The Data Science Course 2019: Complete Data Science Bootcamp

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The Data Science Course 2019: Complete Data Science Bootcamp

What you’ll learn What you’ll learn

The course provides the entire toolbox you need to become a data scientistThe 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 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 Impress interviewers by showing an understanding of the data science field
Learn how to pre-process data Learn how to pre-process data
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!) 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 Start coding in Python and learn how to use it for statistical analysis
Perform linear and logistic regressions in Python Perform linear and logistic regressions in Python
Carry out cluster and factor analysis Carry out cluster and factor analysis
Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
Apply your skills to real-life business cases 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 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 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 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 Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Get Get The Data Science Course 2019: Complete Data Science Bootcamp download
Course content Course content
Expand all 471 lectures28:52:43 Expand all 471 lectures28:52:43
-Part 1: Introduction -Part 1: Introduction
19:20 19:20
A Practical Example: What You Will Learn in This Course A Practical Example: What You Will Learn in This Course
Preview Preview
05:05 05:05
What Does the Course Cover What Does the Course Cover
Preview Preview
03:34 03:34
Download All Resources and Important FAQ Download All Resources and Important FAQ
10:41 10:41
-The Field of Data Science – The Various Data Science Disciplines -The Field of Data Science – The Various Data Science Disciplines
31:11 31:11
Data Science and Business Buzzwords: Why are there so many? Data Science and Business Buzzwords: Why are there so many?
Preview Preview
05:21 05:21
Data Science and Business Buzzwords: Why are there so many? Data Science and Business Buzzwords: Why are there so many?
1 question 1 question
What is the difference between Analysis and Analytics What is the difference between Analysis and Analytics
03:50 03:50
What is the difference between Analysis and Analytics What is the difference between Analysis and Analytics
1 question 1 question
Business Analytics, Data Analytics, and Data Science: An Introduction Business Analytics, Data Analytics, and Data Science: An Introduction
Preview Preview
08:26 08:26
Business Analytics, Data Analytics, and Data Science: An Introduction Business Analytics, Data Analytics, and Data Science: An Introduction
3 questions 3 questions
Continuing with BI, ML, and AI Continuing with BI, ML, and AI
09:31 09:31
Continuing with BI, ML, and AI Continuing with BI, ML, and AI
2 questions 2 questions
A Breakdown of our Data Science Infographic A Breakdown of our Data Science Infographic
04:03 04:03
A Breakdown of our Data Science Infographic A Breakdown of our Data Science Infographic
1 question 1 question
-The Field of Data Science – Connecting the Data Science Disciplines -The Field of Data Science – Connecting the Data Science Disciplines
07:19 07:19
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
07:19 07:19
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
1 question 1 question
-The Field of Data Science – The Benefits of Each Discipline -The Field of Data Science – The Benefits of Each Discipline
04:44 04:44
The Reason behind these Disciplines The Reason behind these Disciplines
04:44 04:44
The Reason behind these Disciplines The Reason behind these Disciplines
1 question 1 question
-The Field of Data Science – Popular Data Science Techniques -The Field of Data Science – Popular Data Science Techniques
53:34 53:34
Techniques for Working with Traditional Data Techniques for Working with Traditional Data
08:13 08:13
Techniques for Working with Traditional Data Techniques for Working with Traditional Data
1 question 1 question
Real Life Examples of Traditional Data Real Life Examples of Traditional Data
01:44 01:44
Techniques for Working with Big Data Techniques for Working with Big Data
04:26 04:26
Techniques for Working with Big Data Techniques for Working with Big Data
1 question 1 question
Real Life Examples of Big Data Real Life Examples of Big Data
01:32 01:32
Business Intelligence (BI) Techniques Business Intelligence (BI) Techniques
06:45 06:45
Business Intelligence (BI) Techniques Business Intelligence (BI) Techniques
4 questions 4 questions
Real Life Examples of Business Intelligence (BI) Real Life Examples of Business Intelligence (BI)
01:42 01:42
Techniques for Working with Traditional Methods Techniques for Working with Traditional Methods
09:08 09:08
Techniques for Working with Traditional Methods Techniques for Working with Traditional Methods
4 questions 4 questions
Real Life Examples of Traditional Methods Real Life Examples of Traditional Methods
02:45 02:45
Machine Learning (ML) Techniques Machine Learning (ML) Techniques
06:55 06:55
Machine Learning (ML) Techniques Machine Learning (ML) Techniques
2 questions 2 questions
Types of Machine Learning Types of Machine Learning
08:13 08:13
Types of Machine Learning Types of Machine Learning
2 questions 2 questions
Real Life Examples of Machine Learning (ML) Real Life Examples of Machine Learning (ML)
02:11 02:11
Real Life Examples of Machine Learning (ML) Real Life Examples of Machine Learning (ML)
5 questions 5 questions
-The Field of Data Science – Popular Data Science Tools -The Field of Data Science – Popular Data Science Tools
05:51 05:51
Necessary Programming Languages and Software Used in Data Science Necessary Programming Languages and Software Used in Data Science
05:51 05:51
Necessary Programming Languages and Software Used in Data Science Necessary Programming Languages and Software Used in Data Science
4 questions 4 questions
-The Field of Data Science – Careers in Data Science -The Field of Data Science – Careers in Data Science
03:29 03:29
Finding the Job – What to Expect and What to Look for Finding the Job – What to Expect and What to Look for
03:29 03:29
Finding the Job – What to Expect and What to Look for Finding the Job – What to Expect and What to Look for
1 question 1 question
-The Field of Data Science – Debunking Common Misconceptions -The Field of Data Science – Debunking Common Misconceptions
04:10 04:10
Debunking Common Misconceptions Debunking Common Misconceptions
04:10 04:10
Debunking Common Misconceptions Debunking Common Misconceptions
1 question 1 question
-Part 2: Probability -Part 2: Probability
23:04 23:04
The Basic Probability Formula The Basic Probability Formula
07:09 07:09
The Basic Probability Formula The Basic Probability Formula
3 questions 3 questions
Computing Expected Values Computing Expected Values
05:29 05:29
Computing Expected Values Computing Expected Values
3 questions 3 questions
Frequency Frequency
05:00 05:00
Frequency Frequency
3 questions 3 questions
Events and Their Complements Events and Their Complements
05:26 05:26
Events and Their Complements Events and Their Complements
3 questions 3 questions
-Probability – Combinatorics -Probability – Combinatorics
42:56 42:56
Fundamentals of Combinatorics Fundamentals of Combinatorics
01:04 01:04
Fundamentals of Combinatorics Fundamentals of Combinatorics
1 question 1 question
Permutations and How to Use Them Permutations and How to Use Them
03:21 03:21
Permutations and How to Use Them Permutations and How to Use Them
2 questions 2 questions
Simple Operations with Factorials Simple Operations with Factorials
03:35 03:35
Simple Operations with Factorials Simple Operations with Factorials
3 questions 3 questions
Solving Variations with Repetition Solving Variations with Repetition
02:59 02:59
Solving Variations with Repetition Solving Variations with Repetition
3 questions 3 questions
Solving Variations without Repetition Solving Variations without Repetition
03:48 03:48
Solving Variations without Repetition Solving Variations without Repetition
3 questions 3 questions
Solving Combinations Solving Combinations
04:51 04:51
Solving Combinations Solving Combinations
4 questions 4 questions
Symmetry of Combinations Symmetry of Combinations
03:26 03:26
Symmetry of Combinations Symmetry of Combinations
1 question 1 question
Solving Combinations with Separate Sample Spaces Solving Combinations with Separate Sample Spaces
02:52 02:52
Solving Combinations with Separate Sample Spaces Solving Combinations with Separate Sample Spaces
1 question 1 question
Combinatorics in Real-Life: The Lottery Combinatorics in Real-Life: The Lottery
03:12 03:12
Combinatorics in Real-Life: The Lottery Combinatorics in Real-Life: The Lottery
1 question 1 question
A Recap of Combinatorics A Recap of Combinatorics
02:55 02:55
A Practical Example of Combinatorics A Practical Example of Combinatorics
10:53 10:53
-Probability – Bayesian Inference -Probability – Bayesian Inference
