AI, Basic Statistics, Basic Python, Basic R, ML (Overview)

Define artificial intelligence as the study of making computers perform tasks humans excel at, covering machine learning, deep learning, natural language processing, and analytics.

Trace the history of artificial intelligence from its 1950s origins to milestones like the Turing test, chess programs, the traveling salesman problem, and Deep Blue's victory over Kasparov.

Explore the original seven aspects of AI defined in 1955, examining how brain and body, abstraction, creativity, forecasting, and self-improvement shape a functional system.

Define intelligence through multiple perspectives, showing how it generalizes to unseen situations, enables reasoning and problem solving, and relies on perception and language understanding.

Explore the types of AI, including strong AI and weak AI, and learn how researchers define and compare machine intelligence.

Explore maturity level in AI by analyzing data to understand customer behavior, demand, and supply chain dynamics, highlighting self-learning systems that augment human decision making.

Identify the core problem in teaching machines and outline potential solutions. Examine how different groups, customers, consultants, and algorithms shape the challenges and responses in machine teaching.

Explore the wide range of ai applications—from analytics and behavior analysis to robotics, driverless cars, and natural language processing—and learn to select key use cases to study.

Explore how AI analyzes calls, usage patterns, and customer profiles to enable cross-sell, upsell, churn protection, fraud detection, and tailored product recommendations in telecom.

Ai in finance delivers portfolio management and asset allocation insights, valuing equities, private equity, and bonds, while highlighting anomaly detection and cyber-security risks.

Explore image analytics with ai, including automatic colorization of black-and-white photos, object detection with yolo, and applications in driverless cars.

Explore how ai in medical science analyzes scan images to assess disease presence and severity, aiding doctors with diagnosis and treatment decisions while addressing privacy and data storage challenges.

Learn how AI in IT operations management enables proactive outage prevention and data-driven planning in the cloud. It also covers bring-your-own-device integration and automated fraud detection to secure enterprise infrastructure.

Explore how artificial intelligence shapes news, from prediction and coverage to privacy and workforce impact, with a look at Google's capsule network and prescriptive analytics trends.

Explore open source technologies that underpin AI and data science, highlighting free tools used by industry and their impact on big data, deep learning, and analytics.

Explore cognitive computing, its definitions, and how it aims to simulate human thought through unstructured data, natural language processing, and self-learning within automated systems.

Explore the CRISP-DM cross-industry data mining framework guiding analytics projects from business understanding and data understanding to data integration, training and test data, modeling, evaluation, and deployment.

Explore what the next generation of AI may look like over the next five years, from descriptive and predictive analytics to real-time, self-learning automation and robots.

Explore next generation ai applications and how chatbots and real-time analytics transform customer engagement and marketing across banking, telecom, and other industries.

Examine how the internet of things enables devices—from your smart home cameras to fridges and cars, and factory machines—to talk to each other, enabling coordinated operation and IoT analytics.

Explore how artificial intelligence and human intelligence can coexist and complement each other, with insights on creativity, collaboration with machines, and the evolving role of AI in human thinking.

Explore the basics of statistics, including population and sample concepts, descriptive and inferential statistics, parameters, and data types. Delve into sampling procedures, probability distributions, normal distribution, hypothesis testing, and ANOVA.

Discover how statistics underpin business decisions through descriptive statistics and inference. Explore data collection, sampling, and testing as tools to understand populations and inform finance and government applications.

Explore population versus sample concepts, and how to select representative samples from global and regional groups using random sampling, to estimate metrics like mean and variance.

Examine mean, median, and mode as measures of central tendency and how symmetry or left side of a distribution changes their values, with practical insights from data examples.

Explore measures of dispersion, including range, quartiles, percentiles, and box plots, and learn how standard deviation and the 68-95-99.7 rules describe data spread around the mean.

Learn to add a data analysis tab in Excel and apply descriptive statistics, correlation, and single factor analysis to explore data.

Describe a population and generalize from samples using descriptive and inferential statistics; interpret data with measures like mean, median, and standard deviation, and apply in Excel data analysis.

Explore data types and variables by distinguishing continuous and discrete data, and nominal, ordinal, and categorical classifications. See how independent and dependent variables drive basic machine learning analyses.

