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| [CourseClub.ME].url |
122б |
| [FCS Forum].url |
133б |
| [FreeCourseSite.com].url |
127б |
| [GigaCourse.Com].url |
49б |
| 001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML__en.srt |
9.14Кб |
| 001 Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 |
21.73Мб |
| 001 A Practical Example_ What You Will Learn in This Course__en.srt |
6.41Кб |
| 001 A Practical Example_ What You Will Learn in This Course.mp4 |
13.08Мб |
| 001 Are You Sure You're All Set_.html |
513б |
| 001 Basic NN Example (Part 1)__en.srt |
4.52Кб |
| 001 Basic NN Example (Part 1).mp4 |
5.14Мб |
| 001 Bonus Lecture_ Next Steps.html |
2.84Кб |
| 001 Business Case_ Exploring the Dataset and Identifying Predictors__en.srt |
10.50Кб |
| 001 Business Case_ Exploring the Dataset and Identifying Predictors.mp4 |
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| 001 Business Case_ Getting Acquainted with the Dataset__en.srt |
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| 001 Business Case_ Getting Acquainted with the Dataset.mp4 |
60.26Мб |
| 001 Comparison Operators__en.srt |
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| 001 Comparison Operators.mp4 |
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| 001 Data Science and Business Buzzwords_ Why are there so Many___en.srt |
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| 001 Data Science and Business Buzzwords_ Why are there so Many_.mp4 |
54.72Мб |
| 001 Debunking Common Misconceptions__en.srt |
5.43Кб |
| 001 Debunking Common Misconceptions.mp4 |
16.43Мб |
| 001 Defining a Function in Python__en.srt |
2.43Кб |
| 001 Defining a Function in Python.mp4 |
3.23Мб |
| 001 EXERCISE - Age vs Probability.html |
367б |
| 001 Exploring the Problem with a Machine Learning Mindset__en.srt |
4.63Кб |
| 001 Exploring the Problem with a Machine Learning Mindset.mp4 |
11.08Мб |
| 001 Finding the Job - What to Expect and What to Look for__en.srt |
4.34Кб |
| 001 Finding the Job - What to Expect and What to Look for.mp4 |
9.48Мб |
| 001 For Loops__en.srt |
6.69Кб |
| 001 For Loops.mp4 |
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| 001 Fundamentals of Combinatorics__en.srt |
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| 001 Fundamentals of Combinatorics.mp4 |
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| 001 Fundamentals of Probability Distributions__en.srt |
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| 001 Fundamentals of Probability Distributions.mp4 |
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| 001 Game Plan for this Python, SQL, and Tableau Business Exercise__en.srt |
5.54Кб |
| 001 Game Plan for this Python, SQL, and Tableau Business Exercise.mp4 |
15.80Мб |
| 001 How to Install TensorFlow 2.0__en.srt |
6.52Кб |
| 001 How to Install TensorFlow 2.0.mp4 |
27.34Мб |
| 001 Introduction__en.srt |
1.63Кб |
| 001 Introduction.mp4 |
2.93Мб |
| 001 Introduction to Cluster Analysis__en.srt |
4.77Кб |
| 001 Introduction to Cluster Analysis.mp4 |
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| 001 Introduction to Logistic Regression__en.srt |
1.71Кб |
| 001 Introduction to Logistic Regression.mp4 |
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| 001 Introduction to Neural Networks__en.srt |
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| 001 Introduction to Neural Networks.mp4 |
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| 001 Introduction to pandas Series__en.srt |
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| 001 Introduction to pandas Series.mp4 |
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| 001 Introduction to Programming__en.srt |
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| 001 Introduction to Programming.mp4 |
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| 001 Introduction to Regression Analysis__en.srt |
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| 001 Introduction to Regression Analysis.mp4 |
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| 001 K-Means Clustering__en.srt |
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| 001 Lists__en.srt |
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| 001 MNIST_ The Dataset__en.srt |
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| 001 MNIST_ The Dataset.mp4 |
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| 001 Multiple Linear Regression__en.srt |
3.32Кб |
| 001 Multiple Linear Regression.mp4 |
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| 001 Necessary Programming Languages and Software Used in Data Science__en.srt |
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| 001 Necessary Programming Languages and Software Used in Data Science.mp4 |
19.54Мб |
| 001 Null vs Alternative Hypothesis__en.srt |
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| 001 Null vs Alternative Hypothesis.mp4 |
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| 001 Object Oriented Programming__en.srt |
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| 001 Object Oriented Programming.mp4 |
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| 001 Population and Sample__en.srt |
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| 001 Population and Sample.mp4 |
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| 001 Practical Example_ Descriptive Statistics__en.srt |