54:38 54:38
Sets and Events Sets and Events
04:25 04:25
Sets and Events Sets and Events
3 questions 3 questions
Ways Sets Can Interact Ways Sets Can Interact
03:45 03:45
Ways Sets Can Interact Ways Sets Can Interact
2 questions 2 questions
Intersection of Sets Intersection of Sets
02:06 02:06
Intersection of Sets Intersection of Sets
3 questions 3 questions
Union of Sets Union of Sets
04:51 04:51
Union of Sets Union of Sets
3 questions 3 questions
Mutually Exclusive Sets Mutually Exclusive Sets
02:09 02:09
Mutually Exclusive Sets Mutually Exclusive Sets
4 questions 4 questions
Dependence and Independence of Sets Dependence and Independence of Sets
03:01 03:01
Dependence and Independence of Sets Dependence and Independence of Sets
3 questions 3 questions
The Conditional Probability Formula The Conditional Probability Formula
04:16 04:16
The Conditional Probability Formula The Conditional Probability Formula
3 questions 3 questions
The Law of Total Probability The Law of Total Probability
03:03 03:03
The Additive Rule The Additive Rule
02:21 02:21
The Additive Rule The Additive Rule
2 questions 2 questions
The Multiplication Law The Multiplication Law
04:05 04:05
The Multiplication Law The Multiplication Law
2 questions 2 questions
Bayes’ Law Bayes’ Law
05:44 05:44
Bayes’ Law Bayes’ Law
2 questions 2 questions
A Practical Example of Bayesian Inference A Practical Example of Bayesian Inference
14:52 14:52
-Probability – Distributions -Probability – Distributions
01:17:12 01:17:12
Fundamentals of Probability Distributions Fundamentals of Probability Distributions
06:29 06:29
Fundamentals of Probability Distributions Fundamentals of Probability Distributions
3 questions 3 questions
Types of Probability Distributions Types of Probability Distributions
07:32 07:32
Types of Probability Distributions Types of Probability Distributions
2 questions 2 questions
Characteristics of Discrete Distributions Characteristics of Discrete Distributions
02:00 02:00
Characteristics of Discrete Distributions Characteristics of Discrete Distributions
2 questions 2 questions
Discrete Distributions: The Uniform Distribution Discrete Distributions: The Uniform Distribution
02:13 02:13
Discrete Distributions: The Uniform Distribution Discrete Distributions: The Uniform Distribution
2 questions 2 questions
Discrete Distributions: The Bernoulli Distribution Discrete Distributions: The Bernoulli Distribution
03:26 03:26
Discrete Distributions: The Bernoulli Distribution Discrete Distributions: The Bernoulli Distribution
1 question 1 question
Discrete Distributions: The Binomial Distribution Discrete Distributions: The Binomial Distribution
07:04 07:04
Discrete Distributions: The Binomial Distribution Discrete Distributions: The Binomial Distribution
1 question 1 question
Discrete Distributions: The Poisson Distribution Discrete Distributions: The Poisson Distribution
05:27 05:27
Discrete Distributions: The Poisson Distribution Discrete Distributions: The Poisson Distribution
1 question 1 question
Characteristics of Continuous Distributions Characteristics of Continuous Distributions
07:12 07:12
Characteristics of Continuous Distributions Characteristics of Continuous Distributions
1 question 1 question
Continuous Distributions: The Normal Distribution Continuous Distributions: The Normal Distribution
04:08 04:08
Continuous Distributions: The Normal Distribution Continuous Distributions: The Normal Distribution
1 question 1 question
Continuous Distributions: The Standard Normal Distribution Continuous Distributions: The Standard Normal Distribution
04:25 04:25
Continuous Distributions: The Standard Normal Distribution Continuous Distributions: The Standard Normal Distribution
1 question 1 question
Continuous Distributions: The Students’ T Distribution Continuous Distributions: The Students’ T Distribution
02:29 02:29
Continuous Distributions: The Students’ T Distribution Continuous Distributions: The Students’ T Distribution
1 question 1 question
Continuous Distributions: The Chi-Squared Distribution Continuous Distributions: The Chi-Squared Distribution
02:22 02:22
Continuous Distributions: The Chi-Squared Distribution Continuous Distributions: The Chi-Squared Distribution
1 question 1 question
Continuous Distributions: The Exponential Distribution Continuous Distributions: The Exponential Distribution
03:15 03:15
Continuous Distributions: The Exponential Distribution Continuous Distributions: The Exponential Distribution
1 question 1 question
Continuous Distributions: The Logistic Distribution Continuous Distributions: The Logistic Distribution
04:07 04:07
Continuous Distributions: The Logistic Distribution Continuous Distributions: The Logistic Distribution
1 question 1 question
A Practical Example of Probability Distributions A Practical Example of Probability Distributions
15:03 15:03
-Probability – Probability in Other Fields -Probability – Probability in Other Fields
18:51 18:51
Probability in Finance Probability in Finance
07:46 07:46
Probability in Statistics Probability in Statistics
06:18 06:18
Probability in Data Science Probability in Data Science
04:47 04:47
-Part 3: Statistics -Part 3: Statistics
04:02 04:02
Population and Sample Population and Sample
04:02 04:02
Population and Sample Population and Sample
2 questions 2 questions
-Statistics – Descriptive Statistics -Statistics – Descriptive Statistics
48:11 48:11
Types of Data Types of Data
04:33 04:33
Types of Data Types of Data
2 questions 2 questions
Levels of Measurement Levels of Measurement
03:43 03:43
Levels of Measurement Levels of Measurement
2 questions 2 questions
Categorical Variables – Visualization Techniques Categorical Variables – Visualization Techniques
Preview Preview
04:52 04:52
Categorical Variables – Visualization Techniques Categorical Variables – Visualization Techniques
1 question 1 question
Categorical Variables Exercise Categorical Variables Exercise
00:03 00:03
Numerical Variables – Frequency Distribution Table Numerical Variables – Frequency Distribution Table
03:09 03:09
Numerical Variables – Frequency Distribution Table Numerical Variables – Frequency Distribution Table
1 question 1 question
Numerical Variables Exercise Numerical Variables Exercise
00:03 00:03
The Histogram The Histogram
02:14 02:14
The Histogram The Histogram
1 question 1 question
Histogram Exercise Histogram Exercise
00:03 00:03
Cross Tables and Scatter Plots Cross Tables and Scatter Plots
04:44 04:44
Cross Tables and Scatter Plots Cross Tables and Scatter Plots
1 question 1 question
Cross Tables and Scatter Plots Exercise Cross Tables and Scatter Plots Exercise
00:03 00:03
Mean, median and mode Mean, median and mode
04:20 04:20
Mean, Median and Mode Exercise Mean, Median and Mode Exercise
00:03 00:03
Skewness Skewness
02:37 02:37
Skewness Skewness
1 question 1 question
Skewness Exercise Skewness Exercise
00:03 00:03
Variance Variance
05:55 05:55
Variance Exercise Variance Exercise
00:15 00:15
Standard Deviation and Coefficient of Variation Standard Deviation and Coefficient of Variation
04:40 04:40
Standard Deviation Standard Deviation
1 question 1 question
Standard Deviation and Coefficient of Variation Exercise Standard Deviation and Coefficient of Variation Exercise
00:03 00:03
Covariance Covariance
03:23 03:23
Covariance Covariance
1 question 1 question
Covariance Exercise Covariance Exercise
00:03 00:03
Correlation Coefficient Correlation Coefficient
03:17 03:17
Correlation Correlation
1 question 1 question
Correlation Coefficient Exercise Correlation Coefficient Exercise
00:03 00:03
-Statistics – Practical Example: Descriptive Statistics -Statistics – Practical Example: Descriptive Statistics
16:18 16:18
Practical Example: Descriptive Statistics Practical Example: Descriptive Statistics
Preview Preview
16:15 16:15
Practical Example: Descriptive Statistics Exercise Practical Example: Descriptive Statistics Exercise
00:03 00:03
-Statistics – Inferential Statistics Fundamentals -Statistics – Inferential Statistics Fundamentals
21:53 21:53
Introduction Introduction
01:00 01:00
What is a Distribution What is a Distribution
04:33 04:33
What is a Distribution What is a Distribution
1 question 1 question
The Normal Distribution The Normal Distribution
03:54 03:54
The Normal Distribution The Normal Distribution
1 question 1 question
The Standard Normal Distribution The Standard Normal Distribution
03:30 03:30
The Standard Normal Distribution The Standard Normal Distribution
1 question 1 question
The Standard Normal Distribution Exercise The Standard Normal Distribution Exercise
00:03 00:03
Central Limit Theorem Central Limit Theorem
04:20 04:20
Central Limit Theorem Central Limit Theorem
1 question 1 question
Standard error Standard error
01:26 01:26
Standard Error Standard Error
1 question 1 question
Estimators and Estimates Estimators and Estimates
03:07 03:07
Estimators and Estimates Estimators and Estimates
1 question 1 question
-Statistics – Inferential Statistics: Confidence Intervals -Statistics – Inferential Statistics: Confidence Intervals
44:25 44:25
What are Confidence Intervals? What are Confidence Intervals?
