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### The Data Science Course 2019: Complete Data Science Bootcamp

What you’ll learn

The course provides the entire toolbox you need to become a data scientist

Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow

Impress interviewers by showing an understanding of the data science field

Learn how to pre-process data

Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)

Start coding in Python and learn how to use it for statistical analysis

Perform linear and logistic regressions in Python

Carry out cluster and factor analysis

Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn

Apply your skills to real-life business cases

Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data

Unfold the power of deep neural networks

Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance

Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Get The Data Science Course 2019: Complete Data Science Bootcamp download

Course content

Expand all 471 lectures28:52:43

-Part 1: Introduction

19:20

A Practical Example: What You Will Learn in This Course

Preview

05:05

What Does the Course Cover

Preview

03:34

Download All Resources and Important FAQ

10:41

-The Field of Data Science – The Various Data Science Disciplines

31:11

Data Science and Business Buzzwords: Why are there so many?

Preview

05:21

Data Science and Business Buzzwords: Why are there so many?

1 question

What is the difference between Analysis and Analytics

03:50

What is the difference between Analysis and Analytics

1 question

Business Analytics, Data Analytics, and Data Science: An Introduction

Preview

08:26

Business Analytics, Data Analytics, and Data Science: An Introduction

3 questions

Continuing with BI, ML, and AI

09:31

Continuing with BI, ML, and AI

2 questions

A Breakdown of our Data Science Infographic

04:03

A Breakdown of our Data Science Infographic

1 question

-The Field of Data Science – Connecting the Data Science Disciplines

07:19

Applying Traditional Data, Big Data, BI, Traditional Data Science and ML

07:19

Applying Traditional Data, Big Data, BI, Traditional Data Science and ML

1 question

-The Field of Data Science – The Benefits of Each Discipline

04:44

The Reason behind these Disciplines

04:44

The Reason behind these Disciplines

1 question

-The Field of Data Science – Popular Data Science Techniques

53:34

Techniques for Working with Traditional Data

08:13

Techniques for Working with Traditional Data

1 question

Real Life Examples of Traditional Data

01:44

Techniques for Working with Big Data

04:26

Techniques for Working with Big Data

1 question

Real Life Examples of Big Data

01:32

Business Intelligence (BI) Techniques

06:45

Business Intelligence (BI) Techniques

4 questions

Real Life Examples of Business Intelligence (BI)

01:42

Techniques for Working with Traditional Methods

09:08

Techniques for Working with Traditional Methods

4 questions

Real Life Examples of Traditional Methods

02:45

Machine Learning (ML) Techniques

06:55

Machine Learning (ML) Techniques

2 questions

Types of Machine Learning

08:13

Types of Machine Learning

2 questions

Real Life Examples of Machine Learning (ML)

02:11

Real Life Examples of Machine Learning (ML)

