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| [FCS Forum].url |
133б |
| [FreeCourseSite.com].url |
127б |
| 1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx |
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| 1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx |
1.74Мб |
| 1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx |
51.71Кб |
| 1.1 5 Files Needed to Deploy the Model.html |
134б |
| 1.1 Absenteeism_data.csv.csv |
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| 1.1 Absenteeism_preprocessed.csv.csv |
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| 1.1 Arithmetic Operators - Resources.html |
134б |
| 1.1 Audiobooks_data.csv.csv |
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| 1.1 Comparison Operators - Resources.html |
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| 1.1 Course notes_descriptive_statistics.pdf.pdf |
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| 1.1 Course notes_hypothesis_testing.pdf.pdf |
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| 1.1 Course notes_inferential statistics.pdf.pdf |
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| 1.1 Course Notes - Basic Probability.pdf.pdf |
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| 1.1 Course Notes - Bayesian Inference.pdf.pdf |
386.01Кб |
| 1.1 Course Notes - Combinatorics.pdf.pdf |
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| 1.1 Course Notes - Probability Distributions.pdf.pdf |
456.24Кб |
| 1.1 Course Notes - Section 2.pdf.pdf |
578.08Кб |
| 1.1 Course Notes - Section 6.pdf.pdf |
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| 1.1 Defining a Function in Python - Resources.html |
134б |
| 1.1 For Loops - Resources.html |
134б |
| 1.1 Introduction to the If Statement - Resources.html |
134б |
| 1.1 Lists - Resources.html |
134б |
| 1.1 Shortcuts-for-Jupyter.pdf.pdf |
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| 1.1 Shortcuts-for-Jupyter.pdf.pdf |
619.17Кб |
| 1.1 sklearn - Linear Regression - Practical Example (Part 1).html |
134б |
| 1.1 Statistics Glossary.xlsx.xlsx |
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| 1.1 Variables - Resources.html |
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| 1.2 Bais NN Example Part 1.html |
136б |
| 1.2 Course notes_descriptive_statistics.pdf.pdf |
482.21Кб |
| 1.2 df_preprocessed.csv.csv |
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| 1.2 Python Introduction - Course Notes.pdf.pdf |
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| 1.3 data_preprocessing_homework.pdf.pdf |
134.47Кб |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
126.87Мб |
| 1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt |
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| 1. A Practical Example What You Will Learn in This Course.mp4 |
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| 1. A Practical Example What You Will Learn in This Course.vtt |
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| 1. Are You Sure You're All Set.html |
519б |
| 1. Basic NN Example (Part 1).mp4 |
20.60Мб |
| 1. Basic NN Example (Part 1).vtt |
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| 1. Business Case Getting acquainted with the dataset.mp4 |
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| 1. Comparison Operators.mp4 |
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| 1. Data Science and Business Buzzwords Why are there so many.mp4 |
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| 1. Data Science and Business Buzzwords Why are there so many.vtt |
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| 1. Debunking Common Misconceptions.mp4 |
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| 1. Debunking Common Misconceptions.vtt |
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| 1. Defining a Function in Python.mp4 |
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| 1. Defining a Function in Python.vtt |
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| 1. EXERCISE - Age vs Probability.html |
385б |
| 1. Exploring the Problem with a Machine Learning Mindset.mp4 |
27.54Мб |
| 1. Exploring the Problem with a Machine Learning Mindset.vtt |
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| 1. Finding the Job - What to Expect and What to Look for.mp4 |
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| 1. Finding the Job - What to Expect and What to Look for.vtt |
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| 1. For Loops.mp4 |
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| 1. For Loops.vtt |
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| 1. Fundamentals of Combinatorics.mp4 |
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| 1. Fundamentals of Probability Distributions.mp4 |
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| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 |
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| 1. Game Plan for this Python, SQL, and Tableau Business Exercise.vtt |
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| 1. How to Install TensorFlow.mp4 |
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| 1. How to Install TensorFlow.vtt |
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| 1. Introduction.mp4 |
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| 1. Introduction.vtt |