02:41 02:41
What are Confidence Intervals? What are Confidence Intervals?
1 question 1 question
Confidence Intervals; Population Variance Known; z-score Confidence Intervals; Population Variance Known; z-score
08:01 08:01
Confidence Intervals; Population Variance Known; z-score; Exercise Confidence Intervals; Population Variance Known; z-score; Exercise
00:03 00:03
Confidence Interval Clarifications Confidence Interval Clarifications
04:38 04:38
Student’s T Distribution Student’s T Distribution
03:22 03:22
Student’s T Distribution Student’s T Distribution
1 question 1 question
Confidence Intervals; Population Variance Unknown; t-score Confidence Intervals; Population Variance Unknown; t-score
04:36 04:36
Confidence Intervals; Population Variance Unknown; t-score; Exercise Confidence Intervals; Population Variance Unknown; t-score; Exercise
00:03 00:03
Margin of Error Margin of Error
04:52 04:52
Margin of Error Margin of Error
1 question 1 question
Confidence intervals. Two means. Dependent samples Confidence intervals. Two means. Dependent samples
06:04 06:04
Confidence intervals. Two means. Dependent samples Exercise Confidence intervals. Two means. Dependent samples Exercise
00:03 00:03
Confidence intervals. Two means. Independent samples (Part 1) Confidence intervals. Two means. Independent samples (Part 1)
04:31 04:31
Confidence intervals. Two means. Independent samples (Part 1) Exercise Confidence intervals. Two means. Independent samples (Part 1) Exercise
00:03 00:03
Confidence intervals. Two means. Independent samples (Part 2) Confidence intervals. Two means. Independent samples (Part 2)
03:57 03:57
Confidence intervals. Two means. Independent samples (Part 2) Exercise Confidence intervals. Two means. Independent samples (Part 2) Exercise
00:03 00:03
Confidence intervals. Two means. Independent samples (Part 3) Confidence intervals. Two means. Independent samples (Part 3)
01:27 01:27
-Statistics – Practical Example: Inferential Statistics -Statistics – Practical Example: Inferential Statistics
10:08 10:08
Practical Example: Inferential Statistics Practical Example: Inferential Statistics
10:05 10:05
Practical Example: Inferential Statistics Exercise Practical Example: Inferential Statistics Exercise
00:03 00:03
-Statistics – Hypothesis Testing -Statistics – Hypothesis Testing
48:24 48:24
Null vs Alternative Hypothesis Null vs Alternative Hypothesis
Preview Preview
05:51 05:51
Further Reading on Null and Alternative Hypothesis Further Reading on Null and Alternative Hypothesis
01:16 01:16
Null vs Alternative Hypothesis Null vs Alternative Hypothesis
2 questions 2 questions
Rejection Region and Significance Level Rejection Region and Significance Level
07:05 07:05
Rejection Region and Significance Level Rejection Region and Significance Level
2 questions 2 questions
Type I Error and Type II Error Type I Error and Type II Error
04:14 04:14
Type I Error and Type II Error Type I Error and Type II Error
4 questions 4 questions
Test for the Mean. Population Variance Known Test for the Mean. Population Variance Known
06:34 06:34
Test for the Mean. Population Variance Known Exercise Test for the Mean. Population Variance Known Exercise
00:03 00:03
p-value p-value
04:13 04:13
p-value p-value
4 questions 4 questions
Test for the Mean. Population Variance Unknown Test for the Mean. Population Variance Unknown
04:48 04:48
Test for the Mean. Population Variance Unknown Exercise Test for the Mean. Population Variance Unknown Exercise
00:03 00:03
Test for the Mean. Dependent Samples Test for the Mean. Dependent Samples
05:18 05:18
Test for the Mean. Dependent Samples Exercise Test for the Mean. Dependent Samples Exercise
00:03 00:03
Test for the mean. Independent samples (Part 1) Test for the mean. Independent samples (Part 1)
04:22 04:22
Test for the mean. Independent samples (Part 1). Exercise Test for the mean. Independent samples (Part 1). Exercise
00:03 00:03
Test for the mean. Independent samples (Part 2) Test for the mean. Independent samples (Part 2)
04:26 04:26
Test for the mean. Independent samples (Part 2) Test for the mean. Independent samples (Part 2)
1 question 1 question
Test for the mean. Independent samples (Part 2) Exercise Test for the mean. Independent samples (Part 2) Exercise
00:03 00:03
-Statistics – Practical Example: Hypothesis Testing -Statistics – Practical Example: Hypothesis Testing
07:19 07:19
Practical Example: Hypothesis Testing Practical Example: Hypothesis Testing
07:16 07:16
Practical Example: Hypothesis Testing Exercise Practical Example: Hypothesis Testing Exercise
00:03 00:03
-Part 4: Introduction to Python -Part 4: Introduction to Python
32:49 32:49
Introduction to Programming Introduction to Programming
05:04 05:04
Introduction to Programming Introduction to Programming
2 questions 2 questions
Why Python? Why Python?
05:11 05:11
Why Python? Why Python?
2 questions 2 questions
Why Jupyter? Why Jupyter?
03:29 03:29
Why Jupyter? Why Jupyter?