5 questions

-The Field of Data Science – Popular Data Science Tools

05:51

Necessary Programming Languages and Software Used in Data Science

05:51

Necessary Programming Languages and Software Used in Data Science

4 questions

-The Field of Data Science – Careers in Data Science

03:29

Finding the Job – What to Expect and What to Look for

03:29

Finding the Job – What to Expect and What to Look for

1 question

-The Field of Data Science – Debunking Common Misconceptions

04:10

Debunking Common Misconceptions

04:10

Debunking Common Misconceptions

1 question

-Part 2: Probability

23:04

The Basic Probability Formula

07:09

The Basic Probability Formula

3 questions

Computing Expected Values

05:29

Computing Expected Values

3 questions

Frequency

05:00

Frequency

3 questions

Events and Their Complements

05:26

Events and Their Complements

3 questions

-Probability – Combinatorics

42:56

Fundamentals of Combinatorics

01:04

Fundamentals of Combinatorics

1 question

Permutations and How to Use Them

03:21

Permutations and How to Use Them

2 questions

Simple Operations with Factorials

03:35

Simple Operations with Factorials

3 questions

Solving Variations with Repetition

02:59

Solving Variations with Repetition

3 questions

Solving Variations without Repetition

03:48

Solving Variations without Repetition

3 questions

Solving Combinations

04:51

Solving Combinations

4 questions

Symmetry of Combinations

03:26

Symmetry of Combinations

1 question

Solving Combinations with Separate Sample Spaces

02:52

Solving Combinations with Separate Sample Spaces

1 question

Combinatorics in Real-Life: The Lottery

03:12

Combinatorics in Real-Life: The Lottery

1 question

A Recap of Combinatorics

02:55

A Practical Example of Combinatorics

10:53

-Probability – Bayesian Inference

54:38

Sets and Events

04:25

Sets and Events

3 questions

Ways Sets Can Interact

03:45

Ways Sets Can Interact

2 questions

Intersection of Sets

02:06

Intersection of Sets

3 questions

Union of Sets

04:51

Union of Sets

3 questions

Mutually Exclusive Sets

02:09

Mutually Exclusive Sets

4 questions

Dependence and Independence of Sets

03:01

Dependence and Independence of Sets

3 questions

The Conditional Probability Formula

04:16

The Conditional Probability Formula

3 questions

The Law of Total Probability

03:03

The Additive Rule

02:21

The Additive Rule

2 questions

The Multiplication Law

04:05

The Multiplication Law

2 questions

Bayes’ Law

05:44

Bayes’ Law

2 questions

A Practical Example of Bayesian Inference

14:52

-Probability – Distributions

01:17:12

Fundamentals of Probability Distributions

06:29

Fundamentals of Probability Distributions

3 questions

Types of Probability Distributions

07:32

Types of Probability Distributions

2 questions

Characteristics of Discrete Distributions

02:00

Characteristics of Discrete Distributions

2 questions

Discrete Distributions: The Uniform Distribution

02:13

Discrete Distributions: The Uniform Distribution

2 questions

Discrete Distributions: The Bernoulli Distribution

03:26

Discrete Distributions: The Bernoulli Distribution

1 question

Discrete Distributions: The Binomial Distribution

07:04

Discrete Distributions: The Binomial Distribution

1 question

Discrete Distributions: The Poisson Distribution

05:27

Discrete Distributions: The Poisson Distribution

1 question

Characteristics of Continuous Distributions

07:12

Characteristics of Continuous Distributions

1 question

Continuous Distributions: The Normal Distribution

04:08

Continuous Distributions: The Normal Distribution

1 question

Continuous Distributions: The Standard Normal Distribution

04:25

Continuous Distributions: The Standard Normal Distribution

1 question

Continuous Distributions: The Students’ T Distribution

02:29

Continuous Distributions: The Students’ T Distribution

1 question

Continuous Distributions: The Chi-Squared Distribution

02:22

Continuous Distributions: The Chi-Squared Distribution

1 question

Continuous Distributions: The Exponential Distribution

03:15

Continuous Distributions: The Exponential Distribution

1 question

Continuous Distributions: The Logistic Distribution

04:07

Continuous Distributions: The Logistic Distribution

1 question

A Practical Example of Probability Distributions

15:03

-Probability – Probability in Other Fields

18:51

Probability in Finance

07:46

Probability in Statistics

06:18

Probability in Data Science

04:47

-Part 3: Statistics

04:02

Population and Sample

04:02

Population and Sample

