variables and core data structures in R, including vectors, lists, data frames, matrices, and factors, with practical tips on naming, printing, and inspection.

Explore vectors in R, including atomic vector types and single versus multi-element vectors, homogeneous data via dynamic typing, plus creation with c and sequence with seq for printing and subsetting.

Explore vector manipulation and sub-setting techniques in R and Python. Apply these skills to streamline machine learning workflows.

Explore how R handles constants, including built-in vectors like letters, month names, and pi, and use sequence and c functions to subset and manipulate vectors.

Install and set up RStudio, explore its four panes (code editor, console, environment/history, plots), and learn vectors and lists, including creating heterogeneous lists with the list() function.

Explore creating and manipulating lists in R, combining vectors, matrices, and other data types; learn accessing, naming, and subsetting list elements with indices, names, and the dollar operator.

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.

Learn to convert lists to vectors and back, access elements by position or name, and manipulate data; discover matrices and their homogeneous data requirements and basic creation.

Learn to create and manipulate matrices in R, convert lists to homogeneous vectors, set row and column names, and perform transpose operations for data reshaping.

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.

Explore matrix manipulation in R, including creating matrices, setting dimensions, recycling elements, and performing element access; learn the rep and repeat functions and work with data frames.

Explore creating and manipulating data frames in R, including strings as factors, accessing rows and columns with brackets and dollars, and expanding data frames.

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.

Merge and join data frames in R and Python using merge and by, with common columns, and perform inner, left, right, full, cross joins and row binding.

Learn to melt and cast data in R using the reshape package to reshape data frames and perform aggregations and casting.

Explore arrays in R: build multi-dimensional, homogeneous data structures from vectors, set dimension names for rows and columns, and use melt, cast, and apply across margins to manipulate data.

Explore how R handles categorical data with factors, including creating factors from vectors, default and custom levels, labels, and ordering, and working with data frames.

Learn to create and use functions in R, including built-in and user-defined functions, with arguments and defaults. Master control flow and loops, using if, else, switch, for, break, and next.

Master strings and string manipulation with the base package in R, covering string creation, print and cat usage, concatenation, valid versus invalid strings, and key functions like substr and format.

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 string manipulation in r using the stringi package, including splitting, boundaries, replacing, and translating strings, followed by handling date and time with system time, system date, and date formatting.

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.

Extract csv data in r by loading it into data frames with read.csv, manage headers and separators, set the working directory, and explore structure with head, tail, and subset operations.

Learn how to extract data from excel and other formats into R, using read and write functions, with subset and select options, and export results back to Excel or CSV.

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.

Explore the history and types of database management systems, learn the relational model and entity-relationship diagrams, and master SQL sublanguages and normalization for robust data design in machine learning workflows.

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 data manipulation language commands—select, insert, update, delete, and truncate—and how they govern data in road travel database tables 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.

Learn to extract data from relational databases with R, install and connect to MySQL, and run basic queries on a roadway travels database.

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.

Master advanced dplyr data manipulation in R, including selecting, renaming, filtering, and deduplicating data; use starts_with, ends_with, contains, matches, and range helpers to shape data for analysis.

Learn to manipulate air quality data with dplyr: select and drop columns, rename, filter, mutate, and summarize with group_by to compute means by month, all via the pipe operator.

Explore the plyr package for data analysis, learning to split data, apply operations like mutate and summarize, and recombine results with the baseball database, plus a comparison to dplyr.

Learn to tidy data in R using tidyr's gather and spread functions to convert between long and wide formats, and compare with reshape approaches while working on Lamont baseball database.

Explore factor analysis in machine learning with i using r and python, converting categorical data to factorial form and using cross-classified and proportional tables to interpret factor levels.

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 compute minimum, maximum, mean, median, range, and mode in R using base and stats packages; understand normal, left-skewed, right-skewed distributions and visualize with box plots and summary function.

Analyze the credit data set to assess normal distribution and skewness using mean and median; compute min, max, range, and quartiles, and apply box plots and iqr insights.