2 questions 2 questions
Installing Python and Jupyter Installing Python and Jupyter
06:49 06:49
Understanding Jupyter’s Interface – the Notebook Dashboard Understanding Jupyter’s Interface – the Notebook Dashboard
03:15 03:15
Prerequisites for Coding in the Jupyter Notebooks Prerequisites for Coding in the Jupyter Notebooks
06:15 06:15
Jupyter’s Interface Jupyter’s Interface
3 questions 3 questions
Python 2 vs Python 3 Python 2 vs Python 3
02:46 02:46
-Python – Variables and Data Types -Python – Variables and Data Types
19:17 19:17
Variables Variables
04:52 04:52
Variables Variables
1 question 1 question
Numbers and Boolean Values in Python Numbers and Boolean Values in Python
03:05 03:05
Numbers and Boolean Values in Python Numbers and Boolean Values in Python
1 question 1 question
Python Strings Python Strings
11:20 11:20
Python Strings Python Strings
3 questions 3 questions
-Python – Basic Python Syntax -Python – Basic Python Syntax
15:13 15:13
Using Arithmetic Operators in Python Using Arithmetic Operators in Python
03:23 03:23
Using Arithmetic Operators in Python Using Arithmetic Operators in Python
1 question 1 question
The Double Equality Sign The Double Equality Sign
01:33 01:33
The Double Equality Sign The Double Equality Sign
1 question 1 question
How to Reassign Values How to Reassign Values
01:08 01:08
How to Reassign Values How to Reassign Values
1 question 1 question
Add Comments Add Comments
03:20 03:20
Add Comments Add Comments
1 question 1 question
Understanding Line Continuation Understanding Line Continuation
00:49 00:49

Get immediately downloadGet immediately download The Data Science Course 2019: Complete Data Science Bootcamp The Data Science Course 2019: Complete Data Science Bootcamp
Indexing Elements Indexing Elements
01:18 01:18
Indexing Elements Indexing Elements
1 question 1 question
Structuring with Indentation Structuring with Indentation
03:42 03:42
Structuring with Indentation Structuring with Indentation
1 question 1 question
-Python – Other Python Operators -Python – Other Python Operators
07:45 07:45
Comparison Operators Comparison Operators
02:10 02:10
Comparison Operators Comparison Operators
2 questions 2 questions
Logical and Identity Operators Logical and Identity Operators
05:35 05:35
Logical and Identity Operators Logical and Identity Operators
2 questions 2 questions
-Python – Conditional Statements -Python – Conditional Statements
27:44 27:44
The IF Statement The IF Statement
06:13 06:13
The IF Statement The IF Statement
1 question 1 question
The ELSE Statement The ELSE Statement
05:37 05:37
The ELIF Statement The ELIF Statement
11:16 11:16
A Note on Boolean Values A Note on Boolean Values
04:38 04:38
A Note on Boolean Values A Note on Boolean Values
1 question 1 question
-Python – Python Functions -Python – Python Functions
29:26 29:26
Defining a Function in Python Defining a Function in Python
04:20 04:20
How to Create a Function with a Parameter How to Create a Function with a Parameter
07:58 07:58
Defining a Function in Python – Part II Defining a Function in Python – Part II
05:29 05:29
How to Use a Function within a Function How to Use a Function within a Function
01:49 01:49
Conditional Statements and Functions Conditional Statements and Functions
03:06 03:06
Functions Containing a Few Arguments Functions Containing a Few Arguments
02:48 02:48
Built-in Functions in Python Built-in Functions in Python
03:56 03:56
Python Functions Python Functions
2 questions 2 questions
-Python – Sequences -Python – Sequences
34:49 34:49
Lists Lists
08:18 08:18
Lists Lists
1 question 1 question
Using Methods Using Methods
06:54 06:54
Using Methods Using Methods
1 question 1 question
List Slicing List Slicing
04:30 04:30
Tuples Tuples
06:40 06:40
Dictionaries Dictionaries
08:27 08:27
Dictionaries Dictionaries
1 question 1 question
-Python – Iterations -Python – Iterations
32:30 32:30
For Loops For Loops
Preview Preview
05:40 05:40
For Loops For Loops
1 question 1 question
While Loops and Incrementing While Loops and Incrementing
05:10 05:10
Lists with the range() Function Lists with the range() Function
06:22 06:22
Lists with the range() Function Lists with the range() Function
1 question 1 question
Conditional Statements and Loops Conditional Statements and Loops
06:30 06:30
Conditional Statements, Functions, and Loops Conditional Statements, Functions, and Loops
02:27 02:27
How to Iterate over Dictionaries How to Iterate over Dictionaries
06:21 06:21
-Python – Advanced Python Tools -Python – Advanced Python Tools
12:56 12:56
Object Oriented Programming Object Oriented Programming
05:00 05:00
Object Oriented Programming Object Oriented Programming
2 questions 2 questions
Modules and Packages Modules and Packages
01:05 01:05
Modules and Packages Modules and Packages
2 questions 2 questions
What is the Standard Library? What is the Standard Library?
02:47 02:47
What is the Standard Library? What is the Standard Library?
1 question 1 question
Importing Modules in Python Importing Modules in Python
04:04 04:04
Importing Modules in Python Importing Modules in Python
2 questions 2 questions
-Part 5: Advanced Statistical Methods in Python -Part 5: Advanced Statistical Methods in Python
01:27 01:27
Introduction to Regression Analysis Introduction to Regression Analysis
01:27 01:27
Introduction to Regression Analysis Introduction to Regression Analysis
1 question 1 question
-Advanced Statistical Methods – Linear regression with StatsModels -Advanced Statistical Methods – Linear regression with StatsModels
40:55 40:55
The Linear Regression Model The Linear Regression Model
05:50 05:50
The Linear Regression Model The Linear Regression Model
2 questions 2 questions
Correlation vs Regression Correlation vs Regression
01:43 01:43
Correlation vs Regression Correlation vs Regression
1 question 1 question
Geometrical Representation of the Linear Regression Model Geometrical Representation of the Linear Regression Model
01:25 01:25
Geometrical Representation of the Linear Regression Model Geometrical Representation of the Linear Regression Model
1 question 1 question
Python Packages Installation Python Packages Installation
04:39 04:39
First Regression in Python First Regression in Python
07:11 07:11
First Regression in Python Exercise First Regression in Python Exercise
00:39 00:39
Using Seaborn for Graphs Using Seaborn for Graphs
01:21 01:21
How to Interpret the Regression Table How to Interpret the Regression Table
05:47 05:47
How to Interpret the Regression Table How to Interpret the Regression Table
3 questions 3 questions
Decomposition of Variability Decomposition of Variability
03:37 03:37
Decomposition of Variability Decomposition of Variability
1 question 1 question
What is the OLS? What is the OLS?
03:13 03:13
What is the OLS What is the OLS
1 question 1 question
R-Squared R-Squared
05:30 05:30
R-Squared R-Squared
2 questions 2 questions
-Advanced Statistical Methods – Multiple Linear Regression with StatsModels -Advanced Statistical Methods – Multiple Linear Regression with StatsModels
42:18 42:18
Multiple Linear Regression Multiple Linear Regression
02:55 02:55
Multiple Linear Regression Multiple Linear Regression
1 question 1 question
Adjusted R-Squared Adjusted R-Squared
06:00 06:00
Adjusted R-Squared Adjusted R-Squared
3 questions 3 questions
Multiple Linear Regression Exercise Multiple Linear Regression Exercise
00:03 00:03
Test for Significance of the Model (F-Test) Test for Significance of the Model (F-Test)
02:01 02:01
OLS Assumptions OLS Assumptions
02:21 02:21
OLS Assumptions OLS Assumptions
1 question 1 question
A1: Linearity A1: Linearity
01:50 01:50
A1: Linearity A1: Linearity
2 questions 2 questions
A2: No Endogeneity A2: No Endogeneity
04:09 04:09
A2: No Endogeneity A2: No Endogeneity
1 question 1 question
A3: Normality and Homoscedasticity A3: Normality and Homoscedasticity
05:47 05:47
A4: No Autocorrelation A4: No Autocorrelation
03:31 03:31
A4: No autocorrelation A4: No autocorrelation
2 questions 2 questions
A5: No Multicollinearity A5: No Multicollinearity
03:26 03:26
A5: No Multicollinearity A5: No Multicollinearity
1 question 1 question
Dealing with Categorical Data – Dummy Variables Dealing with Categorical Data – Dummy Variables
06:43 06:43
Dealing with Categorical Data – Dummy Variables Dealing with Categorical Data – Dummy Variables
00:03 00:03
Making Predictions with the Linear Regression Making Predictions with the Linear Regression
03:29 03:29
-Advanced Statistical Methods – Linear Regression with sklearn -Advanced Statistical Methods – Linear Regression with sklearn
54:27 54:27
What is sklearn and How is it Different from Other Packages What is sklearn and How is it Different from Other Packages
02:14 02:14
How are Going to Approach this Section? How are Going to Approach this Section?
