وب سایت تخصصی شرکت فرین
دسته بندی دوره ها
1

The Complete Visual Guide to Machine Learning & Data Science

سرفصل های دوره

Explore Data Science & Machine Learning topics with simple, step-by-step demos and user-friendly Excel models (NO code!)


1 - Getting Started
  • 1 - Course Structure Outline
  • 2 - READ ME Important Notes for New Students.html
  • 3 - DOWNLOAD Course Resources.html
  • 3 - Maven-ML-Demos.xlsx
  • 3 - Maven-ML-Demos-COMPLETE.xlsx
  • 3 - Visual-Guide-to-Machine-Learning.pdf
  • 4 - Setting Expectations

  • 2 - PART 1 QA Data Profiling
  • 5 - Part 1 QA Data Profiling

  • 3 - Intro to the ML Landscape
  • 6 - Intro to Machine Learning
  • 7 - When is ML the right fit
  • 8 - The Machine Learning Process
  • 9 - The Machine Learning Landscape

  • 4 - Preliminary Data QA
  • 10 - Introduction
  • 11 - Why QA
  • 12 - Variable Types
  • 13 - Empty Values
  • 14 - Range Calculations
  • 15 - Count Calculations
  • 16 - Left Right Censored Data
  • 17 - Table Structure
  • 18 - CASE STUDY Preliminary QA
  • 19 - BEST PRACTICES Preliminary QA

  • 5 - Univariate Profiling
  • 20 - Introduction
  • 21 - Categorical Variables
  • 22 - Discretization
  • 23 - Nominal vs Ordinal
  • 24 - Categorical Distributions
  • 25 - Numerical Variables
  • 26 - Histograms Kernel Densities
  • 27 - CASE STUDY Histograms
  • 28 - Normal Distribution
  • 29 - CASE STUDY Normal Distribution
  • 30 - Univariate Data Profiling
  • 31 - Mode
  • 32 - Mean
  • 33 - Median
  • 34 - Percentile
  • 35 - Variance
  • 36 - Standard Deviation
  • 37 - Skewness
  • 38 - BEST PRACTICES Univariate Profiling

  • 6 - Multivariate Profiling
  • 39 - Introduction
  • 40 - CategoricalCategorical
  • 41 - CASE STUDY Heat Maps
  • 42 - CategoricalNumerical
  • 43 - Multivariate Kernel Densities
  • 44 - Violin Plots
  • 45 - Box Plots
  • 46 - Limitations of Categorical Distributions
  • 47 - NumericalNumerical
  • 48 - Correlation
  • 49 - Correlation vs Causation
  • 50 - Visualizing Third Dimension
  • 51 - CASE STUDY Correlation
  • 52 - BEST PRACTICES Multivariate Profiling
  • 53 - Looking Ahead to Part 2

  • 7 - PART 2 Classification Modeling
  • 54 - Part 2 Classification Modeling

  • 8 - Intro to Classification
  • 55 - Supervised vs Unsupervised Learning
  • 56 - Classification vs Regression
  • 57 - RECAP Key Concepts
  • 58 - Classification 101
  • 59 - Classification Workflow
  • 60 - Feature Engineering
  • 61 - Data Splitting
  • 62 - Overfitting

  • 9 - Classification Models
  • 63 - Common Classification Models
  • 64 - Intro to KNearest Neighbors KNN
  • 65 - KNN Examples
  • 66 - CASE STUDY KNN
  • 67 - Intro to Naive Bayes
  • 68 - Naive Bayes Frequency Tables
  • 69 - Naive Bayes Conditional Probability
  • 70 - CASE STUDY Naive Bayes
  • 71 - Intro to Decision Trees
  • 72 - Decision Trees Entropy 101
  • 73 - Entropy Information Gain
  • 74 - Decision Tree Examples
  • 75 - Random Forests
  • 76 - CASE STUDY Decision Trees
  • 77 - Intro to Logistic Regression
  • 78 - Logistic Regression Example
  • 79 - False Positives vs False Negatives
  • 80 - Logistic Regression Equation
  • 81 - The Likelihood Function
  • 82 - Multivariate Logistic Regression
  • 83 - CASE STUDY Logistic Regression
  • 84 - Intro to Sentiment Analysis
  • 85 - Cleaning Text Data
  • 86 - Bag of Words Analysis
  • 87 - CASE STUDY Sentiment Analysis

  • 10 - Model Selection Tuning
  • 88 - Intro to Selection Tuning
  • 89 - Hyperparameters
  • 90 - Imbalanced Classes
  • 91 - Confusion Matrix
  • 92 - Accuracy Precision Recall
  • 93 - Multiclass Confusion Matrix
  • 94 - Multiclass Scoring
  • 95 - Model Selection
  • 96 - Model Drift
  • 97 - Looking ahead to Part 3