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| 001 Practical Example_ Descriptive Statistics.mp4 |
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| 001 Practical Example_ Hypothesis Testing__en.srt |
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| 001 Practical Example_ Hypothesis Testing.mp4 |
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| 001 Practical Example_ Inferential Statistics__en.srt |
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| 001 Practical Example_ Inferential Statistics.mp4 |
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| 001 Practical Example_ Linear Regression (Part 1)__en.srt |
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| 001 Practical Example_ Linear Regression (Part 1).mp4 |
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| 001 Preprocessing Introduction__en.srt |
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| 001 Preprocessing Introduction.mp4 |
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| 001 Probability in Finance__en.srt |
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| 001 Probability in Finance.mp4 |
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| 001 READ ME____.html |
564б |
| 001 Sets and Events__en.srt |
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| 001 Sets and Events.mp4 |
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| 001 Stochastic Gradient Descent__en.srt |
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| 001 Stochastic Gradient Descent.mp4 |
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| 001 Summary on What You've Learned__en.srt |
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| 001 Summary on What You've Learned.mp4 |
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| 001 Techniques for Working with Traditional Data__en.srt |
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| 001 Techniques for Working with Traditional Data.mp4 |
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| 001 The Basic Probability Formula__en.srt |
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| 001 The Basic Probability Formula.mp4 |
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| 001 The IF Statement__en.srt |
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| 001 The IF Statement.mp4 |
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| 001 The Linear Regression Model__en.srt |
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| 001 The Linear Regression Model.mp4 |
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| 001 The Reason Behind These Disciplines__en.srt |
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| 001 The Reason Behind These Disciplines.mp4 |
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| 001 Types of Clustering__en.srt |
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| 001 Types of Clustering.mp4 |
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| 001 Types of Data__en.srt |
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| 001 Types of Data.mp4 |
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| 001 Using Arithmetic Operators in Python__en.srt |
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| 001 Using Arithmetic Operators in Python.mp4 |
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| 001 Using the .format() Method__en.srt |
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| 001 Using the .format() Method.mp4 |
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| 001 Variables__en.srt |
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| 001 Variables.mp4 |
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| 001 What are Confidence Intervals___en.srt |
3.29Кб |
| 001 What are Confidence Intervals_.mp4 |
28.38Мб |
| 001 What are Data, Servers, Clients, Requests, and Responses__en.srt |
5.92Кб |
| 001 What are Data, Servers, Clients, Requests, and Responses.mp4 |
19.17Мб |
| 001 What is a Layer___en.srt |
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| 001 What is a Layer_.mp4 |
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| 001 What is a Matrix___en.srt |
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| 001 What is a Matrix_.mp4 |
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| 001 What is Initialization___en.srt |
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| 001 What is Initialization_.mp4 |
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| 001 What is Overfitting___en.srt |
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| 001 What is Overfitting_.mp4 |
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| 001 What is sklearn and How is it Different from Other Packages__en.srt |
3.38Кб |
| 001 What is sklearn and How is it Different from Other Packages.mp4 |
6.24Мб |
| 001 What to Expect from the Following Sections_.html |
2.43Кб |
| 001 What to Expect from this Part___en.srt |
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| 001 What to Expect from this Part_.mp4 |
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| 002 Adjusted R-Squared__en.srt |
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| 002 Adjusted R-Squared.mp4 |
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| 002 Analyzing Age vs Probability in Tableau__en.srt |
10.22Кб |
| 002 Analyzing Age vs Probability in Tableau.mp4 |
38.69Мб |
| 002 A Simple Example in Python__en.srt |