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| 1. Introduction to Cluster Analysis.mp4 |
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| 1. Introduction to Logistic Regression.mp4 |
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| 1. Introduction to Neural Networks.mp4 |
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| 1. Introduction to Programming.mp4 |
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| 1. Introduction to Regression Analysis.mp4 |
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| 1. Introduction to Regression Analysis.vtt |
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| 1. K-Means Clustering.mp4 |
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| 1. K-Means Clustering.vtt |
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| 1. Lists.mp4 |
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| 1. Lists.vtt |
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| 1. MNIST What is the MNIST Dataset.mp4 |
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| 1. MNIST What is the MNIST Dataset.vtt |
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| 1. Multiple Linear Regression.mp4 |
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| 1. Multiple Linear Regression.vtt |
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| 1. Necessary Programming Languages and Software Used in Data Science.mp4 |
103.51Мб |
| 1. Necessary Programming Languages and Software Used in Data Science.vtt |
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| 1. Null vs Alternative Hypothesis.mp4 |
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| 1. Object Oriented Programming.mp4 |
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| 1. Population and Sample.mp4 |
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| 1. Practical Example Descriptive Statistics.mp4 |
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| 1. Practical Example Hypothesis Testing.mp4 |
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| 1. Practical Example Inferential Statistics.mp4 |
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| 1. Practical Example Linear Regression (Part 1).mp4 |
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| 1. Preprocessing Introduction.mp4 |
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| 1. Probability in Finance.mp4 |
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| 1. Probability in Finance.vtt |
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| 1. Sets and Events.mp4 |
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| 1. Stochastic Gradient Descent.mp4 |
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| 1. Summary on What You've Learned.mp4 |
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| 1. Summary on What You've Learned.vtt |
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| 1. Techniques for Working with Traditional Data.mp4 |
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| 1. Techniques for Working with Traditional Data.vtt |
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| 1. The Basic Probability Formula.mp4 |
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| 1. The Basic Probability Formula.vtt |
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| 1. The IF Statement.mp4 |
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| 1. The IF Statement.vtt |
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| 1. The Linear Regression Model.mp4 |
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| 1. The Linear Regression Model.vtt |
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| 1. The Reason behind these Disciplines.mp4 |
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| 1. The Reason behind these Disciplines.vtt |
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| 1. Types of Clustering.mp4 |
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| 1. Types of Clustering.vtt |
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| 1. Types of Data.mp4 |
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| 1. Types of Data.vtt |
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| 1. Using Arithmetic Operators in Python.mp4 |
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| 1. Using Arithmetic Operators in Python.vtt |
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| 1. Variables.mp4 |
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| 1. Variables.vtt |
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| 1. What are Confidence Intervals.mp4 |
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| 1. What are Confidence Intervals.vtt |
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| 1. What are Data, Servers, Clients, Requests, and Responses.mp4 |
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| 1. What are Data, Servers, Clients, Requests, and Responses.vtt |
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| 1. What is a Layer.mp4 |
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| 1. What is a Layer.vtt |
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| 1. What is a matrix.mp4 |
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| 1. What is a matrix.vtt |
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| 1. What is Initialization.mp4 |
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| 1. What is Initialization.vtt |