2 questions

-Statistics – Descriptive Statistics

48:11

Types of Data

04:33

Types of Data

2 questions

Levels of Measurement

03:43

Levels of Measurement

2 questions

Categorical Variables – Visualization Techniques

Preview

04:52

Categorical Variables – Visualization Techniques

1 question

Categorical Variables Exercise

00:03

Numerical Variables – Frequency Distribution Table

03:09

Numerical Variables – Frequency Distribution Table

1 question

Numerical Variables Exercise

00:03

The Histogram

02:14

The Histogram

1 question

Histogram Exercise

00:03

Cross Tables and Scatter Plots

04:44

Cross Tables and Scatter Plots

1 question

Cross Tables and Scatter Plots Exercise

00:03

Mean, median and mode

04:20

Mean, Median and Mode Exercise

00:03

Skewness

02:37

Skewness

1 question

Skewness Exercise

00:03

Variance

05:55

Variance Exercise

00:15

Standard Deviation and Coefficient of Variation

04:40

Standard Deviation

1 question

Standard Deviation and Coefficient of Variation Exercise

00:03

Covariance

03:23

Covariance

1 question

Covariance Exercise

00:03

Correlation Coefficient

03:17

Correlation

1 question

Correlation Coefficient Exercise

00:03

-Statistics – Practical Example: Descriptive Statistics

16:18

Practical Example: Descriptive Statistics

Preview

16:15

Practical Example: Descriptive Statistics Exercise

00:03

-Statistics – Inferential Statistics Fundamentals

21:53

Introduction

01:00

What is a Distribution

04:33

What is a Distribution

1 question

The Normal Distribution

03:54

The Normal Distribution

1 question

The Standard Normal Distribution

03:30

The Standard Normal Distribution

1 question

The Standard Normal Distribution Exercise

00:03

Central Limit Theorem

04:20

Central Limit Theorem

1 question

Standard error

01:26

Standard Error

1 question

Estimators and Estimates

03:07

Estimators and Estimates

1 question

-Statistics – Inferential Statistics: Confidence Intervals

44:25

What are Confidence Intervals?

02:41

What are Confidence Intervals?

1 question

Confidence Intervals; Population Variance Known; z-score

08:01

Confidence Intervals; Population Variance Known; z-score; Exercise

00:03

Confidence Interval Clarifications

04:38

Student’s T Distribution

03:22

Student’s T Distribution

1 question

Confidence Intervals; Population Variance Unknown; t-score

04:36

Confidence Intervals; Population Variance Unknown; t-score; Exercise

00:03

Margin of Error

04:52

Margin of Error

1 question

Confidence intervals. Two means. Dependent samples

06:04

Confidence intervals. Two means. Dependent samples Exercise

00:03

Confidence intervals. Two means. Independent samples (Part 1)

04:31

Confidence intervals. Two means. Independent samples (Part 1) Exercise

00:03

Confidence intervals. Two means. Independent samples (Part 2)

03:57

Confidence intervals. Two means. Independent samples (Part 2) Exercise

00:03

Confidence intervals. Two means. Independent samples (Part 3)

01:27

-Statistics – Practical Example: Inferential Statistics

10:08

Practical Example: Inferential Statistics

10:05

Practical Example: Inferential Statistics Exercise

00:03

-Statistics – Hypothesis Testing

48:24

Null vs Alternative Hypothesis

Preview

05:51

Further Reading on Null and Alternative Hypothesis

01:16

Null vs Alternative Hypothesis

2 questions

Rejection Region and Significance Level

07:05

Rejection Region and Significance Level

2 questions

Type I Error and Type II Error

04:14

Type I Error and Type II Error

4 questions

Test for the Mean. Population Variance Known

06:34

Test for the Mean. Population Variance Known Exercise

00:03

p-value

04:13

p-value

4 questions

Test for the Mean. Population Variance Unknown

04:48

Test for the Mean. Population Variance Unknown Exercise

00:03

Test for the Mean. Dependent Samples

05:18

Test for the Mean. Dependent Samples Exercise

00:03

Test for the mean. Independent samples (Part 1)

04:22

Test for the mean. Independent samples (Part 1). Exercise

00:03

Test for the mean. Independent samples (Part 2)

04:26

Test for the mean. Independent samples (Part 2)

1 question

Test for the mean. Independent samples (Part 2) Exercise

00:03

-Statistics – Practical Example: Hypothesis Testing

07:19

Practical Example: Hypothesis Testing

07:16

Practical Example: Hypothesis Testing Exercise

00:03

-Part 4: Introduction to Python

32:49

Introduction to Programming

05:04

Introduction to Programming

2 questions

Why Python?

05:11

Why Python?

2 questions

Why Jupyter?

03:29

Why Jupyter?