Explore the normal distribution and its standard normal form, centered at the mean with a standard deviation of one, noting about 60% within one sigma and 99.7% within three sigmas.

Explore binomial and Poisson distributions, understanding two-outcome probability models and event counts in real-world scenarios like call centers and pizza orders.

Discover how to test hypotheses by comparing the null and alternative, interpret p values, and decide rejection or acceptance using critical values and 95% confidence.

Find the z critical value for a one-tail 95% confidence, illustrated with projected values and the 1.645 threshold.

Explore the confusion matrix and the types of error alpha and beta, and discuss minimizing prediction errors while balancing good versus imperfect predictions in practical examples.

Explore the standard normal distribution through hands-on examples, calculating probabilities and p-values, and formulating and testing a null hypothesis about a population mean.

Explore the student's t distribution, its degree of freedom, and hypothesis testing concepts, as you analyze samples, confidence levels, and comparisons to normal models.

Explore the chi-square distribution and its use in goodness-of-fit and independence tests for categorical data, including setting the null hypothesis and comparing observed and expected frequencies.

Explore various distributions, including the uniform distribution, and understand how data distributions guide machine learning when faced with new distributions.

Explore sampling procedures in statistics, including random sampling, stratified random sampling, and systematic sampling, alongside non-probability approaches, highlighting sample size, bias, and practical considerations.

Explore the central limit theorem through random sampling of large data and observe how distribution patterns emerge across repeated samples.

Learn how to determine an appropriate sample size for a survey using a calculator and practical examples, including percent guidelines and sampling across multiple offices at a company like IBM.

Learn to perform a one-way ANOVA to compare group means, test the null hypothesis that all means are equal, and interpret p-values for treatment effects.

Explore how data distributions shape modeling decisions, from symmetric and centered continuous distributions to categorical variants, and how modern machine learning algorithms adapt to these choices.

Explore graph types and how to visualize group data with charts to compare communities. Interpret graphs and apply these concepts to real data.

Explore chart types and scatter plots to understand data distributions and relationships by plotting numeric variables on the x and y axes using Excel.

Explore how correlation measures relationships from -1 to 1, with positive or negative patterns, visualize via graphs, and note that correlation is not causation in data analysis and business contexts.

Explore box plots, including how to read the minimum, maximum, quartiles, median, and mean line, and use box length to assess data variation.

Explore grouped data with frequency distributions, histograms, and cumulative frequency, and learn to interpret percentiles and quartiles using Pareto and multi-variance charts for data-driven decisions.

Learn by doing a hands-on introduction to basic python, set up an anaconda environment, and explore numeric and categorical data types, including discrete, nominal, ordinal, and binary variables.

Cover Python strings basics, including indexing, slicing, concatenation, repetition, formatting, joining, splitting, replacing, and capitalization to manipulate text.

Explore tuples, lists, and dictionaries in Python, with unpacking, indexing, and basic operations. Learn how to define, update, delete, and access dictionary items by keys and values.

Explore basic Python operators, including arithmetic addition and boolean logic, with in and not in, and working with lists. Learn function definitions, indentation, and how to run simple examples.

Explore regular expressions in Python, learning to search, match, and substitute text. Learn basic patterns, anchoring at the string start, and using the re module.

Learn Python date and time handling, parsing and formatting dates across different frames, and extracting day, month, and weekday using flexible formatting tokens.

Learn how to work with a panda data frame in Python, read and inspect data, convert object columns to category, and apply functions to transform columns.

Master pandas basics by maneuvering a data frame, select columns, view heads, access via index, create temp columns, drop columns, and apply simple condition-based operations.

Explore data frames with various join and table operations, including concatenation and adding columns. Apply group by origin to compute counts and summarize data.

Explore how to visualize data with basic Python and pandas, creating and customizing graphs such as histograms and box plots, and save and interpret plots for statistical insight.

Learn NumPy basics by converting lists to arrays, inspecting data types, and handling two-dimensional data. Master indexing, including first, last, and negative indices, with row and column axes.

Master numpy slicing to select rows and columns, using inclusive and exclusive indices for precise data access. Explore reshaping arrays from one to two dimensions and reference the last column.