5.91Кб |
| 002 A Simple Example in Python.mp4 |
21.91Мб |
| 002 A Simple Example of Clustering__en.srt |
902б |
| 002 A Simple Example of Clustering_en.vtt |
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| 002 A Simple Example of Clustering.mp4 |
26.08Мб |
| 002 Basic NN Example (Part 2)__en.srt |
6.90Кб |
| 002 Basic NN Example (Part 2).mp4 |
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| 002 Business Case_ Outlining the Solution__en.srt |
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| 002 Business Case_ Outlining the Solution__en.srt |
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| 002 Business Case_ Outlining the Solution.mp4 |
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| 002 Business Case_ Outlining the Solution.mp4 |
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| 002 Computing Expected Values__en.srt |
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| 002 Computing Expected Values.mp4 |
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| 002 Confidence Intervals; Population Variance Known; Z-score__en.srt |
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| 002 Confidence Intervals; Population Variance Known; Z-score.mp4 |
52.21Мб |
| 002 Correlation vs Regression__en.srt |
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| 002 Correlation vs Regression.mp4 |
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| 002 Creating the Targets for the Logistic Regression__en.srt |
8.56Кб |
| 002 Creating the Targets for the Logistic Regression.mp4 |
32.50Мб |
| 002 Dendrogram__en.srt |
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| 002 Dendrogram.mp4 |
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| 002 Deploying the 'absenteeism_module' - Part I__en.srt |
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| 002 Deploying the 'absenteeism_module' - Part I.mp4 |
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| 002 Further Reading on Null and Alternative Hypothesis.html |
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| 002 How are we Going to Approach this Section___en.srt |
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| 002 How are we Going to Approach this Section__en.vtt |
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| 002 How are we Going to Approach this Section_.mp4 |
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| 002 How to Create a Function with a Parameter__en.srt |
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| 002 How to Create a Function with a Parameter.mp4 |
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| 002 How to Install TensorFlow 1__en.srt |
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| 002 How to Install TensorFlow 1_en.vtt |
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| 002 How to Install TensorFlow 1.mp4 |
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| 002 Importing the Absenteeism Data in Python__en.srt |
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| 002 Importing the Absenteeism Data in Python.mp4 |
18.03Мб |
| 002 Iterating Over Range Objects__en.srt |
6.04Кб |
| 002 Iterating Over Range Objects.mp4 |
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| 002 Levels of Measurement__en.srt |
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| 002 Levels of Measurement.mp4 |
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| 002 Logical and Identity Operators__en.srt |
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| 002 Logical and Identity Operators.mp4 |
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| 002 MNIST_ How to Tackle the MNIST__en.srt |
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| 002 MNIST_ How to Tackle the MNIST__en.srt |
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| 002 MNIST_ How to Tackle the MNIST.mp4 |
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| 002 MNIST_ How to Tackle the MNIST.mp4 |
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| 002 Modules and Packages__en.srt |
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| 002 Modules and Packages.mp4 |
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| 002 Numbers and Boolean Values in Python__en.srt |
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| 002 Numbers and Boolean Values in Python.mp4 |
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| 002 Permutations and How to Use Them__en.srt |
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| 002 Permutations and How to Use Them.mp4 |
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| 002 Practical Example_ Descriptive Statistics Exercise.html |
81б |
| 002 Practical Example_ Hypothesis Testing Exercise.html |
81б |
| 002 Practical Example_ Inferential Statistics Exercise.html |
81б |
| 002 Practical Example_ Linear Regression (Part 2)__en.srt |
5.00Кб |
| 002 Practical Example_ Linear Regression (Part 2)_en.vtt |
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| 002 Practical Example_ Linear Regression (Part 2).mp4 |
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| 002 Probability in Statistics__en.srt |
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| 002 Probability in Statistics.mp4 |
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| 002 Problems with Gradient Descent__en.srt |
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| 002 Problems with Gradient Descent.mp4 |