2 questions

Installing Python and Jupyter

06:49

Understanding Jupyter’s Interface – the Notebook Dashboard

03:15

Prerequisites for Coding in the Jupyter Notebooks

06:15

Jupyter’s Interface

3 questions

Python 2 vs Python 3

02:46

-Python – Variables and Data Types

19:17

Variables

04:52

Variables

1 question

Numbers and Boolean Values in Python

03:05

Numbers and Boolean Values in Python

1 question

Python Strings

11:20

Python Strings

3 questions

-Python – Basic Python Syntax

15:13

Using Arithmetic Operators in Python

03:23

Using Arithmetic Operators in Python

1 question

The Double Equality Sign

01:33

The Double Equality Sign

1 question

How to Reassign Values

01:08

How to Reassign Values

1 question

Add Comments

03:20

Add Comments

1 question

Understanding Line Continuation

00:49

Get immediately download** The Data Science Course 2019: Complete Data Science Bootcamp**

Indexing Elements

01:18

Indexing Elements

1 question

Structuring with Indentation

03:42

Structuring with Indentation

1 question

-Python – Other Python Operators

07:45

Comparison Operators

02:10

Comparison Operators

2 questions

Logical and Identity Operators

05:35

Logical and Identity Operators

2 questions

-Python – Conditional Statements

27:44

The IF Statement

06:13

The IF Statement

1 question

The ELSE Statement

05:37

The ELIF Statement

11:16

A Note on Boolean Values

04:38

A Note on Boolean Values

1 question

-Python – Python Functions

29:26

Defining a Function in Python

04:20

How to Create a Function with a Parameter

07:58

Defining a Function in Python – Part II

05:29

How to Use a Function within a Function

01:49

Conditional Statements and Functions

03:06

Functions Containing a Few Arguments

02:48

Built-in Functions in Python

03:56

Python Functions

2 questions

-Python – Sequences

34:49

Lists

08:18

Lists

1 question

Using Methods

06:54

Using Methods

1 question

List Slicing

04:30

Tuples

06:40

Dictionaries

08:27

Dictionaries

1 question

-Python – Iterations

32:30

For Loops

Preview

05:40

For Loops

1 question

While Loops and Incrementing

05:10

Lists with the range() Function

06:22

Lists with the range() Function

1 question

Conditional Statements and Loops

06:30

Conditional Statements, Functions, and Loops

02:27

How to Iterate over Dictionaries

06:21

-Python – Advanced Python Tools

12:56

Object Oriented Programming

05:00

Object Oriented Programming

2 questions

Modules and Packages

01:05

Modules and Packages

2 questions

What is the Standard Library?

02:47

What is the Standard Library?

1 question

Importing Modules in Python

04:04

Importing Modules in Python

2 questions

-Part 5: Advanced Statistical Methods in Python

01:27

Introduction to Regression Analysis

01:27

Introduction to Regression Analysis

1 question

-Advanced Statistical Methods – Linear regression with StatsModels

40:55

The Linear Regression Model

05:50

The Linear Regression Model

2 questions

Correlation vs Regression

01:43

Correlation vs Regression

1 question

Geometrical Representation of the Linear Regression Model

01:25

Geometrical Representation of the Linear Regression Model

1 question

Python Packages Installation

04:39

First Regression in Python

07:11

First Regression in Python Exercise

00:39

Using Seaborn for Graphs

01:21

How to Interpret the Regression Table

05:47

How to Interpret the Regression Table

3 questions

Decomposition of Variability

03:37

Decomposition of Variability

1 question

What is the OLS?

03:13

What is the OLS

1 question

R-Squared

05:30

R-Squared

2 questions

-Advanced Statistical Methods – Multiple Linear Regression with StatsModels

42:18

Multiple Linear Regression

02:55

Multiple Linear Regression

1 question

Adjusted R-Squared

06:00

Adjusted R-Squared

3 questions

Multiple Linear Regression Exercise

00:03

Test for Significance of the Model (F-Test)

02:01

OLS Assumptions

02:21

OLS Assumptions

1 question

A1: Linearity

01:50

A1: Linearity

2 questions

A2: No Endogeneity

04:09

A2: No Endogeneity

1 question

A3: Normality and Homoscedasticity

05:47

A4: No Autocorrelation

03:31

A4: No autocorrelation

2 questions

A5: No Multicollinearity

03:26

A5: No Multicollinearity

1 question

Dealing with Categorical Data – Dummy Variables

06:43

Dealing with Categorical Data – Dummy Variables

00:03

Making Predictions with the Linear Regression

03:29

-Advanced Statistical Methods – Linear Regression with sklearn

54:27

What is sklearn and How is it Different from Other Packages

02:14

How are Going to Approach this Section?