01:56 01:56
Simple Linear Regression with sklearn Simple Linear Regression with sklearn
Preview Preview
05:38 05:38
Simple Linear Regression with sklearn – A StatsModels-like Summary Table Simple Linear Regression with sklearn – A StatsModels-like Summary Table
Preview Preview
04:49 04:49
A Note on Normalization A Note on Normalization
00:09 00:09
Simple Linear Regression with sklearn – Exercise Simple Linear Regression with sklearn – Exercise
00:03 00:03
Multiple Linear Regression with sklearn Multiple Linear Regression with sklearn
03:10 03:10
Calculating the Adjusted R-Squared in sklearn Calculating the Adjusted R-Squared in sklearn
04:45 04:45
Calculating the Adjusted R-Squared in sklearn – Exercise Calculating the Adjusted R-Squared in sklearn – Exercise
00:03 00:03
Feature Selection (F-regression) Feature Selection (F-regression)
04:41 04:41
A Note on Calculation of P-values with sklearn A Note on Calculation of P-values with sklearn
00:13 00:13
Creating a Summary Table with p-values Creating a Summary Table with p-values
02:10 02:10
Multiple Linear Regression – Exercise Multiple Linear Regression – Exercise
00:03 00:03
Feature Scaling (Standardization) Feature Scaling (Standardization)
05:38 05:38
Feature Selection through Standardization of Weights Feature Selection through Standardization of Weights
05:22 05:22
Predicting with the Standardized Coefficients Predicting with the Standardized Coefficients
03:53 03:53
Feature Scaling (Standardization) – Exercise Feature Scaling (Standardization) – Exercise
00:03 00:03
Underfitting and Overfitting Underfitting and Overfitting
02:42 02:42
Train – Test Split Explained Train – Test Split Explained
06:54 06:54
-Advanced Statistical Methods – Practical Example: Linear Regression -Advanced Statistical Methods – Practical Example: Linear Regression
37:58 37:58
Practical Example: Linear Regression (Part 1) Practical Example: Linear Regression (Part 1)
11:59 11:59
Practical Example: Linear Regression (Part 2) Practical Example: Linear Regression (Part 2)
06:12 06:12
A Note on Multicollinearity A Note on Multicollinearity
00:14 00:14
Practical Example: Linear Regression (Part 3) Practical Example: Linear Regression (Part 3)
03:15 03:15
Dummies and Variance Inflation Factor – Exercise Dummies and Variance Inflation Factor – Exercise
00:03 00:03
Practical Example: Linear Regression (Part 4) Practical Example: Linear Regression (Part 4)
08:10 08:10
Dummy Variables – Exercise Dummy Variables – Exercise
00:14 00:14
Practical Example: Linear Regression (Part 5) Practical Example: Linear Regression (Part 5)
07:34 07:34
Linear Regression – Exercise Linear Regression – Exercise
00:16 00:16
-Advanced Statistical Methods – Logistic Regression -Advanced Statistical Methods – Logistic Regression
40:49 40:49
Introduction to Logistic Regression Introduction to Logistic Regression
01:19 01:19
A Simple Example in Python A Simple Example in Python
04:42 04:42
Logistic vs Logit Function Logistic vs Logit Function
04:00 04:00
Building a Logistic Regression Building a Logistic Regression
02:48 02:48
Building a Logistic Regression – Exercise Building a Logistic Regression – Exercise
00:03 00:03
An Invaluable Coding Tip An Invaluable Coding Tip
02:26 02:26
Understanding Logistic Regression Tables Understanding Logistic Regression Tables
04:06 04:06
Understanding Logistic Regression Tables – Exercise Understanding Logistic Regression Tables – Exercise
00:03 00:03
What do the Odds Actually Mean What do the Odds Actually Mean
04:30 04:30
Binary Predictors in a Logistic Regression Binary Predictors in a Logistic Regression
04:32 04:32
Binary Predictors in a Logistic Regression – Exercise Binary Predictors in a Logistic Regression – Exercise
00:03 00:03
Calculating the Accuracy of the Model Calculating the Accuracy of the Model
03:21 03:21
Calculating the Accuracy of the Model Calculating the Accuracy of the Model
00:03 00:03
Underfitting and Overfitting Underfitting and Overfitting
03:43 03:43
Testing the Model Testing the Model
05:05 05:05
Testing the Model – Exercise Testing the Model – Exercise
00:03 00:03
-Advanced Statistical Methods – Cluster Analysis -Advanced Statistical Methods – Cluster Analysis
14:03 14:03
Introduction to Cluster Analysis Introduction to Cluster Analysis
03:41 03:41
Some Examples of Clusters Some Examples of Clusters
04:31 04:31
Difference between Classification and Clustering Difference between Classification and Clustering
02:32 02:32
Math Prerequisites Math Prerequisites
03:19 03:19
-Advanced Statistical Methods – K-Means Clustering -Advanced Statistical Methods – K-Means Clustering
49:01 49:01
K-Means Clustering K-Means Clustering
04:41 04:41
A Simple Example of Clustering A Simple Example of Clustering
07:48 07:48
A Simple Example of Clustering – Exercise A Simple Example of Clustering – Exercise
00:03 00:03
Clustering Categorical Data Clustering Categorical Data
02:50 02:50
Clustering Categorical Data – Exercise Clustering Categorical Data – Exercise
00:03 00:03
How to Choose the Number of Clusters How to Choose the Number of Clusters
06:11 06:11
How to Choose the Number of Clusters – Exercise How to Choose the Number of Clusters – Exercise
00:03 00:03
Pros and Cons of K-Means Clustering Pros and Cons of K-Means Clustering
03:23 03:23
To Standardize or not to Standardize To Standardize or not to Standardize
04:32 04:32
Relationship between Clustering and Regression Relationship between Clustering and Regression
01:31 01:31
Market Segmentation with Cluster Analysis (Part 1) Market Segmentation with Cluster Analysis (Part 1)
06:03 06:03
Market Segmentation with Cluster Analysis (Part 2) Market Segmentation with Cluster Analysis (Part 2)
06:58 06:58
How is Clustering Useful? How is Clustering Useful?
04:47 04:47
EXERCISE: Species Segmentation with Cluster Analysis (Part 1) EXERCISE: Species Segmentation with Cluster Analysis (Part 1)
00:03 00:03
EXERCISE: Species Segmentation with Cluster Analysis (Part 2) EXERCISE: Species Segmentation with Cluster Analysis (Part 2)
00:03 00:03
-Advanced Statistical Methods – Other Types of Clustering -Advanced Statistical Methods – Other Types of Clustering
13:34 13:34
Types of Clustering Types of Clustering
03:39 03:39
Dendrogram Dendrogram
05:21 05:21
Heatmaps Heatmaps
Preview Preview
04:34 04:34
-Part 6: Mathematics -Part 6: Mathematics
51:01 51:01
What is a matrix? What is a matrix?