Explore various join and stacking concepts while mastering deep copy versus shallow copy in data structures, with practical implications for machine learning workflows.

Identify missing values in a dataset, learn practical steps to handle them, create a temporary data frame for practice, and explore filling or addressing gaps with data from the source.

Count missing values, examine per-column missing data, and drop incomplete rows, avoiding interpolation and prioritizing complete data from the source with business understanding.

Encode categorical features by turning a column with three unique values into a new encoded column, such as American, European, and Japanese. Then build a dummy data frame, drop the original column, and sort the data ascending or descending to illustrate the encoding results.

Learn probability functions by plotting the standard normal distribution in python and computing the cumulative normal probability using the cumulative density function.

Learn to generate random samples, avoid duplicates, and log program activity with configurable levels and handlers, including console and file logging, and implement exception handling.

Explore the introduction and history of the R language, cover installation and setup, and begin applying basic statistics to data using open source tools.

Learn to declare and print vectors, work with lists, and explore basic matrix ideas, while understanding data types such as character, string, integer, and floating in practical exercises.

Explore how to define and manipulate a data frame: create and name columns, print the frame, access and update columns, add new columns, and perform basic indexing.

Learn how to apply functions to columns or rows using apply, compute the mean of each column, and use a custom function for analysis.

Learn to handle missing data by checking data sources for corrections, identifying missingness patterns, and using a library that helps analyze missing data for practical replacement decisions.

Learn how to encode categorical features, identify unique values in columns, and clean and finalize encoded columns by removing origin columns for a streamlined data table.

Learn to create and customize graphs using ggplot, including line, box, and histogram plots; adjust axes, colors, titles, and save visualizations for data analysis.

Learn how to use cbind and rbind in R to bind columns and rows, organizing data into column-wise and row-wise structures for flexible data assembly.

Sort data by any column, apply default and descending orders, and work with date time formats in R by converting, printing, and extracting values.

Explore saving any object you create and managing its structure. Then apply common mathematical functions like absolute value, natural logarithm, floor, and concatenation in data workflows.

Explore standard normal concepts and the normal density function, compute cumulative normal values, and visualize with plots and histograms while simulating normal, uniform, and Poisson distributions.

Learn measures of central tendency, including mean and median, standard deviation, handle missing values, and apply basic sampling and user-defined functions in a data vector.

Set up a logging system with handlers to write messages to files or the console, configure log levels (info, warning, error), and apply try/catch blocks for robust exception handling.

Explore what machine learning is, from Turing's definition to data-driven learning, powerful algorithms like SVM and deep learning, the role of data and infrastructure, and the challenges of teaching machines.

Explore the three core types of machine learning—unsupervised learning, supervised learning, and reinforcement learning—through practical concepts like clustering, outliers, prediction, and reward-driven learning.

Explore unsupervised machine learning with clustering, dimensionality reduction via principal component analysis, and methods to choose cluster counts using the elbow technique, plus practical k-means workflows.

Discover how association, or market basket analysis, reveals item co-purchasing patterns in retail to maximize sales and optimize product placement, with applications across marketing, tourism, and fraud.

Explain supervised machine learning, mapping input data to an output variable, with regression for numeric outcomes and classification for category outcomes, including nominal and ordinal examples.

Explore random forest, an ensemble method using bagging with bootstrap samples and replacement to create diverse subsets, then build multiple trees and average their predictions.

Explore xgboost, which builds models on successive data subsets and combines their predictions to form a final outcome, enabling effective classification.

Examine the overview of supervised and unsupervised algorithms, including regularization, clustering, neural networks, and deep learning, and learn how mastering one technique enables exploring others in machine learning.

Reinforcement learning blends computer science with mathematics, economics, and psychology to learn from experience via a reward system. The approach uses feedback from past actions to continue learning on the job.

Explore regression, classification, and clustering within supervised and unsupervised learning to power customer segmentation, targeted marketing, and recommender systems, with forecasting and business impact.

Explore the trade-off between data quantity and quality, noting missing or incomplete data and holes in observations reduce value; aim for small, clean, complete, high-quality data as a starting point.

Explore next steps after an overview of AI, basic statistics, basic Python, basic R, and ML, with guidance for non-technical and technical learners on choosing courses and starting points.