01:56

Simple Linear Regression with sklearn

Preview

05:38

Simple Linear Regression with sklearn – A StatsModels-like Summary Table

Preview

04:49

A Note on Normalization

00:09

Simple Linear Regression with sklearn – Exercise

00:03

Multiple Linear Regression with sklearn

03:10

Calculating the Adjusted R-Squared in sklearn

04:45

Calculating the Adjusted R-Squared in sklearn – Exercise

00:03

Feature Selection (F-regression)

04:41

A Note on Calculation of P-values with sklearn

00:13

Creating a Summary Table with p-values

02:10

Multiple Linear Regression – Exercise

00:03

Feature Scaling (Standardization)

05:38

Feature Selection through Standardization of Weights

05:22

Predicting with the Standardized Coefficients

03:53

Feature Scaling (Standardization) – Exercise

00:03

Underfitting and Overfitting

02:42

Train – Test Split Explained

06:54

-Advanced Statistical Methods – Practical Example: Linear Regression

37:58

Practical Example: Linear Regression (Part 1)

11:59

Practical Example: Linear Regression (Part 2)

06:12

A Note on Multicollinearity

00:14

Practical Example: Linear Regression (Part 3)

03:15

Dummies and Variance Inflation Factor – Exercise

00:03

Practical Example: Linear Regression (Part 4)

08:10

Dummy Variables – Exercise

00:14

Practical Example: Linear Regression (Part 5)

07:34

Linear Regression – Exercise

00:16

-Advanced Statistical Methods – Logistic Regression

40:49

Introduction to Logistic Regression

01:19

A Simple Example in Python

04:42

Logistic vs Logit Function

04:00

Building a Logistic Regression

02:48

Building a Logistic Regression – Exercise

00:03

An Invaluable Coding Tip

02:26

Understanding Logistic Regression Tables

04:06

Understanding Logistic Regression Tables – Exercise

00:03

What do the Odds Actually Mean

04:30

Binary Predictors in a Logistic Regression

04:32

Binary Predictors in a Logistic Regression – Exercise

00:03

Calculating the Accuracy of the Model

03:21

Calculating the Accuracy of the Model

00:03

Underfitting and Overfitting

03:43

Testing the Model

05:05

Testing the Model – Exercise

00:03

-Advanced Statistical Methods – Cluster Analysis

14:03

Introduction to Cluster Analysis

03:41

Some Examples of Clusters

04:31

Difference between Classification and Clustering

02:32

Math Prerequisites

03:19

-Advanced Statistical Methods – K-Means Clustering

49:01

K-Means Clustering

04:41

A Simple Example of Clustering

07:48

A Simple Example of Clustering – Exercise

00:03

Clustering Categorical Data

02:50

Clustering Categorical Data – Exercise

00:03

How to Choose the Number of Clusters

06:11

How to Choose the Number of Clusters – Exercise

00:03

Pros and Cons of K-Means Clustering

03:23

To Standardize or not to Standardize

04:32

Relationship between Clustering and Regression

01:31

Market Segmentation with Cluster Analysis (Part 1)

06:03

Market Segmentation with Cluster Analysis (Part 2)

06:58

How is Clustering Useful?

04:47

EXERCISE: Species Segmentation with Cluster Analysis (Part 1)

00:03

EXERCISE: Species Segmentation with Cluster Analysis (Part 2)

00:03

-Advanced Statistical Methods – Other Types of Clustering

13:34

Types of Clustering

03:39

Dendrogram

05:21

Heatmaps

Preview

04:34

-Part 6: Mathematics

51:01

What is a matrix?