03:37 03:37
What is a Matrix? What is a Matrix?
6 questions 6 questions
Scalars and Vectors Scalars and Vectors
02:58 02:58
Scalars and Vectors Scalars and Vectors
5 questions 5 questions
Linear Algebra and Geometry Linear Algebra and Geometry
03:06 03:06
Linear Algebra and Geometry Linear Algebra and Geometry
3 questions 3 questions
Arrays in Python – A Convenient Way To Represent Matrices Arrays in Python – A Convenient Way To Represent Matrices
05:09 05:09
What is a Tensor? What is a Tensor?
03:00 03:00
What is a Tensor? What is a Tensor?
2 questions 2 questions
Addition and Subtraction of Matrices Addition and Subtraction of Matrices
03:36 03:36
Addition and Subtraction of Matrices Addition and Subtraction of Matrices
3 questions 3 questions
Errors when Adding Matrices Errors when Adding Matrices
02:01 02:01
Transpose of a Matrix Transpose of a Matrix
05:13 05:13
Dot Product Dot Product
03:48 03:48
Dot Product of Matrices Dot Product of Matrices
08:23 08:23
Why is Linear Algebra Useful? Why is Linear Algebra Useful?
10:10 10:10
-Part 7: Deep Learning -Part 7: Deep Learning
03:07 03:07
What to Expect from this Part? What to Expect from this Part?
03:07 03:07
What is Machine Learning What is Machine Learning
4 questions 4 questions
-Deep Learning – Introduction to Neural Networks -Deep Learning – Introduction to Neural Networks
42:38 42:38
Introduction to Neural Networks Introduction to Neural Networks
04:09 04:09
Introduction to Neural Networks Introduction to Neural Networks
1 question 1 question
Training the Model Training the Model
02:54 02:54
Training the Model Training the Model
3 questions 3 questions
Types of Machine Learning Types of Machine Learning
03:43 03:43

Get immediately download Get immediately download The Data Science Course 2019: Complete Data Science BootcampThe Data Science Course 2019: Complete Data Science Bootcamp
Types of Machine LearningTypes of Machine Learning
4 questions 4 questions
The Linear Model (Linear Algebraic Version) The Linear Model (Linear Algebraic Version)
03:08 03:08
The Linear Model The Linear Model
2 questions 2 questions
The Linear Model with Multiple Inputs The Linear Model with Multiple Inputs
02:25 02:25
The Linear Model with Multiple Inputs The Linear Model with Multiple Inputs
2 questions 2 questions
The Linear model with Multiple Inputs and Multiple Outputs The Linear model with Multiple Inputs and Multiple Outputs
04:25 04:25
The Linear model with Multiple Inputs and Multiple Outputs The Linear model with Multiple Inputs and Multiple Outputs
3 questions 3 questions
Graphical Representation of Simple Neural Networks Graphical Representation of Simple Neural Networks
01:47 01:47
Graphical Representation of Simple Neural Networks Graphical Representation of Simple Neural Networks
1 question 1 question
What is the Objective Function? What is the Objective Function?
01:27 01:27
What is the Objective Function? What is the Objective Function?
2 questions 2 questions
Common Objective Functions: L2-norm Loss Common Objective Functions: L2-norm Loss
02:04 02:04
Common Objective Functions: L2-norm Loss Common Objective Functions: L2-norm Loss
3 questions 3 questions
Common Objective Functions: Cross-Entropy Loss Common Objective Functions: Cross-Entropy Loss
03:55 03:55
Common Objective Functions: Cross-Entropy Loss Common Objective Functions: Cross-Entropy Loss
4 questions 4 questions
Optimization Algorithm: 1-Parameter Gradient Descent Optimization Algorithm: 1-Parameter Gradient Descent
06:33 06:33
Optimization Algorithm: 1-Parameter Gradient Descent Optimization Algorithm: 1-Parameter Gradient Descent
4 questions 4 questions
Optimization Algorithm: n-Parameter Gradient Descent Optimization Algorithm: n-Parameter Gradient Descent
06:08 06:08
Optimization Algorithm: n-Parameter Gradient Descent Optimization Algorithm: n-Parameter Gradient Descent
3 questions 3 questions
-Deep Learning – How to Build a Neural Network from Scratch with NumPy -Deep Learning – How to Build a Neural Network from Scratch with NumPy
20:35 20:35
Basic NN Example (Part 1) Basic NN Example (Part 1)
03:06 03:06
Basic NN Example (Part 2) Basic NN Example (Part 2)
04:58 04:58
Basic NN Example (Part 3) Basic NN Example (Part 3)
03:25 03:25
Basic NN Example (Part 4) Basic NN Example (Part 4)
08:15 08:15
Basic NN Example Exercises Basic NN Example Exercises
00:51 00:51
-Deep Learning – TensorFlow 2.0: Introduction -Deep Learning – TensorFlow 2.0: Introduction
28:10 28:10
How to Install TensorFlow 2.0 How to Install TensorFlow 2.0
05:02 05:02
TensorFlow Outline and Comparison with Other Libraries TensorFlow Outline and Comparison with Other Libraries
03:28 03:28
TensorFlow 1 vs TensorFlow 2 TensorFlow 1 vs TensorFlow 2
02:33 02:33
A Note on TensorFlow 2 Syntax A Note on TensorFlow 2 Syntax
00:58 00:58
Types of File Formats Supporting TensorFlow Types of File Formats Supporting TensorFlow
02:34 02:34
Outlining the Model with TensorFlow 2 Outlining the Model with TensorFlow 2
05:48 05:48
Interpreting the Result and Extracting the Weights and Bias Interpreting the Result and Extracting the Weights and Bias
04:09 04:09
Customizing a TensorFlow 2 Model Customizing a TensorFlow 2 Model
02:51 02:51
Basic NN with TensorFlow: Exercises Basic NN with TensorFlow: Exercises
00:47 00:47
-Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks -Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
25:44 25:44
What is a Layer? What is a Layer?
01:53 01:53
What is a Deep Net? What is a Deep Net?
02:18 02:18
Digging into a Deep Net Digging into a Deep Net
04:58 04:58
Non-Linearities and their Purpose Non-Linearities and their Purpose
02:59 02:59
Activation Functions Activation Functions
03:37 03:37
Activation Functions: Softmax Activation Activation Functions: Softmax Activation
03:24 03:24
Backpropagation Backpropagation
03:12 03:12
Backpropagation picture Backpropagation picture
03:02 03:02
Backpropagation – A Peek into the Mathematics of Optimization Backpropagation – A Peek into the Mathematics of Optimization
00:21 00:21
-Deep Learning – Overfitting -Deep Learning – Overfitting
19:36 19:36
What is Overfitting? What is Overfitting?
03:51 03:51
Underfitting and Overfitting for Classification Underfitting and Overfitting for Classification
01:52 01:52
What is Validation? What is Validation?
03:22 03:22
Training, Validation, and Test Datasets Training, Validation, and Test Datasets
02:30 02:30
N-Fold Cross Validation N-Fold Cross Validation
03:07 03:07
Early Stopping or When to Stop Training Early Stopping or When to Stop Training
04:54 04:54
-Deep Learning – Initialization -Deep Learning – Initialization
08:04 08:04
What is Initialization? What is Initialization?
