Data Science, Ai, And Machine Learning With R

Gain practical experience in R for Data Analysis, Machine Learning and Artificial Intelligence. Become a Data Scientist.

What you'll learn

Grasp the core concepts of data science and its applications in various industries.

Set up and navigate the R programming environment effectively.

Master R programming fundamentals, including data types, structures, operators, and control flow.

Understand essential statistical and probability concepts for data analysis.

Collect data from diverse sources (flat files, databases, web, APIs).

Clean, manipulate, and preprocess data to ensure its quality and suitability for analysis.

Conduct exploratory data analysis to uncover patterns and insights using visualizations.

Analyze and interpret data effectively using R's powerful statistical and visualization tools.

Build and evaluate various machine learning models for: Prediction (regression), Classification, Clustering, Association rule mining.

Apply dimensionality reduction methods like PCA and LDA.

Utilize ensemble methods (bagging and boosting) to improve model performance.

Build and deploy machine learning models using R to solve real-world problems.

Think critically about data and apply data science techniques in a variety of contexts.

Complete an end-to-end capstone project to solidify learning and demonstrate practical skills in data science and machine learning using R.

Requirements

Enthusiasm and determination to make your mark on the world!

Description

A warm welcome to the Data Science, Artificial Intelligence, and Machine Learning with R course by Uplatz.R Programming LanguageConcept: R is a free, open-source programming language and software environment designed for statistical computing and graphics. It is widely used by statisticians, data scientists, and researchers.Key Strengths in the Context of Data Science, AI & ML:Vast Ecosystem: R boasts a rich collection of packages (over 18,000+) contributed by the community, covering a broad spectrum of data analysis and machine learning tasks.Data Visualization: R's powerful visualization libraries (like ggplot2) create publication-quality plots and interactive graphics, aiding in data exploration and communication of insights.Statistical Power: R's foundation in statistics provides a strong base for data analysis, hypothesis testing, and modeling.Reproducibility: R encourages reproducible research through its literate programming capabilities (R Markdown), making it easier to document and share the entire analysis process.Data ScienceConcept: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves various techniques, including data mining, statistics, machine learning, and visualization.R's Role in Data Science: R provides a robust environment for data science tasks. Its extensive libraries (like dplyr, tidyr, ggplot2) enable data cleaning, manipulation, exploration, and visualization. R's statistical capabilities make it ideal for hypothesis testing, modeling, and drawing inferences from data.Data Manipulation and Cleaning: R excels at data manipulation and cleaning, using packages like dplyr, tidyr, and data.table. These tools help in transforming and preparing data for analysis.Exploratory Data Analysis (EDA): R provides extensive tools for EDA, allowing users to summarize datasets, detect outliers, and identify trends. Functions in base R along with packages like ggplot2 are commonly used for this purpose.Statistical Analysis: R was built for statistics, so it offers a wide array of functions for hypothesis testing, regression analysis, ANOVA, and more. Packages like stats, MASS, and lmtest are frequently used for statistical modeling.Data Visualization: R is renowned for its data visualization capabilities. ggplot2 is a powerful package for creating complex, multi-layered graphics. Other packages like lattice and plotly allow for interactive visualizations.Artificial Intelligence (AI)Concept: AI is a broad field of computer science that aims to create intelligent agents capable of mimicking human-like cognitive functions such as learning, reasoning, problem-solving, perception, and language understanding.R's Role in AI: While R isn't the primary language for core AI development (like Python or C++), it plays a vital role in AI research and applications. R's statistical and machine learning libraries (like caret, randomForest) facilitate building predictive models, evaluating their performance, and interpreting results.Statistical Learning: R supports various statistical learning methods, which are foundational for AI. Libraries like caret and mlr provide