03:37

What is a Matrix?

6 questions

Scalars and Vectors

02:58

Scalars and Vectors

5 questions

Linear Algebra and Geometry

03:06

Linear Algebra and Geometry

3 questions

Arrays in Python – A Convenient Way To Represent Matrices

05:09

What is a Tensor?

03:00

What is a Tensor?

2 questions

Addition and Subtraction of Matrices

03:36

Addition and Subtraction of Matrices

3 questions

Errors when Adding Matrices

02:01

Transpose of a Matrix

05:13

Dot Product

03:48

Dot Product of Matrices

08:23

Why is Linear Algebra Useful?

10:10

-Part 7: Deep Learning

03:07

What to Expect from this Part?

03:07

What is Machine Learning

4 questions

-Deep Learning – Introduction to Neural Networks

42:38

Introduction to Neural Networks

04:09

Introduction to Neural Networks

1 question

Training the Model

02:54

Training the Model

3 questions

Types of Machine Learning

03:43

Get immediately download **The Data Science Course 2019: Complete Data Science Bootcamp**

**Types of Machine Learning**

4 questions

The Linear Model (Linear Algebraic Version)

03:08

The Linear Model

2 questions

The Linear Model with Multiple Inputs

02:25

The Linear Model with Multiple Inputs

2 questions

The Linear model with Multiple Inputs and Multiple Outputs

04:25

The Linear model with Multiple Inputs and Multiple Outputs

3 questions

Graphical Representation of Simple Neural Networks

01:47

Graphical Representation of Simple Neural Networks

1 question

What is the Objective Function?

01:27

What is the Objective Function?

2 questions

Common Objective Functions: L2-norm Loss

02:04

Common Objective Functions: L2-norm Loss

3 questions

Common Objective Functions: Cross-Entropy Loss

03:55

Common Objective Functions: Cross-Entropy Loss

4 questions

Optimization Algorithm: 1-Parameter Gradient Descent

06:33

Optimization Algorithm: 1-Parameter Gradient Descent

4 questions

Optimization Algorithm: n-Parameter Gradient Descent

06:08

Optimization Algorithm: n-Parameter Gradient Descent

3 questions

-Deep Learning – How to Build a Neural Network from Scratch with NumPy

20:35

Basic NN Example (Part 1)

03:06

Basic NN Example (Part 2)

04:58

Basic NN Example (Part 3)

03:25

Basic NN Example (Part 4)

08:15

Basic NN Example Exercises

00:51

-Deep Learning – TensorFlow 2.0: Introduction

28:10

How to Install TensorFlow 2.0

05:02

TensorFlow Outline and Comparison with Other Libraries

03:28

TensorFlow 1 vs TensorFlow 2

02:33

A Note on TensorFlow 2 Syntax

00:58

Types of File Formats Supporting TensorFlow

02:34

Outlining the Model with TensorFlow 2

05:48

Interpreting the Result and Extracting the Weights and Bias

04:09

Customizing a TensorFlow 2 Model

02:51

Basic NN with TensorFlow: Exercises

00:47

-Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks

25:44

What is a Layer?

01:53

What is a Deep Net?

02:18

Digging into a Deep Net

04:58

Non-Linearities and their Purpose

02:59

Activation Functions

03:37

Activation Functions: Softmax Activation

03:24

Backpropagation

03:12

Backpropagation picture

03:02

Backpropagation – A Peek into the Mathematics of Optimization

00:21

-Deep Learning – Overfitting

19:36

What is Overfitting?

03:51

Underfitting and Overfitting for Classification

01:52

What is Validation?

03:22

Training, Validation, and Test Datasets

02:30

N-Fold Cross Validation

03:07

Early Stopping or When to Stop Training

04:54

-Deep Learning – Initialization

08:04

What is Initialization?

