Aug 30 2023 Data Science In Python: Regression & Forecasting BaDshaH LEARNING / e-learning - Tutorials 01:20 0 Published 8/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 3.19 GB | Duration: 8h 31mLearn Python for Data Science & Machine Learning, and build regression and forecasting models with hands-on projects What you'll learnMaster the machine learning foundations for regression analysis in PythonPerform exploratory data analysis on model features, the target, and relationships between themBuild and interpret simple and multiple linear regression models with Statsmodels and Scikit-LearnEvaluate model performance using tools like hypothesis tests, residual plots, and mean error metricsDiagnose and fix violations to the assumptions of linear regression modelsTune and test your models with data splitting, validation and cross validation, and model scoringLeverage regularized regression algorithms to improve test model performance & accuracyEmploy time series analysis techniques to identify trends & seasonality, perform decomposition, and forecast future valuesRequirementsWe strongly recommend taking our Data Prep & EDA course firstJupyter Notebooks (free download, we'll walk through the install)Familiarity with base Python and Pandas is recommended, but not requiredDescriptionThis is a hands-on, project-based course designed to help you master the foundations for regression analysis in Python.We'll start by reviewing the data science workflow, discussing the primary goals & types of regression analysis, and do a deep dive into the regression modeling steps we'll be using throughout the course.You'll learn to perform exploratory data analysis, fit simple & multiple linear regression models, and build an intuition for interpreting models and evaluating their performance using tools like hypothesis tests, residual plots, and error metrics. We'll also review the assumptions of linear regression, and learn how to diagnose and fix each one.From there, we'll cover the model testing & validation steps that help ensure our models perform well on new, unseen data, including the concepts of data splitting, tuning, and model selection. You'll also learn how to improve model performance by leveraging feature engineering techniques and regularized regression algorithms.Throughout the course, you'll play the role of Associate Data Scientist for Maven Consulting Group on a team that focuses on pricing strategy for their clients. Using the skills you learn throughout the course, you'll use Python to explore their data and build regression models to help firms accurately predict prices and understand the variables that impact them.Last but not least, you'll get an introduction to time series analysis & forecasting techniques. You'll learn to analyze trends & seasonality, perform decomposition, and forecast future values.COURSE OUTLINE:Intro to Data ScienceIntroduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflowRegression 101Review the basics of regression, including key terms, the types and goals of regression analysis, and the regression modeling workflowPre-Modeling Data Prep & EDARecap the data prep & EDA steps required to perform modeling, including key techniques to explore the target, features, and their relationshipsSimple Linear RegressionBuild simple linear regression models in Python and learn about the metrics and statistical tests that help evaluate their quality and outputMultiple Linear RegressionBuild multiple linear regression models in Python and evaluate the model fit, perform variable selection, and compare models using error metricsModel AssumptionsReview the assumptions of linear regression models that need to be met to ensure that the model's predictions and interpretation are validModel Testing & ValidationTest model performance by splitting data, tuning the model with the train & validation data, selecting the best model, and scoring it on the test dataFeature EngineeringApply feature engineering techniques for regression models, including dummy variables, interaction terms, binning, and moreRegularized RegressionIntroduce regularized regression techniques, which are alternatives to linear