tools for building and evaluating statistical models.Natural Language Processing (NLP): While Python is more popular for NLP, R has packages like tm and quanteda for text mining and processing tasks. These can be used for sentiment analysis, topic modeling, and other NLP tasks.Computer Vision: R can be used for basic computer vision tasks through packages like EBImage. However, for more complex tasks, Python is generally preferred due to its more extensive libraries.Integration with Python: For AI tasks where Python’s libraries are more advanced, R can be integrated with Python through the reticulate package, allowing users to leverage Python’s AI capabilities while staying within the R environment.Machine Learning (ML)Concept: ML is a subset of AI that focuses on developing algorithms that enable systems to learn from data and improve their performance on a specific task without being explicitly programmed.R's Role in Machine Learning: R shines in the machine learning domain. It offers a comprehensive collection of machine learning algorithms (regression, classification, clustering, etc.) and tools for model building, evaluation, and tuning. Packages like caret simplify the process of training and comparing various models.Model Development: R offers several packages for building machine learning models, such as randomForest, xgboost, and caret. These tools help in creating models like decision trees, random forests, and gradient boosting machines.Model Evaluation: R provides robust tools for evaluating model performance, including cross-validation, ROC curves, and other metrics. The caret package is particularly useful for this purpose.Feature Engineering: R’s data manipulation packages, like dplyr and caret, are used for feature engineering, which involves creating new features from raw data to improve model performance.Deep Learning: While Python dominates deep learning, R has packages like keras and tensorflow that provide an interface to TensorFlow, allowing users to build deep learning models within R.Deployment: R can be used to deploy models into production environments. The plumber package, for example, can turn R scripts into RESTful APIs, enabling the integration of R models into applications.Artificial Intelligence, Data Science, and Machine Learning with R - Course Curriculum1. Overview of Data Science and R Environment SetupEssential concepts of data science R language Environment Setup2. Introduction and Foundation Principles of R ProgrammingBasic concepts of R programming3. Data CollectionEffective ways of handling various file types and importing techniques4. Probability & StatisticsUnderstanding patterns, summarizing data mastering statistical thinking and probability theory5. Exploratory Data Analysis & Data VisualizationMaking the data ready using charts, graphs, and interactive visualizations to use in statistical models6. Data Cleaning, Data Manipulation & PreprocessingGarbage in - Garbage out (Wrangling/Munging):7. Statistical Modeling & Machine LearningSet of algorithms that use data to learn, generalize, and predict8. End to End Capstone Project1. Overview of Data Science and R Environment Setupa. Overview of Data ScienceIntroduction to Data ScienceComponents of Data ScienceVerticals influenced by Data ScienceData Science Use cases and Business ApplicationsLifecycle of Data Science Projectb. R language Environment SetupIntroduction to Anaconda DistributionInstallation of R and R StudioAnaconda Navigator and Jupyter Notebook with RMarkdown Introduction and ScriptingR Studio Introduction and Features2. Introduction and Foundation Principles of R Programminga. Overview of R environment and core R functionalityb. Data typesNumeric (integer and double)complexcharacter and factorlogicaldate and timeRawc. Data structuresvectorsmatricesarrayslistsdata framesd. Operatorsarithmeticrelationallogicalassignment Operatorse. Control Structures & Loopsfor, whileif elserepeat, next, breakswitch caseg. Functions apply family functions          (i) apply         (ii) lapply        (iii) sapply        (iv) tapply         (v) mapplyBuilt-in functionsUser defined functions3. Data Collectiona. Data Importing techniques, handling inaccurate and inconsistent datab. Flat-files dataread.csvread.tableread.csv2read.delimread.delim2c. Excel datareadxlxlsxreadrxlconnectgdatad. Databases (MySQL, SQLite…etc)RmySQLRSQLitee. Statistical software's data (SAS, SPSS, stata, etc.)foreignhavenhmiscf. web-based data (HTML, xml, json, etc.)rvest packagerjson packageg. Social media networks (Facebook Twitter Google sheets APIs)RfacebooktwitteR4. Probability & Statisticsa. Core