02:32

Types of Simple Initializations

02:47

State-of-the-Art Method – (Xavier) Glorot Initialization

02:45

-Deep Learning – Digging into Gradient Descent and Learning Rate Schedules

20:40

Stochastic Gradient Descent

03:24

Problems with Gradient Descent

02:02

Momentum

02:30

Learning Rate Schedules, or How to Choose the Optimal Learning Rate

04:25

Learning Rate Schedules Visualized

01:32

Adaptive Learning Rate Schedules (AdaGrad and RMSprop )

04:08

Adam (Adaptive Moment Estimation)

02:39

-Deep Learning – Preprocessing

14:33

Preprocessing Introduction

02:51

Types of Basic Preprocessing

01:17

Standardization

04:31

Preprocessing Categorical Data

02:15

Binary and One-Hot Encoding

03:39

-Deep Learning – Classifying on the MNIST Dataset

36:34

MNIST: The Dataset

02:25

MNIST: How to Tackle the MNIST

02:44

MNIST: Importing the Relevant Packages and Loading the Data

02:11

MNIST: Preprocess the Data – Create a Validation Set and Scale It

04:43

MNIST: Preprocess the Data – Scale the Test Data – Exercise

00:03

MNIST: Preprocess the Data – Shuffle and Batch

06:30

MNIST: Preprocess the Data – Shuffle and Batch – Exercise

00:03

MNIST: Outline the Model

04:54

MNIST: Select the Loss and the Optimizer

02:05

MNIST: Learning

05:38

MNIST – Exercises

01:21

MNIST: Testing the Model

03:56

-Deep Learning – Business Case Example

39:19

Business Case: Exploring the Dataset and Identifying Predictors

07:54

Business Case: Outlining the Solution

01:31

Business Case: Balancing the Dataset

03:39

Business Case: Preprocessing the Data

11:32

Business Case: Preprocessing the Data – Exercise

00:12

Business Case: Load the Preprocessed Data

03:23

Business Case: Load the Preprocessed Data – Exercise

00:03

Business Case: Learning and Interpreting the Result

04:15

Business Case: Setting an Early Stopping Mechanism

05:01

Setting an Early Stopping Mechanism – Exercise

00:08

Business Case: Testing the Model

01:23

Business Case: Final Exercise

00:16

-Deep Learning – Conclusion

17:26

Summary on What You’ve Learned

03:41

What’s Further out there in terms of Machine Learning

01:47

DeepMind and Deep Learning

00:21

An overview of CNNs

04:55

An Overview of RNNs

02:50

An Overview of non-NN Approaches

03:52

-Appendix: Deep Learning – TensorFlow 1: Introduction

28:52

READ ME!!!!

00:21

How to Install TensorFlow 1

02:20

A Note on Installing Packages in Anaconda

01:14

TensorFlow Intro

03:46

Actual Introduction to TensorFlow

01:40

Types of File Formats, supporting Tensors

02:38

Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases

06:05

Basic NN Example with TF: Loss Function and Gradient Descent

03:41

Basic NN Example with TF: Model Output

06:05

Basic NN Example with TF Exercises

01:01

-Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset

39:31

MNIST: What is the MNIST Dataset?

02:26

MNIST: How to Tackle the MNIST

02:48

MNIST: Relevant Packages

01:34

MNIST: Model Outline

06:51

MNIST: Loss and Optimization Algorithm

02:39

Calculating the Accuracy of the Model

04:18

MNIST: Batching and Early Stopping

02:08

MNIST: Learning

07:35

MNIST: Results and Testing

06:11

MNIST: Solutions

01:31

MNIST: Exercises

01:29

-Appendix: Deep Learning – TensorFlow 1: Business Case

50:57

Business Case: Getting acquainted with the dataset

07:55

Business Case: Outlining the Solution

01:57

The Importance of Working with a Balanced Dataset

03:39

Business Case: Preprocessing

11:35

Business Case: Preprocessing Exercise

00:13

Creating a Data Provider

06:37

Business Case: Model Outline

05:34

Business Case: Optimization

05:10

Business Case: Interpretation

02:05

Business Case: Testing the Model

02:04

Business Case: A Comment on the Homework

03:51

Business Case: Final Exercise

00:17

-Software Integration

29:38

What are Data, Servers, Clients, Requests, and Responses

04:43

What are Data, Servers, Clients, Requests, and Responses

2 questions

What are Data Connectivity, APIs, and Endpoints?