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| 1. What is Overfitting.mp4 |
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| 1. What is Overfitting.vtt |
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| 1. What is sklearn and How is it Different from Other Packages.mp4 |
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| 1. What is sklearn and How is it Different from Other Packages.vtt |
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| 1. What to Expect from the Following Sections.html |
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| 1. What to Expect from this Part.mp4 |
31.11Мб |
| 1. What to Expect from this Part.vtt |
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| 10.1 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx |
13.15Кб |
| 10.1 Addition and Subtraction of Matrices Python Notebook.html |
178б |
| 10.1 Binary predictors.html |
134б |
| 10.1 Feature selection.html |
134б |
| 10.1 Indexing Elements - Resources.html |
134б |
| 10.1 Online p-value calculator.pdf.pdf |
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| 10.1 TensorFlow MNIST All Exercises.html |
144б |
| 10.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx |
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| 10. A1 Linearity.html |
158б |
| 10. A Breakdown of our Data Science Infographic.html |
158б |
| 10. Addition and Subtraction of Matrices.mp4 |
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| 10. Addition and Subtraction of Matrices.vtt |
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| 10. Analyzing the Reasons for Absence.mp4 |
40.58Мб |
| 10. Analyzing the Reasons for Absence.vtt |
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| 10. Binary Predictors in a Logistic Regression.mp4 |
38.44Мб |
| 10. Binary Predictors in a Logistic Regression.vtt |
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| 10. Business Case Testing the Model.mp4 |
11.20Мб |
| 10. Business Case Testing the Model.vtt |
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| 10. Central Limit Theorem.html |
158б |
| 10. Discrete Distributions The Bernoulli Distribution.html |
158б |
| 10. Feature Selection (F-regression).mp4 |
29.52Мб |
| 10. Feature Selection (F-regression).vtt |
5.85Кб |
| 10. Indexing Elements.mp4 |
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| 10. Indexing Elements.vtt |
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| 10. Interpreting the Coefficients of the Logistic Regression.mp4 |
40.41Мб |
| 10. Interpreting the Coefficients of the Logistic Regression.vtt |
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| 10. Jupyter's Interface.html |
158б |
| 10. Margin of Error.mp4 |
59.17Мб |
| 10. Margin of Error.vtt |
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| 10. MNIST Exercises.html |
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| 10. Mutually Exclusive Sets.html |
158б |
| 10. Numerical Variables Exercise.html |
81б |
| 10. p-value.mp4 |
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| 10. p-value.vtt |
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| 10. Relationship between Clustering and Regression.mp4 |
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| 10. Relationship between Clustering and Regression.vtt |
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| 10. Software Integration - Explained.html |
158б |
| 10. Solving Variations without Repetition.html |
158б |
| 10. Techniques for Working with Traditional Methods.mp4 |
123.51Мб |
| 10. Techniques for Working with Traditional Methods.vtt |
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| 10. The Linear Model with Multiple Inputs.html |
158б |
| 10. Using Seaborn for Graphs.mp4 |
12.24Мб |
| 10. Using Seaborn for Graphs.vtt |
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| 11.10 TensorFlow MNIST 'Around 98% Accuracy' Solution.html |
157б |
| 11.11 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html |
162б |
| 11.1 2.5. The Histogram_lesson.xlsx.xlsx |
18.63Кб |
| 11.1 Bank_data.csv.csv |
19.55Кб |
| 11.1 Calculation of P-values.html |
134б |
| 11.1 Combinations With Repetition.pdf.pdf |
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| 11.1 Logistic Regression prior to Backward Elimination.html |
226б |
| 11.1 Market segmentation.html |
134б |
| 11.1 Python Introduction - Course Notes.pdf.pdf |
2.03Мб |
| 11.1 TensorFlow Business Case Homework.html |
134б |
| 11.1 TensorFlow MNIST '1. Width' Solution.html |
150б |
| 11.2 Binary predictors - exercise.html |
134б |
| 11.2 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html |
172б |
| 11.3 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html |
162б |
| 11.4 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html |
165б |
| 11.5 TensorFlow MNIST 'Time' Solution.html |
162б |
| 11.6 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html |
172б |
| 11.7 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html |
165б |
| 11.8 TensorFlow MNIST '3. Width and Depth' Solution.html |
160б |
| 11.9 TensorFlow MNIST '2. Depth' Solution.html |
150б |
| 11. A2 No Endogeneity.mp4 |
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| 11. A2 No Endogeneity.vtt |
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| 11. Addition and Subtraction of Matrices.html |
158б |
| 11. A Note on Calculation of P-values with sklearn.html |
372б |