02:32 02:32
Types of Simple Initializations Types of Simple Initializations
02:47 02:47
State-of-the-Art Method – (Xavier) Glorot Initialization State-of-the-Art Method – (Xavier) Glorot Initialization
02:45 02:45
-Deep Learning – Digging into Gradient Descent and Learning Rate Schedules -Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
20:40 20:40
Stochastic Gradient Descent Stochastic Gradient Descent
03:24 03:24
Problems with Gradient Descent Problems with Gradient Descent
02:02 02:02
Momentum Momentum
02:30 02:30
Learning Rate Schedules, or How to Choose the Optimal Learning Rate Learning Rate Schedules, or How to Choose the Optimal Learning Rate
04:25 04:25
Learning Rate Schedules Visualized Learning Rate Schedules Visualized
01:32 01:32
Adaptive Learning Rate Schedules (AdaGrad and RMSprop ) Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
04:08 04:08
Adam (Adaptive Moment Estimation) Adam (Adaptive Moment Estimation)
02:39 02:39
-Deep Learning – Preprocessing -Deep Learning – Preprocessing
14:33 14:33
Preprocessing Introduction Preprocessing Introduction
02:51 02:51
Types of Basic Preprocessing Types of Basic Preprocessing
01:17 01:17
Standardization Standardization
04:31 04:31
Preprocessing Categorical Data Preprocessing Categorical Data
02:15 02:15
Binary and One-Hot Encoding Binary and One-Hot Encoding
03:39 03:39
-Deep Learning – Classifying on the MNIST Dataset -Deep Learning – Classifying on the MNIST Dataset
36:34 36:34
MNIST: The Dataset MNIST: The Dataset
02:25 02:25
MNIST: How to Tackle the MNIST MNIST: How to Tackle the MNIST
02:44 02:44
MNIST: Importing the Relevant Packages and Loading the Data MNIST: Importing the Relevant Packages and Loading the Data
02:11 02:11
MNIST: Preprocess the Data – Create a Validation Set and Scale It MNIST: Preprocess the Data – Create a Validation Set and Scale It
04:43 04:43
MNIST: Preprocess the Data – Scale the Test Data – Exercise MNIST: Preprocess the Data – Scale the Test Data – Exercise
00:03 00:03
MNIST: Preprocess the Data – Shuffle and Batch MNIST: Preprocess the Data – Shuffle and Batch
06:30 06:30
MNIST: Preprocess the Data – Shuffle and Batch – Exercise MNIST: Preprocess the Data – Shuffle and Batch – Exercise
00:03 00:03
MNIST: Outline the Model MNIST: Outline the Model
04:54 04:54
MNIST: Select the Loss and the Optimizer MNIST: Select the Loss and the Optimizer
02:05 02:05
MNIST: Learning MNIST: Learning
05:38 05:38
MNIST – Exercises MNIST – Exercises
01:21 01:21
MNIST: Testing the Model MNIST: Testing the Model
03:56 03:56
-Deep Learning – Business Case Example -Deep Learning – Business Case Example
39:19 39:19
Business Case: Exploring the Dataset and Identifying Predictors Business Case: Exploring the Dataset and Identifying Predictors
07:54 07:54
Business Case: Outlining the Solution Business Case: Outlining the Solution
01:31 01:31
Business Case: Balancing the Dataset Business Case: Balancing the Dataset
03:39 03:39
Business Case: Preprocessing the Data Business Case: Preprocessing the Data
11:32 11:32
Business Case: Preprocessing the Data – Exercise Business Case: Preprocessing the Data – Exercise
00:12 00:12
Business Case: Load the Preprocessed Data Business Case: Load the Preprocessed Data
03:23 03:23
Business Case: Load the Preprocessed Data – Exercise Business Case: Load the Preprocessed Data – Exercise
00:03 00:03
Business Case: Learning and Interpreting the Result Business Case: Learning and Interpreting the Result
04:15 04:15
Business Case: Setting an Early Stopping Mechanism Business Case: Setting an Early Stopping Mechanism
05:01 05:01
Setting an Early Stopping Mechanism – Exercise Setting an Early Stopping Mechanism – Exercise
00:08 00:08
Business Case: Testing the Model Business Case: Testing the Model
01:23 01:23
Business Case: Final Exercise Business Case: Final Exercise
00:16 00:16
-Deep Learning – Conclusion -Deep Learning – Conclusion
17:26 17:26
Summary on What You’ve Learned Summary on What You’ve Learned
03:41 03:41
What’s Further out there in terms of Machine Learning What’s Further out there in terms of Machine Learning
01:47 01:47
DeepMind and Deep Learning DeepMind and Deep Learning
00:21 00:21
An overview of CNNs An overview of CNNs
04:55 04:55
An Overview of RNNs An Overview of RNNs
02:50 02:50
An Overview of non-NN Approaches An Overview of non-NN Approaches
03:52 03:52
-Appendix: Deep Learning – TensorFlow 1: Introduction -Appendix: Deep Learning – TensorFlow 1: Introduction
28:52 28:52
READ ME!!!! READ ME!!!!
00:21 00:21
How to Install TensorFlow 1 How to Install TensorFlow 1
02:20 02:20
A Note on Installing Packages in Anaconda A Note on Installing Packages in Anaconda
01:14 01:14
TensorFlow Intro TensorFlow Intro
03:46 03:46
Actual Introduction to TensorFlow Actual Introduction to TensorFlow
01:40 01:40
Types of File Formats, supporting Tensors Types of File Formats, supporting Tensors
02:38 02:38
Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases
06:05 06:05
Basic NN Example with TF: Loss Function and Gradient Descent Basic NN Example with TF: Loss Function and Gradient Descent
03:41 03:41
Basic NN Example with TF: Model Output Basic NN Example with TF: Model Output
06:05 06:05
Basic NN Example with TF Exercises Basic NN Example with TF Exercises
01:01 01:01
-Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset -Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset
39:31 39:31
MNIST: What is the MNIST Dataset? MNIST: What is the MNIST Dataset?
02:26 02:26
MNIST: How to Tackle the MNIST MNIST: How to Tackle the MNIST
02:48 02:48
MNIST: Relevant Packages MNIST: Relevant Packages
01:34 01:34
MNIST: Model Outline MNIST: Model Outline
06:51 06:51
MNIST: Loss and Optimization Algorithm MNIST: Loss and Optimization Algorithm
02:39 02:39
Calculating the Accuracy of the Model Calculating the Accuracy of the Model
04:18 04:18
MNIST: Batching and Early Stopping MNIST: Batching and Early Stopping
02:08 02:08
MNIST: Learning MNIST: Learning
07:35 07:35
MNIST: Results and Testing MNIST: Results and Testing
06:11 06:11
MNIST: Solutions MNIST: Solutions
01:31 01:31
MNIST: Exercises MNIST: Exercises
01:29 01:29
-Appendix: Deep Learning – TensorFlow 1: Business Case -Appendix: Deep Learning – TensorFlow 1: Business Case
50:57 50:57
Business Case: Getting acquainted with the dataset Business Case: Getting acquainted with the dataset
07:55 07:55
Business Case: Outlining the Solution Business Case: Outlining the Solution
01:57 01:57
The Importance of Working with a Balanced Dataset The Importance of Working with a Balanced Dataset
03:39 03:39
Business Case: Preprocessing Business Case: Preprocessing
11:35 11:35
Business Case: Preprocessing Exercise Business Case: Preprocessing Exercise
00:13 00:13
Creating a Data Provider Creating a Data Provider
06:37 06:37
Business Case: Model Outline Business Case: Model Outline
05:34 05:34
Business Case: Optimization Business Case: Optimization
05:10 05:10
Business Case: Interpretation Business Case: Interpretation
02:05 02:05
Business Case: Testing the Model Business Case: Testing the Model
02:04 02:04
Business Case: A Comment on the Homework Business Case: A Comment on the Homework
03:51 03:51
Business Case: Final Exercise Business Case: Final Exercise
00:17 00:17
-Software Integration -Software Integration
29:38 29:38
What are Data, Servers, Clients, Requests, and Responses What are Data, Servers, Clients, Requests, and Responses
04:43 04:43
What are Data, Servers, Clients, Requests, and Responses What are Data, Servers, Clients, Requests, and Responses
2 questions 2 questions
What are Data Connectivity, APIs, and Endpoints? What are Data Connectivity, APIs, and Endpoints?