07:05

What are Data Connectivity, APIs, and Endpoints?

2 questions

Taking a Closer Look at APIs

08:05

Taking a Closer Look at APIs

2 questions

Communication between Software Products through Text Files

04:20

Communication between Software Products through Text Files

1 question

Software Integration – Explained

05:25

Software Integration – Explained

2 questions

-Case Study – What’s Next in the Course?

10:14

Game Plan for this Python, SQL, and Tableau Business Exercise

04:08

The Business Task

02:48

Introducing the Data Set

03:18

Introducing the Data Set

1 question

-Case Study – Preprocessing the ‘Absenteeism_data’

01:29:34

What to Expect from the Following Sections?

01:28

Importing the Absenteeism Data in Python

03:23

Checking the Content of the Data Set

05:53

Introduction to Terms with Multiple Meanings

03:27

What’s Regression Analysis – a Quick Refresher

01:50

Using a Statistical Approach towards the Solution to the Exercise

02:17

Dropping a Column from a DataFrame in Python

06:27

EXERCISE – Dropping a Column from a DataFrame in Python

00:26

SOLUTION – Dropping a Column from a DataFrame in Python

00:01

Analyzing the Reasons for Absence

05:04

Obtaining Dummies from a Single Feature

08:37

EXERCISE – Obtaining Dummies from a Single Feature

00:04

SOLUTION – Obtaining Dummies from a Single Feature

00:00

Dropping a Dummy Variable from the Data Set

01:32

More on Dummy Variables: A Statistical Perspective

01:28

Classifying the Various Reasons for Absence

08:35

Using .concat() in Python

04:35

EXERCISE – Using .concat() in Python

00:04

SOLUTION – Using .concat() in Python

00:01

Reordering Columns in a Pandas DataFrame in Python

01:43

EXERCISE – Reordering Columns in a Pandas DataFrame in Python

00:06

SOLUTION – Reordering Columns in a Pandas DataFrame in Python

00:12

Creating Checkpoints while Coding in Jupyter

02:52

EXERCISE – Creating Checkpoints while Coding in Jupyter

00:04

SOLUTION – Creating Checkpoints while Coding in Jupyter

00:00

Analyzing the Dates from the Initial Data Set

07:48

Extracting the Month Value from the “Date” Column

07:00

Extracting the Day of the Week from the “Date” Column

03:36

EXERCISE – Removing the “Date” Column

00:37

Analyzing Several “Straightforward” Columns for this Exercise

03:17

Working on “Education”, “Children”, and “Pets”

04:38

Final Remarks of this Section

01:59

A Note on Exporting Your Data as a *.csv File

00:26

-Case Study – Applying Machine Learning to Create the ‘absenteeism_module’

01:07:05

Exploring the Problem with a Machine Learning Mindset

03:20

Creating the Targets for the Logistic Regression

06:32

Selecting the Inputs for the Logistic Regression

02:41

Standardizing the Data

03:26

Splitting the Data for Training and Testing

06:12

Fitting the Model and Assessing its Accuracy

05:39

Creating a Summary Table with the Coefficients and Intercept

05:16

Interpreting the Coefficients for Our Problem

06:14

Standardizing only the Numerical Variables (Creating a Custom Scaler)

04:12

Interpreting the Coefficients of the Logistic Regression

05:10

Backward Elimination or How to Simplify Your Model

04:02

Testing the Model We Created

04:43

Saving the Model and Preparing it for Deployment

04:06

ARTICLE – A Note on ‘pickling’

01:15

EXERCISE – Saving the Model (and Scaler)

00:13

Preparing the Deployment of the Model through a Module

04:04

-Case Study – Loading the ‘absenteeism_module’

10:58

Are You Sure You’re All Set?

00:14

Deploying the ‘absenteeism_module’ – Part I

03:50

Deploying the ‘absenteeism_module’ – Part

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