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| 002 Real Life Examples of Traditional Data__en.srt |
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| 002 Real Life Examples of Traditional Data.mp4 |
13.92Мб |
| 002 Scalars and Vectors__en.srt |
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| 002 Scalars and Vectors.mp4 |
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| 002 Some Examples of Clusters__en.srt |
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| 002 Some Examples of Clusters.mp4 |
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| 002 TensorFlow Outline and Comparison with Other Libraries__en.srt |
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| 002 TensorFlow Outline and Comparison with Other Libraries_en.vtt |
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| 002 TensorFlow Outline and Comparison with Other Libraries.mp4 |
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| 002 The Business Task__en.srt |
3.72Кб |
| 002 The Business Task.mp4 |
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| 002 The Double Equality Sign__en.srt |
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| 002 The Double Equality Sign.mp4 |
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| 002 The ELSE Statement__en.srt |
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| 002 The ELSE Statement.mp4 |
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| 002 Training the Model__en.srt |
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| 002 Training the Model.mp4 |
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| 002 Types of Basic Preprocessing__en.srt |
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| 002 Types of Basic Preprocessing.mp4 |
2.40Мб |
| 002 Types of Probability Distributions__en.srt |
9.72Кб |
| 002 Types of Probability Distributions.mp4 |
28.69Мб |
| 002 Types of Simple Initializations__en.srt |
3.82Кб |
| 002 Types of Simple Initializations.mp4 |
5.73Мб |
| 002 Underfitting and Overfitting for Classification__en.srt |
2.69Кб |
| 002 Underfitting and Overfitting for Classification.mp4 |
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| 002 Using Methods__en.srt |
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| 002 Using Methods.mp4 |
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| 002 Ways Sets Can Interact__en.srt |
4.40Кб |
| 002 Ways Sets Can Interact.mp4 |
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| 002 What's Further out there in terms of Machine Learning__en.srt |
2.64Кб |
| 002 What's Further out there in terms of Machine Learning.mp4 |
3.71Мб |
| 002 What are Data Connectivity, APIs, and Endpoints___en.srt |
8.51Кб |
| 002 What are Data Connectivity, APIs, and Endpoints_.mp4 |
58.83Мб |
| 002 What Does the Course Cover__en.srt |
5.10Кб |
| 002 What Does the Course Cover.mp4 |
49.69Мб |
| 002 What is a Deep Net___en.srt |
3.30Кб |
| 002 What is a Deep Net_.mp4 |
11.06Мб |
| 002 What is a Distribution__en.srt |
6.13Кб |
| 002 What is a Distribution.mp4 |
16.90Мб |
| 002 What is the difference between Analysis and Analytics__en.srt |
5.03Кб |
| 002 What is the difference between Analysis and Analytics.mp4 |
8.01Мб |
| 002 While Loops and Incrementing__en.srt |
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| 002 While Loops and Incrementing.mp4 |
20.20Мб |
| 002 Why Python___en.srt |
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| 002 Why Python_.mp4 |
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| 002 Working with Methods in Python - Part I__en.srt |
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| 002 Working with Methods in Python - Part I.mp4 |
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| 003 A Note on Installing Packages in Anaconda.html |
2.28Кб |
| 003 A Note on Multicollinearity.html |
849б |
| 003 A Simple Example of Clustering - Exercise.html |
87б |
| 003 Basic NN Example (Part 3)__en.srt |
4.39Кб |
| 003 Basic NN Example (Part 3).mp4 |
15.68Мб |
| 003 Business Analytics, Data Analytics, and Data Science_ An Introduction__en.srt |
11.00Кб |
| 003 Business Analytics, Data Analytics, and Data Science_ An Introduction.mp4 |
49.96Мб |
| 003 Business Case_ Balancing the Dataset__en.srt |
4.72Кб |
| 003 Business Case_ Balancing the Dataset.mp4 |
26.19Мб |
| 003 Categorical Variables - Visualization Techniques__en.srt |
6.44Кб |
| 003 Categorical Variables - Visualization Techniques.mp4 |
36.65Мб |
| 003 Characteristics of Discrete Distributions__en.srt |
2.51Кб |
| 003 Characteristics of Discrete Distributions.mp4 |
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| 003 Checking the Content of the Data Set__en.srt |
7.12Кб |
| 003 Checking the Content of the Data Set.mp4 |
54.27Мб |
| 003 Confidence Intervals; Population Variance Known; Z-score; Exercise.html |
81б |
| 003 DeepMind and Deep Learning.html |
1.04Кб |
| 003 Defining a Function in Python - Part II__en.srt |
2.88Кб |
| 003 Defining a Function in Python - Part II.mp4 |
6.45Мб |
| 003 Deploying the 'absenteeism_module' - Part II__en.srt |
429б |
| 003 Deploying the 'absenteeism_module' - Part II_en.vtt |
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| 003 Deploying the 'absenteeism_module' - Part II.mp4 |
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| 003 Difference between Classification and Clustering__en.srt |