  • 11 - PART 3 Regression Forecasting
  • 98 - Part 3 Regression Forecasting

  • 12 - Intro to Regression
  • 99 - Supervised vs Unsupervised Learning
  • 100 - RECAP Key Concepts
  • 101 - Regression 101
  • 102 - Feature Engineering for Regression
  • 103 - Prediction vs RootCause Analysis

  • 13 - Regression Modeling 101
  • 104 - Intro to Regression Modeling
  • 105 - Linear Relationships
  • 106 - Least Squared Error
  • 107 - Univariate Linear Regression
  • 108 - CASE STUDY Univariate Linear Regression
  • 109 - Multiple Linear Regression
  • 110 - NonLinear Regression
  • 111 - CASE STUDY NonLinear Regression

  • 14 - Model Diagnostics
  • 112 - Intro to Model Diagnostics
  • 113 - Sample Model Output
  • 114 - RSquared
  • 115 - Mean Error Metrics MSE MAE MAPE
  • 116 - Homoskedasticity
  • 117 - Null Hypothesis
  • 118 - FSignificance
  • 119 - TValues PValues
  • 120 - Multicollinearity
  • 121 - Variance Inflation Factor
  • 122 - RECAP Sample Model Output

  • 15 - TimeSeries Forecasting
  • 123 - Intro to Forecasting
  • 124 - Seasonality
  • 125 - Auto Correlation Function
  • 126 - CASE STUDY Seasonality with ACF
  • 127 - OneHot Encoding
  • 128 - CASE STUDY Seasonality with OneHot Encoding
  • 129 - Linear Trending
  • 130 - CASE STUDY Seasonality with Linear Trend
  • 131 - Smoothing
  • 132 - CASE STUDY Smoothing
  • 133 - NonLinear Trends
  • 134 - CASE STUDY NonLinear Trend
  • 135 - Intervention Analysis
  • 136 - CASE STUDY Intervention Analysis
  • 137 - Looking Ahead to Part 4

  • 16 - PART 4 Unsupervised Learning
  • 138 - Part 4 Unsupervised Learning

  • 17 - Intro to Unsupervised ML
  • 139 - Supervised vs Unsupervised Learning
  • 140 - Common Unsupervised Techniques
  • 141 - Unsupervised ML Workflow
  • 142 - RECAP Feature Engineering
  • 143 - KEY TAKEAWAYS Intro to Unsupervised ML

  • 18 - Clustering Segmentation
  • 144 - Introduction
  • 145 - Clustering Basics
  • 146 - Intro to KMeans
  • 147 - WSS Elbow Plots
  • 148 - KMeans FAQs
  • 149 - CASE STUDY KMeans
  • 150 - Intro to Hierarchical Clustering
  • 151 - Anatomy of a Dendrogram
  • 152 - Hierarchical Clustering FAQs
  • 153 - KEY TAKEAWAYS Clustering Segmentation

  • 19 - Association Mining Basket Analysis
  • 154 - Introduction
  • 155 - Association Mining Basics
  • 156 - The Apriori Algorithm
  • 157 - Basket Analysis Examples
  • 158 - Minimum Support Thresholds
  • 159 - Infrequent Itemsets
  • 160 - Multiple Item Sets
  • 161 - CASE STUDY Apriori
  • 162 - Markov Chains
  • 163 - CASE STUDY Markov Chains
  • 164 - KEY TAKEAWAYS Association Mining

  • 20 - Outlier Detection
  • 165 - Introduction
  • 166 - Outlier Detection Basics
  • 167 - CrossSectional Outliers
  • 168 - CrossSectional Outlier Example
  • 169 - CASE STUDY CrossSectional Outlier
  • 170 - TimeSeries Outliers
  • 171 - TimeSeries Outlier Example
  • 172 - KEY TAKEAWAYS Outlier Detection

  • 21 - Dimensionality Reduction
  • 173 - Introduction
  • 174 - Dimensionality Reduction Basics
  • 175 - Principle Component Analysis
  • 176 - PCA Example
  • 177 - Interpreting Components
  • 178 - Scree Plots
  • 179 - Advanced Techniques
  • 180 - KEY TAKEAWAYS Dimensionality Reduction

  • 22 - Wrapping Up
  • 181 - Series Conclusion
  • 182 - BONUS LESSON.html
  • 139,000 تومان
    بیش از یک محصول به صورت دانلودی میخواهید؟ محصول را به سبد خرید اضافه کنید.
    افزودن به سبد خرید
    خرید دانلودی فوری

    در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

    ایمیل شما:
    تولید کننده:
    شناسه: 12368
    حجم: 3282 مگابایت
    مدت زمان: 528 دقیقه
    تاریخ انتشار: ۲۸ اردیبهشت ۱۴۰۲
    طراحی سایت و خدمات سئو

    139,000 تومان
    افزودن به سبد خرید