Explore data visualization in R and Python by creating pie charts, 3d pie charts, and bar charts, with options for labels, colors, legends, and high-level versus low-level plotting.

Learn to visualize data distribution with box plots in R and Python, interpreting minimum, maximum, median, and quartiles, and relate it to other charts like bar and pie charts.

Explore histograms and line graphs in R and Python, learn how to create bar charts, box plots, and multi-line plots with axes labels, colors, and breaks.

Learn how to create scatter plots and scatter plot matrices to visualize relationships between two variables and across multiple variables, assess correlations, and interpret distributions with box plots and outliers.

Learn to create high- and low-level plots in R, including box plots and scatter plots, tune colors and borders, and interpret distributions and outliers.

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.

Master combining plots in a single graph with par and layout in R, arranging multiple plots—bar, scatter, density, and box plots—in a 2x2 grid, with by-row or by-column control.

Learn to create, combine, and customize scatter plots, box plots, histograms, and pie charts using matrix layouts in R, and interpret boxplot whiskers and outliers.

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 how to customize box plot style parameters in R using colors, median lines, fills, and outliers, with Iris dataset examples.

Use set.seed to reproduce a fixed data order, then reshape data to a long format with melt, gather, or reshape, enabling consistent plotting with dg plot.

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

Explore chi-squared tests for independence, t tests, and ANOVA with practical cross-table and variance analyses, including p-values, correlation basics, and common data visuals in R.

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.

Examine data exploration and visualization, from iris data sampling to covariance and correlation, then compare supervised, unsupervised, and reinforcement learning with practical machine learning examples.

Learn how machine learning uses training data and labels to solve real-time problems, with supervised, unsupervised, and reinforcement approaches, including classification, regression, and dynamic pricing.

Learn the basics of k-nearest neighbor classification, including data preparation, training versus testing splits, Euclidean distance, and model evaluation to predict class labels.

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.

Explore Naive Bayes classification using a spam/ham dataset, building and evaluating models with train-test splits, cross table insights, and bayesian reasoning.

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.

Create a text mining workflow: build a corpus, clean text (lowercase, stop words, punctuation, numbers), and build a document-term matrix; visualize with a word cloud on spam vs ham data.

Train and evaluate a naive bayes model on real-time spam data, using corpus creation, document metrics, and 75/25 train-test splits, with Laplace smoothing and confusion-matrix evaluation.

Develop a text classification workflow in R: collect and prepare data, train a Naive Bayes model, evaluate with cross tables and confusion metrics, and report results using knitr markdown.

Explore how decision trees apply supervised learning to classify both categorical and numerical data, using entropy-based splits on credit data to train, evaluate, and improve default predictions.

Apply decision trees to credit data for classification. Convert the target to a categorical variable, build a C5.0 model in R with entropy, and evaluate using a confusion metric.

Explore building and evaluating decision trees for credit data using R and Python, including data prep, train-test splits, and boosting with C5.0 to improve accuracy.

Learn to apply support vector machines, neural networks, and random forests using R and Python, with iris data, hyperparameter tuning (cost, gamma, kernel), and evaluation via cross-validation and confusion matrices.

Explore regression concepts with linear regression fundamentals, including predictor and response variables, the equation y = mx + b, and how to train and predict with supervised learning.

Learn how multiple linear regression extends simple regression to model a single output with multiple predictor variables, using coefficients and intercept to make predictions.

Explore generalized linear regression, non linear regression, and logistic regression with R, covering linear and multiple regression foundations, glm usage, and practical examples like the body fat and spam datasets.

Explore clustering as an unsupervised learning approach that groups similar data using k-means, Euclidean distance, and centroid updates, illustrated with the iris dataset.

Apply unsupervised k-means clustering to high school social network data to uncover patterns in interests and demographics, while practicing data cleaning and handling missing values.

Learn to mine association rules in unsupervised learning (market basket analysis) using support, confidence, and lift, with the R arules package on Titanic data.

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.