3.34Кб |
| 003 Difference between Classification and Clustering.mp4 |
9.53Мб |
| 003 Digging into a Deep Net__en.srt |
6.73Кб |
| 003 Digging into a Deep Net.mp4 |
19.14Мб |
| 003 Download All Resources and Important FAQ.html |
21.36Кб |
| 003 EXERCISE - Reasons vs Probability.html |
385б |
| 003 Frequency__en.srt |
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| 003 Frequency.mp4 |
36.39Мб |
| 003 Geometrical Representation of the Linear Regression Model__en.srt |
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| 003 Geometrical Representation of the Linear Regression Model.mp4 |
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| 003 Heatmaps__en.srt |
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| 003 Heatmaps.mp4 |
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| 003 How to Reassign Values__en.srt |
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| 003 How to Reassign Values.mp4 |
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| 003 Intersection of Sets__en.srt |
2.49Кб |
| 003 Intersection of Sets.mp4 |
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| 003 Introducing the Data Set__en.srt |
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| 003 Introducing the Data Set.mp4 |
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| 003 Introduction to Nested For Loops__en.srt |
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| 003 Introduction to Nested For Loops.mp4 |
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| 003 Linear Algebra and Geometry__en.srt |
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| 003 Linear Algebra and Geometry.mp4 |
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| 003 List Slicing__en.srt |
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| 003 List Slicing.mp4 |
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| 003 Lists with the range() Function__en.srt |
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| 003 Lists with the range() Function.mp4 |
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| 003 Logistic vs Logit Function__en.srt |
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| 003 Logistic vs Logit Function.mp4 |
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| 003 MNIST_ Importing the Relevant Packages and Loading the Data__en.srt |
3.01Кб |
| 003 MNIST_ Importing the Relevant Packages and Loading the Data.mp4 |
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| 003 MNIST_ Relevant Packages__en.srt |
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| 003 Momentum__en.srt |
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| 003 Momentum.mp4 |
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| 003 Multiple Linear Regression Exercise.html |
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| 003 Probability in Data Science__en.srt |
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| 003 Probability in Data Science.mp4 |
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| 003 Python Strings__en.srt |
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| 003 Python Strings.mp4 |
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| 003 Rejection Region and Significance Level__en.srt |
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| 003 Rejection Region and Significance Level.mp4 |
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| 003 Selecting the Inputs for the Logistic Regression__en.srt |
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| 003 Selecting the Inputs for the Logistic Regression.mp4 |
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| 003 Simple Linear Regression with sklearn__en.srt |
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| 003 Simple Operations with Factorials__en.srt |
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| 003 Standardization__en.srt |
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| 003 State-of-the-Art Method - (Xavier) Glorot Initialization__en.srt |
3.67Кб |
| 003 State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 |
4.18Мб |
| 003 Taking a Closer Look at APIs__en.srt |
10.60Кб |
| 003 Taking a Closer Look at APIs.mp4 |
65.29Мб |
| 003 Techniques for Working with Big Data__en.srt |
5.74Кб |
| 003 Techniques for Working with Big Data.mp4 |
60.48Мб |
| 003 TensorFlow 1 vs TensorFlow 2__en.srt |
3.76Кб |
| 003 TensorFlow 1 vs TensorFlow 2.mp4 |
14.95Мб |
| 003 The ELIF Statement__en.srt |
6.60Кб |
| 003 The ELIF Statement.mp4 |
14.25Мб |
| 003 The Importance of Working with a Balanced Dataset__en.srt |
4.72Кб |
| 003 The Importance of Working with a Balanced Dataset.mp4 |
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| 003 The Normal Distribution__en.srt |
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| 003 Types of Machine Learning__en.srt |
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| 003 Types of Machine Learning.mp4 |
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| 003 What is the Standard Library___en.srt |
3.66Кб |
| 003 What is the Standard Library_.mp4 |
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| 003 What is Validation___en.srt |
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| 003 What is Validation_.mp4 |
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| 003 Why Jupyter___en.srt |
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