concepts of mastering in statistical thinking and probability theoryb. Descriptive Statistics    Types of Variables & Scales of Measurement       (i) Qualitative/Categorical           1) Nominal           2) Ordinal       (ii) Quantitative/Numerical           1) Discrete           2) Continuous           3) Interval           4) RatioMeasures of Central Tendency       (i) Mean, median, modeMeasures of Variability & Shape       (i) Standard deviation, variance and Range, IQR      (ii) Sleekness & Kurtosisc. Probability & DistributionsIntroduction to probabilitybinomial distributionuniform distributiond. Inferential StatisticsSampling & Sampling DistributionCentral Limit TheoremConfidence Interval EstimationHypothesis Testing5. Exploratory Data Analysis & Data Visualizationa. Understanding patterns, summarizing data and presentation using charts, graphs and interactive visualizationsb. Univariate data analysisc. Bivariate data analysisd. Multivariate Data analysise. Frequency Tables, Contingency Tables & Cross Tablesf. Plotting Charts and GraphicsScatter plotsBar Plots / Stacked bar chartPie ChartsBox plotsHistogramsLine Graphsggplot2, lattice packages6. Data Cleaning, Data Manipulation & Preprocessinga. Garbage in - garbage out: Data munging or Data wranglingb. Handling errors and outliersc. Handling missing valuesd. Reshape data (adding, filtering, dropping and merging)e. Rename columns and data type conversionf. Duplicate recordsg. Feature selection and feature scalingh. Useful R packagesdata.tabledplyrsqldftidyrreshape2lubridatestringr7. Statistical Modeling & Machine Learninga. Set of algorithms that uses data to learn, generalize, and predictb. RegressionSimple Linear RegressionMultiple Linear RegressionPolynomial Regressionc. ClassificationLogistic RegressionK-Nearest Neighbors (K-NN)Support Vector Machine (SVM)Decision Trees and Random ForestNaive Bayes Classifierd. ClusteringK-Means ClusteringHierarchical clusteringDBSCAN clusteringe. Association Rule MiningAprioriMarket Basket Analysisf. Dimensionality ReductionPrincipal Component Analysis (PCA)Linear Discriminant Analysis (LDA)g. Ensemble MethodsBaggingBoosting8. End to End Capstone ProjectCareer Path and Job Titles after learning RR is primarily used for statistical analysis, data science, and data visualization. It’s particularly popular in academia, research, finance, and industries where data analysis is crucial. Following is a potential career path and the job titles you might target after learning R:1. Entry-Level RolesData Analyst: Uses R to clean, manipulate, and analyze datasets. This role often involves generating reports, creating visualizations, and conducting basic statistical analysis.Statistical Analyst: Focuses on applying statistical methods to analyze data and interpret results. R is commonly used for its rich set of statistical tools.Junior Data Scientist: Works under the supervision of senior data scientists to gather, clean, and analyze data, often using R for data exploration and model building.Research Assistant: Supports research projects by performing data analysis, literature reviews, and statistical testing, often using R for handling data.2. Mid-Level RolesData Scientist: Uses R to build predictive models, perform advanced statistical analysis, and extract actionable insights from data. This role may also involve developing and testing machine learning algorithms.Quantitative Analyst (Quant): Works in finance or trading, using R to analyze financial data, develop pricing models, and perform risk assessment.Biostatistician: Uses R to analyze biological data, often in clinical trials or medical research. This role involves designing experiments, analyzing results, and interpreting the data.Econometrician: Applies statistical methods to economic data to analyze trends, make forecasts, and model economic behavior. R is commonly used for econometric modeling.3. Senior-Level RolesSenior Data Scientist: Leads data science projects, mentors junior team members, and designs complex models to solve business problems using R and other tools.Data Science Manager: Oversees data science teams, ensuring that projects align with business goals. This role involves both technical work and managerial responsibilities.Principal Statistician: Works at a high level within organizations, leading statistical analysis and contributing to the design of studies, experiments, and surveys.Chief Data Officer (CDO): An executive role responsible for the data strategy and governance within an organization. This position requires deep expertise in data science, often with a background in using tools like R.