| 11. Backward Elimination or How to Simplify Your Model.mp4 |
39.56Мб |
| 11. Backward Elimination or How to Simplify Your Model.vtt |
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| 11. Binary Predictors in a Logistic Regression - Exercise.html |
87б |
| 11. Business Case A Comment on the Homework.mp4 |
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| 11. Business Case A Comment on the Homework.vtt |
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| 11. Dependence and Independence of Sets.mp4 |
34.78Мб |
| 11. Dependence and Independence of Sets.vtt |
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| 11. Discrete Distributions The Binomial Distribution.mp4 |
65.51Мб |
| 11. Discrete Distributions The Binomial Distribution.vtt |
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| 11. How to Interpret the Regression Table.mp4 |
44.65Мб |
| 11. How to Interpret the Regression Table.vtt |
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| 11. Indexing Elements.html |
158б |
| 11. Margin of Error.html |
158б |
| 11. Market Segmentation with Cluster Analysis (Part 1).mp4 |
43.01Мб |
| 11. Market Segmentation with Cluster Analysis (Part 1).vtt |
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| 11. MNIST Solutions.html |
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| 11. Obtaining Dummies from a Single Feature.mp4 |
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| 11. Obtaining Dummies from a Single Feature.vtt |
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| 11. p-value.html |
158б |
| 11. Python 2 vs Python 3.mp4 |
11.27Мб |
| 11. Python 2 vs Python 3.vtt |
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| 11. Solving Combinations.mp4 |
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| 11. Solving Combinations.vtt |
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| 11. Standard error.mp4 |
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| 11. Standard error.vtt |
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| 11. Techniques for Working with Traditional Methods.html |
158б |
| 11. The Histogram.mp4 |
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| 11. The Histogram.vtt |
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| 11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 |
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| 11. The Linear model with Multiple Inputs and Multiple Outputs.vtt |
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| 12.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx |
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| 12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx |
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| 12.1 Accuracy.html |
134б |
| 12.1 Errors when Adding Matrices Python Notebook.html |
220б |
| 12.1 Market segmentation.html |
134б |
| 12.1 Structure Your Code with Indentation - Resources.html |
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| 12.1 Summary table with p-values.html |
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| 12.1 TensorFlow Business Case Homework.html |
134б |
| 12. A2 No Endogeneity.html |
158б |
| 12. Business Case Final Exercise.html |
439б |
| 12. Calculating the Accuracy of the Model.mp4 |
32.85Мб |
| 12. Calculating the Accuracy of the Model.vtt |
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| 12. Confidence intervals. Two means. Dependent samples.mp4 |
70.47Мб |
| 12. Confidence intervals. Two means. Dependent samples.vtt |
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| 12. Creating a Summary Table with p-values.mp4 |
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| 12. Creating a Summary Table with p-values.vtt |
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| 12. Dependence and Independence of Sets.html |
158б |
| 12. Discrete Distributions The Binomial Distribution.html |
158б |
| 12. Errors when Adding Matrices.mp4 |
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| 12. Errors when Adding Matrices.vtt |
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| 12. EXERCISE - Obtaining Dummies from a Single Feature.html |
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| 12. How to Interpret the Regression Table.html |
158б |
| 12. Market Segmentation with Cluster Analysis (Part 2).mp4 |
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| 12. Market Segmentation with Cluster Analysis (Part 2).vtt |
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| 12. Real Life Examples of Traditional Methods.mp4 |
42.78Мб |
| 12. Real Life Examples of Traditional Methods.vtt |
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| 12. Solving Combinations.html |
158б |
| 12. Standard Error.html |
158б |
| 12. Structuring with Indentation.mp4 |
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| 12. Structuring with Indentation.vtt |
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| 12. Test for the Mean. Population Variance Unknown.mp4 |
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| 12. Test for the Mean. Population Variance Unknown.vtt |
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| 12. Testing the Model We Created.mp4 |
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| 12. Testing the Model We Created.vtt |
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| 12. The Histogram.html |
158б |
