Description
Today Data Science and Machine Learning are used in almost every industry, including automobiles, banks, health, telecommunications, telecommunications, and more. As the manager of Data Science and Machine Learning, you will have to research and look beyond common problems, you may need to do a lot of data processing. test data using advanced tools and build amazing business solutions. However, where and how will you learn these skills required in Data Science and Machine Learning?DATA SCIENCE COURSE-OVERVIEWGetting Started with Data ScienceDefine DataWhy Data Science?Who is a Data Scientist?What does a Data Scientist do?The lifecycle of Data Science with the help of a use caseJob trendsData Science ComponentsData Science Job RolesMath BasicsMultivariable CalculusFunctions of several variablesDerivatives and gradientsStep function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) functionCost functionPlotting of functionsMinimum and Maximum values of a functionLinear AlgebraVectorsMatricesTranspose of a matrixThe inverse of a matrixThe determinant of a matrixDot productEigenvaluesEigenvectorsOptimization MethodsCost function/Objective functionLikelihood functionError functionGradient Descent Algorithm and its variants (e. g., Stochastic Gradient Descent Algorithm)Programming BasicsR Programming for Data ScienceHistory of RWhy R?R InstallationInstallation of R StudioInstall R Packages. R for businessFeatures of RBasic R syntaxR programming fundamentalsFoundational R programming concepts such as data types, vectors arithmetic, indexing, and data framesHow to perform operations in R including sorting, data wrangling using dplyr, and data visualization with ggplot2Understand and use the various graphics in R for data visualization. Gain a basic understanding of various statistical concepts. Understand and use hypothesis testing method to drive businessdecisions. Understand and use linear, non-linear regression models, andclassification techniques for data analysis. Working with data in RMaster R programming and understand how various statements are executed in R. Python for Data ScienceIntroduction to Python for Data ScienceIntroduction to PythonPython InstallationPython Environment SetupPython Packages InstallationVariables and DatatypesOperatorsPython Pandas-IntroPython Numpy-IntroPython SciPy-IntroPython Matplotlib-IntroPython BasicsPython Data StructuresProgramming FundamentalsWorking with data in PythonObject-oriented programming aspects of PythonJupyter notebooksUnderstand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functionsPerform high-level mathematical computing using the NumPy package and its vast library of mathematical functionsPerform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and WeavePerform data analysis and manipulation using data structures and tools provided in the Pandas packageGain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipelineUse the matplotlib library of Python for data visualizationExtract useful data from websites by performing web scraping usingPythonIntegrate Python with MapReduceData BasicsLearn how to manipulate data in various formats, for example, CSV file, pdf file, text file, etc. Learn how to clean data, impute data, scale data, import and export data, and scrape data from the internet. Learn data transformation and dimensionality reduction techniques such as covariance matrix plot, principal component analysis (PCA), and linear discriminant analysis (LDA). Probability and Statistics BasicsImportant statistical concepts used in data scienceDifference between population and sampleTypes of variablesMeasures of central tendencyMeasures of variabilityCoefficient of varianceSkewness and KurtosisInferential StatisticsRegression and ANOVAExploratory Data AnalysisData visualizationMissing value analysisIntroduction to Big DataIntroduction to HadoopIntroduction to TableauIntroduction to Business AnalyticsIntroduction to Machine Learning BasicsSupervised vs UnsupervisedTime Series AnalysisText MiningData Science Capstone ProjectScience and Mechanical Data require in-depth knowledge on a variety of topics. Scientific data is not limited to knowing specific packages/libraries and learning how to use them. Science and Mechanical Data requires an accurate understanding of the following skills, Understand the complete structure of Science and Mechanical DataDifferent Types of Data Analytics, Data Design, Scientific Data Transfer Features and Machine Learning ProjectsPython Programming Skills which is the most popular language in Science and Mechanical DataMachine Learning Mathematics including Linear Algebra, Calculus and how to apply it to Machine Learning Algorithms and Science DataMathematics and Mathematical Analysis of Data ScienceData Science Data RecognitionData processing and deception before installing Learning MachinesMachine learningRidge (L2), Lasso (L1), and Elasticnet Regression / Regularization for Machine LearningSelection and Minimization Feature for Machine Learning ModelsSelection of Machine Learning Model using Cross Verification and Hyperparameter TuningAnalysis of Machine Learning Materials GroupsIn-depth learning uses the most popular tools and technologies of today. This Data Science and Machine Learning course is designed to consider all of the above, True Data Science and Machine Learning A-Z Course. In most Data Science and Machine Learning courses, algorithms are taught without teaching Python or this programming language. However, it is very important to understand language structure in order to apply any discipline including Data Science and Mechanical Learning. Also, without understanding Mathematics and Statistics it is impossible to understand how other Data Science and Machine Learning algorithms and techniques work. Science and Mechanical Data is a set of complex linked topics. However, we strongly believe in what Einstein once said, If you can't explain it easily, you didn't understand it well enough. As a teacher, I constantly strive to reach my goal. This is one comprehensive course in Science and Mechanical Data that teaches you everything you need to learn Science and Mechanical Data using simple examples with great depth. As you will see from the preview talks, some of the more complex topics are explained in simple language. Some important skills you will learn, Python ProgrammingPython is listed as the # 1 language for Data Science and Mechanical Data. It is easy to use and rich with various libraries and functions required to perform various Data Science and Machine Learning activities. In addition, it is the most widely used and automated language for the use of many Deep Learning frameworks including Tensorflow and Keras. Advanced Mathematics Learning MachineMathematics is the foundation of Data Science in general and Learning Machines in particular. Without understanding the meanings of Vectors, Matrices, their operations and understanding Calculus, it is impossible to understand the basics of Data Science and Machine Learning. The Gradient Declaration of Basic Neural Network and Mechanical Learning is built on the foundations of Calculus and Derivatives. Previous Statistics for Data ScienceIt is not enough to know only what you are saying, in the middle, the mode, etc. Advanced Techniques for Science and Mechanical Data such as feature selection, size reduction using PCA are all based on previous Distribution and Statistical Significance calculations. It also helps us to understand the operation of the data and use the appropriate machine learning process to get the best results from various Data Science and Mechanical Learning techniques. Data recognitionAs they say, the picture costs a thousand words. Data identification is one of the most important methods of Data Science and Mechanical Data and is used for Analytical Data Analysis. In that, we analyze the data visually to identify patterns and styles. We will learn how to create different sites and charts and how to analyze them for all practical purposes. Feature Selection plays an important role in Machine Learning and Visualization Data is its key. Data processingScientific Data requires extensive data processing. Data Science and Machine Learning specialists spend more than 2/3 of their time analyzing and analyzing data. Data can be noisy and never in good condition. Data processing is one of the most important ways for Data Science and Mechanics to learn to get the best results. We will be using Pandas which is a well-known Python data processing library and various other libraries for reading, analyzing, processing and cleaning data. Machine learningHeart and Soul Data Science is a guessing skill provided by algorithms from the Deep Learning and Learning Machines. Machine learning takes the complete discipline of Data Science ahead of others. We will integrate everything we have learned in previous sections and build learning models for various machines. The key features of Machine Learning are not only ingenuity but also understanding of the various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values affect the outcome in order to build the best machine learning models.