Overview

Section 1: R Programming For AI - Introduction

Lecture 1 R Programming For AI - Introduction

Section 2: R Installation and Environment Setup

Lecture 2 Part 1 - R Installation and Environment Setup

Lecture 3 Part 2 - R Installation and Environment Setup

Section 3: Object Data Types and Data Structures

Lecture 4 Part 1 - Object Data Types and Data Structures

Lecture 5 Part 2 - Object Data Types and Data Structures

Lecture 6 Part 3 - Object Data Types and Data Structures

Section 4: Operators and Control Structures and Loops

Lecture 7 Part 1 - Operators and Control Structures and Loops

Lecture 8 Part 2 - Operators and Control Structures and Loops

Lecture 9 Part 3 - Operators and Control Structures and Loops

Section 5: Functions Apply Families and Packages

Lecture 10 Part 1 - Functions Apply Families and Packages

Lecture 11 Part 2 - Functions Apply Families and Packages

Lecture 12 Part 3 - Functions Apply Families and Packages

Lecture 13 Part 4 - Functions Apply Families and Packages

Lecture 14 Part 5 - Functions Apply Families and Packages

Lecture 15 Part 6 - Functions Apply Families and Packages

Section 6: Data Importing with R

Lecture 16 Part 1 - Data Importing with R

Lecture 17 Part 2 - Data Importing with R

Lecture 18 Part 3 - Data Importing with R

Section 7: SQL with R for Relational Database

Lecture 19 Part 1 - SQL with R for Relational Database

Lecture 20 Part 2 - SQL with R for Relational Database

Section 8: Statistics and EDA with R

Lecture 21 Part 1 - Statistics and EDA with R

Lecture 22 Part 2 - Statistics and EDA with R

Lecture 23 Part 3 - Statistics and EDA with R

Lecture 24 Part 4 - Statistics and EDA with R

Lecture 25 Part 5 - Statistics and EDA with R

Section 9: Data Cleaning with R

Lecture 26 Part 1 - Data Cleaning with R

Lecture 27 Part 2 - Data Cleaning with R

Lecture 28 Part 3 - Data Cleaning with R

Lecture 29 Part 4 - Data Cleaning with R

Lecture 30 Part 5 - Data Cleaning with R

Section 10: Data Visualizations with R

Lecture 31 Part 1 - Data Visualizations with R

Lecture 32 Part 2 - Data Visualizations with R

Lecture 33 Part 3 - Data Visualizations with R

Lecture 34 Part 4 - Data Visualizations with R

Lecture 35 Part 5 - Data Visualizations with R

Lecture 36 Part 6 - Data Visualizations with R

Lecture 37 Part 7 - Data Visualizations with R

Lecture 38 Part 8 - Data Visualizations with R

Section 11: Machine Learning Introduction

Lecture 39 Machine Learning Introduction

Section 12: Data Cleaning and Preprocessing

Lecture 40 Part 1 - Data Cleaning and Preprocessing

Lecture 41 Part 2 - Data Cleaning and Preprocessing

Section 13: Regression Analysis

Lecture 42 Part 1 - Regression Analysis

Lecture 43 Part 2 - Regression Analysis

Section 14: Classification Techniques

Lecture 44 Part 1 - Classification Techniques

Lecture 45 Part 2 - Classification Techniques

Lecture 46 Part 3 - Classification Techniques

Section 15: Clustering Algorithms

Lecture 47 Clustering Algorithms

Section 16: Dimensionality Reduction

Lecture 48 Dimensionality Reduction

Section 17: Association Rule Mining

Lecture 49 Association Rule Mining

Section 18: Ensemble Learning

Lecture 50 Part 1 - Ensemble Learning

Lecture 51 Part 2 - Ensemble Learning

Section 19: Data Science and Machine Learning Projects in R

Lecture 52 Part 1 - Data Science and Machine Learning Projects in R

Lecture 53 Part 2 - Data Science and Machine Learning Projects in R

Anyone aspiring for a career in Data Science, Machine Learning, and AI.,Data Analysts looking to expand their skill set and move into data science roles.,Data Scientists transitioning from other tools or languages to R.,Machine Learning Engineers seeking to strengthen their foundation in data science and statistics using R.,AI Engineers looking to leverage R's capabilities for data analysis and research.,Business Analysts seeking to leverage data for more advanced insights and decision-making.,Statisticians wanting to learn how to apply their expertise in a machine learning context.,Software Developers or Engineers interested in data-driven applications and AI development.,Researchers from various fields (e.g., social sciences, biology) aiming to apply data science and ML techniques to their work.,Anyone with a strong quantitative background looking to transition into a data science career.,Undergraduate or graduate students studying statistics, computer science, mathematics, or related disciplines.,Researchers and academics interested in incorporating data science and ML into their research.