07:05 07:05
What are Data Connectivity, APIs, and Endpoints? What are Data Connectivity, APIs, and Endpoints?
2 questions 2 questions
Taking a Closer Look at APIs Taking a Closer Look at APIs
08:05 08:05
Taking a Closer Look at APIs Taking a Closer Look at APIs
2 questions 2 questions
Communication between Software Products through Text Files Communication between Software Products through Text Files
04:20 04:20
Communication between Software Products through Text Files Communication between Software Products through Text Files
1 question 1 question
Software Integration – Explained Software Integration – Explained
05:25 05:25
Software Integration – Explained Software Integration – Explained
2 questions 2 questions
-Case Study – What’s Next in the Course? -Case Study – What’s Next in the Course?
10:14 10:14
Game Plan for this Python, SQL, and Tableau Business Exercise Game Plan for this Python, SQL, and Tableau Business Exercise
04:08 04:08
The Business Task The Business Task
02:48 02:48
Introducing the Data Set Introducing the Data Set
03:18 03:18
Introducing the Data Set Introducing the Data Set
1 question 1 question
-Case Study – Preprocessing the ‘Absenteeism_data’ -Case Study – Preprocessing the ‘Absenteeism_data’
01:29:34 01:29:34
What to Expect from the Following Sections? What to Expect from the Following Sections?
01:28 01:28
Importing the Absenteeism Data in Python Importing the Absenteeism Data in Python
03:23 03:23
Checking the Content of the Data Set Checking the Content of the Data Set
05:53 05:53
Introduction to Terms with Multiple Meanings Introduction to Terms with Multiple Meanings
03:27 03:27
What’s Regression Analysis – a Quick Refresher What’s Regression Analysis – a Quick Refresher
01:50 01:50
Using a Statistical Approach towards the Solution to the Exercise Using a Statistical Approach towards the Solution to the Exercise
02:17 02:17
Dropping a Column from a DataFrame in Python Dropping a Column from a DataFrame in Python
06:27 06:27
EXERCISE – Dropping a Column from a DataFrame in Python EXERCISE – Dropping a Column from a DataFrame in Python
00:26 00:26
SOLUTION – Dropping a Column from a DataFrame in Python SOLUTION – Dropping a Column from a DataFrame in Python
00:01 00:01
Analyzing the Reasons for Absence Analyzing the Reasons for Absence
05:04 05:04
Obtaining Dummies from a Single Feature Obtaining Dummies from a Single Feature
08:37 08:37
EXERCISE – Obtaining Dummies from a Single Feature EXERCISE – Obtaining Dummies from a Single Feature
00:04 00:04
SOLUTION – Obtaining Dummies from a Single Feature SOLUTION – Obtaining Dummies from a Single Feature
00:00 00:00
Dropping a Dummy Variable from the Data Set Dropping a Dummy Variable from the Data Set
01:32 01:32
More on Dummy Variables: A Statistical Perspective More on Dummy Variables: A Statistical Perspective
01:28 01:28
Classifying the Various Reasons for Absence Classifying the Various Reasons for Absence
08:35 08:35
Using .concat() in Python Using .concat() in Python
04:35 04:35
EXERCISE – Using .concat() in Python EXERCISE – Using .concat() in Python
00:04 00:04
SOLUTION – Using .concat() in Python SOLUTION – Using .concat() in Python
00:01 00:01
Reordering Columns in a Pandas DataFrame in Python Reordering Columns in a Pandas DataFrame in Python
01:43 01:43
EXERCISE – Reordering Columns in a Pandas DataFrame in Python EXERCISE – Reordering Columns in a Pandas DataFrame in Python
00:06 00:06
SOLUTION – Reordering Columns in a Pandas DataFrame in Python SOLUTION – Reordering Columns in a Pandas DataFrame in Python
00:12 00:12
Creating Checkpoints while Coding in Jupyter Creating Checkpoints while Coding in Jupyter
02:52 02:52
EXERCISE – Creating Checkpoints while Coding in Jupyter EXERCISE – Creating Checkpoints while Coding in Jupyter
00:04 00:04
SOLUTION – Creating Checkpoints while Coding in Jupyter SOLUTION – Creating Checkpoints while Coding in Jupyter
00:00 00:00
Analyzing the Dates from the Initial Data Set Analyzing the Dates from the Initial Data Set
07:48 07:48
Extracting the Month Value from the “Date” Column Extracting the Month Value from the “Date” Column
07:00 07:00
Extracting the Day of the Week from the “Date” Column Extracting the Day of the Week from the “Date” Column
03:36 03:36
EXERCISE – Removing the “Date” Column EXERCISE – Removing the “Date” Column
00:37 00:37
Analyzing Several “Straightforward” Columns for this Exercise Analyzing Several “Straightforward” Columns for this Exercise
03:17 03:17
Working on “Education”, “Children”, and “Pets” Working on “Education”, “Children”, and “Pets”
04:38 04:38
Final Remarks of this Section Final Remarks of this Section
01:59 01:59
A Note on Exporting Your Data as a *.csv File A Note on Exporting Your Data as a *.csv File
00:26 00:26
-Case Study – Applying Machine Learning to Create the ‘absenteeism_module’ -Case Study – Applying Machine Learning to Create the ‘absenteeism_module’
01:07:05 01:07:05
Exploring the Problem with a Machine Learning Mindset Exploring the Problem with a Machine Learning Mindset
03:20 03:20
Creating the Targets for the Logistic Regression Creating the Targets for the Logistic Regression
06:32 06:32
Selecting the Inputs for the Logistic Regression Selecting the Inputs for the Logistic Regression
02:41 02:41
Standardizing the Data Standardizing the Data
03:26 03:26
Splitting the Data for Training and Testing Splitting the Data for Training and Testing
06:12 06:12
Fitting the Model and Assessing its Accuracy Fitting the Model and Assessing its Accuracy
05:39 05:39
Creating a Summary Table with the Coefficients and Intercept Creating a Summary Table with the Coefficients and Intercept
05:16 05:16
Interpreting the Coefficients for Our Problem Interpreting the Coefficients for Our Problem
06:14 06:14
Standardizing only the Numerical Variables (Creating a Custom Scaler) Standardizing only the Numerical Variables (Creating a Custom Scaler)
04:12 04:12
Interpreting the Coefficients of the Logistic Regression Interpreting the Coefficients of the Logistic Regression
05:10 05:10
Backward Elimination or How to Simplify Your Model Backward Elimination or How to Simplify Your Model
04:02 04:02
Testing the Model We Created Testing the Model We Created
04:43 04:43
Saving the Model and Preparing it for Deployment Saving the Model and Preparing it for Deployment
04:06 04:06
ARTICLE – A Note on ‘pickling’ ARTICLE – A Note on ‘pickling’
01:15 01:15
EXERCISE – Saving the Model (and Scaler) EXERCISE – Saving the Model (and Scaler)
00:13 00:13
Preparing the Deployment of the Model through a Module Preparing the Deployment of the Model through a Module
04:04 04:04
-Case Study – Loading the ‘absenteeism_module’ -Case Study – Loading the ‘absenteeism_module’
10:58 10:58
Are You Sure You’re All Set? Are You Sure You’re All Set?
00:14 00:14
Deploying the ‘absenteeism_module’ – Part I Deploying the ‘absenteeism_module’ – Part I
03:50 03:50
Deploying the ‘absenteeism_module’ – Part Deploying the ‘absenteeism_module’ – Part

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