| 12. The Linear model with Multiple Inputs and Multiple Outputs.html |
158б |
| 13.1 2.5.The-Histogram-exercise-solution.xlsx.xlsx |
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| 13.1 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx |
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| 13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx |
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| 13.1 Accuracy of the model - exercise.html |
134б |
| 13.1 Multiple linear regression - Exercise.html |
134б |
| 13.1 Symmetry Explained.pdf.pdf |
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| 13.1 Transpose of a Matrix Python Notebook.html |
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| 13.2 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx |
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| 13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx |
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| 13.2 Bank_data.csv.csv |
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| 13.2 Statistics - PDF with Excel Solutions that don't visualize properly.pdf.pdf |
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| 13.3 2.5.The-Histogram-exercise.xlsx.xlsx |
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| 13. A3 Normality and Homoscedasticity.mp4 |
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| 13. Calculating the Accuracy of the Model.html |
87б |
| 13. Confidence intervals. Two means. Dependent samples Exercise.html |
81б |
| 13. Decomposition of Variability.mp4 |
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| 13. Decomposition of Variability.vtt |
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| 13. Discrete Distributions The Poisson Distribution.mp4 |
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| 13. Discrete Distributions The Poisson Distribution.vtt |
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| 13. Estimators and Estimates.mp4 |
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| 13. Estimators and Estimates.vtt |
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| 13. Graphical Representation of Simple Neural Networks.mp4 |
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| 13. Histogram Exercise.html |
81б |
| 13. How is Clustering Useful.mp4 |
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| 13. How is Clustering Useful.vtt |
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| 13. Machine Learning (ML) Techniques.mp4 |
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| 13. Machine Learning (ML) Techniques.vtt |
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| 13. Multiple Linear Regression - Exercise.html |
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| 13. Saving the Model and Preparing it for Deployment.mp4 |
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| 13. Saving the Model and Preparing it for Deployment.vtt |
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| 13. SOLUTION - Obtaining Dummies from a Single Feature.html |
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| 13. Structuring with Indentation.html |
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| 13. Symmetry of Combinations.mp4 |
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| 13. Test for the Mean. Population Variance Unknown Exercise.html |
81б |
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| 13. Transpose of a Matrix.mp4 |
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| 14.1 2.6. Cross table and scatter plot.xlsx.xlsx |
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| 14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx |
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| 14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx |
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| 14.1 Dot Product Python Notebook.html |
154б |
| 14.1 Exercise - part 1.html |
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| 14.1 Feature scaling.html |
134б |
| 14.2 iris_dataset.csv.csv |
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| 14. A4 No Autocorrelation.mp4 |
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| 14. ARTICLE - A Note on 'pickling'.html |
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| 14. Confidence intervals. Two means. Independent samples (Part 1).mp4 |
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| 14. Confidence intervals. Two means. Independent samples (Part 1).vtt |
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| 14. Cross Tables and Scatter Plots.mp4 |
39.80Мб |
| 14. Cross Tables and Scatter Plots.vtt |
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| 14. Decomposition of Variability.html |
158б |
| 14. Discrete Distributions The Poisson Distribution.html |
158б |
| 14. Dot Product.mp4 |
24.00Мб |
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| 14. Dropping a Dummy Variable from the Data Set.html |
2.34Кб |
| 14. Estimators and Estimates.html |
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| 7. Backpropagation.mp4 |
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| 7. Built-in Functions in Python.mp4 |
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| 7. Conditional Statements, Functions, and Loops.mp4 |
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| 7. Continuing with BI, ML, and AI.mp4 |
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