regression, including Ridge, Lasso, and Elastic Net regressionTime Series AnalysisLearn methods for exploring time series data and how to perform time series forecasting using linear regression and Facebook Prophet__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:8.5 hours of high-quality video14 homework assignments10 quizzes3 projectsData Science in Python: Regression ebook (230+ pages)Downloadable project files & solutionsExpert support and Q&A forum30-day Udemy satisfaction guaranteeIf you're an aspiring data scientist looking for an introduction to the world of regression modeling with Python, this is the course for you.Happy learning!-Chris Bruehl (Data Science Expert & Lead Python Instructor, Maven Analytics)OverviewSection 1: Getting StartedLecture 1 Course IntroductionLecture 2 About This SeriesLecture 3 Course Structure & OutlineLecture 4 READ ME: Important Notes for New StudentsLecture 5 DOWNLOAD: Course ResourcesLecture 6 Introducing the Course ProjectLecture 7 Setting ExpectationsLecture 8 Jupyter Installation & LaunchSection 2: Intro to Data ScienceLecture 9 What is Data Science?Lecture 10 Data Science SkillsetLecture 11 What is Machine Learning?Lecture 12 Common Machine Learning AlgorithmsLecture 13 Data Science WorkflowLecture 14 Step 1: Scoping a ProjectLecture 15 Step 2: Gathering DataLecture 16 Step 3: Cleaning DataLecture 17 Step 4: Exploring DataLecture 18 Step 5: Modeling DataLecture 19 Step 6: Sharing InsightsLecture 20 Regression ModelingLecture 21 Key TakeawaysSection 3: Regression 101Lecture 22 Regression 101Lecture 23 Goals of RegressionLecture 24 Types of RegressionLecture 25 Regression Modeling WorkflowLecture 26 Key TakeawaysSection 4: Pre-Modeling Data Prep & EDALecture 27 EDA for RegressionLecture 28 Exploring the TargetLecture 29 Exploring the FeaturesLecture 30 ASSIGNMENT: Exploring the Target & FeaturesLecture 31 SOLUTION: Exploring the Target & FeaturesLecture 32 Linear Relationships & CorrelationLecture 33 Linear Relationships in PythonLecture 34 Feature-Target RelationshipsLecture 35 Feature-Feature RelationshipsLecture 36 PRO TIP: Pairplots & LmplotsLecture 37 ASSIGNMENT: Exploring RelationshipsLecture 38 SOLUTION: Exploring RelationshipsLecture 39 Preparing For ModelingLecture 40 Key TakeawaysSection 5: Simple Linear RegressionLecture 41 Simple Linear RegressionLecture 42 The Linear Regression ModelLecture 43 Least Squared ErrorLecture 44 Linear Regression in PythonLecture 45 Linear Regression in StatsmodelsLecture 46 Interpreting the ModelLecture 47 Making PredictionsLecture 48 R-SquaredLecture 49 Hypothesis TestsLecture 50 The F-TestLecture 51 Coefficient Estimates & P-ValuesLecture 52 Residual PlotsLecture 53 CASE STUDY: Modeling Health Insurance PricesLecture 54 ASSIGNMENT: Simple Linear RegressionLecture 55 SOLUTION: Simple Linear RegressionLecture 56 Key TakeawaysSection 6: Multiple Linear RegressionLecture 57 Multiple Linear Regression EquationLecture 58 Fitting a Multiple Linear RegressionLecture 59 Interpreting Multiple Linear Regression ModelsLecture 60 Variable SelectionLecture 61 ASSIGNMENT: Multiple Linear RegressionLecture 62 SOLUTION: Multiple Linear RegressionLecture 63 Mean Error MetricsLecture 64 DEMO: Mean Error MetricsLecture 65 Adjusted R-SquaredLecture 66 ASSIGNMENT: Mean Error MetricsLecture 67 SOLUTION: Mean Error MetricsLecture 68 Key TakeawaysSection 7: Model AssumptionsLecture 69 Assumptions of Linear RegressionLecture 70 LinearityLecture 71 Independence of ErrorsLecture 72 Normality of ErrorsLecture 73 DEMO: Normality of ErrorsLecture 74 PRO TIP: Interpreting Transformed TargetsLecture 75 No Perfect MulticollinearityLecture 76 Equal Variance of ErrorsLecture 77 Outliers, Leverage & InfluenceLecture 78 RECAP: Assumptions of Linear RegressionLecture 79 ASSIGNMENT: Model AssumptionsLecture 80 SOLUTION: Model AssumptionsLecture 81 Key TakeawaysSection 8: Model Testing & ValidationLecture 82 Model Scoring StepsLecture 83 Data SplittingLecture 84 Overfitting & UnderfittingLecture 85 The Bias-Variance TradeoffLecture 86 Validation DataLecture 87 Model TuningLecture 88 Model ScoringLecture 89 Cross ValidationLecture 90 Simple vs. Cross ValidationLecture 91 ASSIGNMENT: Model Testing & ValidationLecture 92 SOLUTION: Model Testing & ValidationLecture 93 Key TakeawaysSection 9: Feature EngineeringLecture 94 Intro To Feature EngineeringLecture 95 Feature Engineering TechniquesLecture 96 Polynomial TermsLecture 97 Combining FeaturesLecture 98 Interaction TermsLecture 99 Categorical FeaturesLecture 100 Dummy VariablesLecture 101 DEMO: Dummy VariablesLecture 102 Binning Categorical DataLecture 103 Binning Numeric DataLecture 104 DEMO: Additional Feature Engineering IdeasLecture 105 ASSIGNMENT: Feature EngineeringLecture 106 SOLUTION: Feature EngineeringLecture 107 Key TakeawaysSection 10: Project 1: San Francisco Rent PricesLecture 108 Project BriefLecture 109 Solution WalkthroughSection 11: Regularized RegressionLecture 110 Intro to Regularized RegressionLecture 111 Ridge RegressionLecture 112 StandardizationLecture 113 Fitting a Ridge Regression ModelLecture 114 DEMO: Fitting a Ridge RegressionLecture 115 PRO TIP: RidgeCVLecture 116 ASSIGNMENT: Ridge RegressionLecture 117 SOLUTION: Ridge RegressionLecture 118 Lasso RegressionLecture 119 PRO TIP: LassoCVLecture 120 ASSIGNMENT: Lasso RegressionLecture 121 SOLUTION: Lasso RegressionLecture 122 Elastic Net RegressionLecture 123 DEMO: Fitting an Elastic Net RegressionLecture 124 PRO TIP: ElasticNetCVLecture 125 ASSIGNMENT: Elastic Net RegressionLecture 126 SOLUTION: Elastic Net RegressionLecture 127 RECAP: Regularized Regression ModelsLecture 128 PREVIEW: Tree Based ModelsLecture 129 Key TakeawaysSection 12: Project 1: San Francisco Rent Prices (Continued)Lecture 130 Project BriefLecture 131 Solution WalkthroughSection 13: Time Series AnalysisLecture 132 Intro to Time SeriesLecture 133 Moving AveragesLecture 134 DEMO: Moving AveragesLecture 135 Exponential SmoothingLecture 136 ASSIGNMENT: SmoothingLecture 137 SOLUTION: SmoothingLecture 138 DecompositionLecture 139 DEMO: DecompositionLecture 140 PRO TIP: Autocorrelation ChartLecture 141 ASSIGNMENT: DecompositionLecture 142 SOLUTION: DecompositionLecture 143 ForecastingLecture 144 Linear Regression With Trend & SeasonLecture 145 DEMO: Linear Regression With Trend & SeasonLecture 146 Facebook ProphetLecture 147 ASSIGNMENT: ForecastingLecture 148 SOLUTION: ForecastingLecture 149 Key TakeawaysSection 14: Project 2: Electricity ConsumptionLecture 150 Project BriefLecture 151 Solution WalkthroughSection 15: Next StepsLecture 152 EXTRA LESSONData analysts or BI experts looking to transition into a data science role,Python users who want to build the core skills for applying regression models in Python,Anyone interested in learning one of the most popular open source programming languages in the worldHomepagehttps://www.udemy.com/course/data-science-in-python-regression/Download From Rapidgatorhttps://rapidgator.net/file/d346b6c0448d7ec7a1e5c0209dba948chttps://rapidgator.net/file/5650c331b1cd4e26011a608fc87c636ahttps://rapidgator.net/file/e9b3802241028b2b661994a7fc5fb3e6https://rapidgator.net/file/cd9bde20f65aa81dc242a3487205f9e8Download From Nitroflarehttps://nitroflare.com/view/84492A812BA306Ahttps://nitroflare.com/view/2AD9EC5663EAB24https://nitroflare.com/view/2E2D720A2349FA6https://nitroflare.com/view/7B8EB4BD1942712Download From 1DLhttps://1dl.net/55w2fifcj27ihttps://1dl.net/5j85qdalvrjxhttps://1dl.net/aap0yn3qb8duhttps://1dl.net/ivz37udn1hsz Related News Python & Data Science with R | Python & R ProgrammingThe Complete Visual Guide To Machine Learning & Data ScienceX4 Foundations Kingdom End-RUNEPython- Numpy & Pandas Python Programming Language LibrariesPython - Complete Python, Django, Data Science And Ml Guide Comments (0)Add comment Submit NEWEST RELEASES 17.05: StreamFab 6.1.7.8 (x64) Multilingual Portable 17.05: Atlantis Word Processor 4.3.10 17.05: TunesBank Disney+ Downloader 1.5.2 Multilingual 17.05: TunesBank Apple TV+ Downloader 1.2.4 Multilingual 17.05: Ashampoo Snap 16.0.5 (x64) Multilingual 17.05: Danil Pristupov Fork 1.97 17.05: Remote Desktop Manager Enterprise 2024.1.29 (x64) Multilingual 17.05: Adobe Substance 3D Painter 10.0.0 (x64) Multilingual 17.05: Smart PC Optimizer PRO 9.4.0.4 17.05: Astute Graphics Plug-ins Elite Bundle 3.8.4 Recommended Filehosts Freinds Site