Le cours de Data Science 2022 : Bootcamp complet sur la Data Science
Formation complète en science des données : mathématiques, statistiques, Python, statistiques avancées en Python, machine et apprentissage en profondeur
Le cours fournit toute la boîte à outils dont vous avez besoin pour devenir un data scientist
Remplissez votre CV avec des compétences en science des données recherchées : analyse statistique, programmation Python avec NumPy, pandas, matplotlib et Seaborn, analyse statistique avancée, Tableau, Machine Learning avec des modèles de statistiques et scikit-learn, Deep learning avec TensorFlow
Impressionner les enquêteurs en montrant une compréhension du domaine de la science des données
Apprenez à pré-traiter les données
Comprendre les mathématiques derrière l'apprentissage automatique (un must absolu que d'autres cours n'enseignent pas !)
Commencez à coder en Python et apprenez à l'utiliser pour l'analyse statistique
Effectuer des régressions linéaires et logistiques en Python
Effectuer des analyses cluster et factorielles
Être capable de créer des algorithmes de Machine Learning en Python, en utilisant NumPy, statsmodels et scikit-learn
Appliquez vos compétences à des cas d'affaires réels
Utilisez des frameworks de Deep Learning à la pointe de la technologie tels que TensorFlow de GoogleDéveloppez une intuition commerciale tout en codant et en résolvant des tâches avec le Big Data
Déployez la puissance des réseaux de neurones profonds
Améliorer les algorithmes d'apprentissage automatique en étudiant le sous-ajustement, le surajustement, la formation, la validation, la validation croisée n fois, les tests et comment les hyperparamètres pourraient améliorer les performances
Échauffez-vous les doigts car vous aurez hâte d'appliquer tout ce que vous avez appris ici à de plus en plus de situations réelles
Le problème
Le scientifique des données est l’une des professions les mieux adaptées pour prospérer au cours de ce siècle. Il est numérique, orienté programmation et analytique. Par conséquent, il n’est pas surprenant que la demande de spécialistes des données ait augmenté sur le marché du travail.
Cependant, l’offre a été très limitée. Il est difficile d’acquérir les compétences nécessaires pour être embauché en tant que data scientist.
Et comment pouvez-vous faire cela ?
Les universités ont mis du temps à créer des programmes spécialisés en science des données. (sans compter que ceux qui existent sont très coûteux et chronophages)
La plupart des cours en ligne se concentrent sur un sujet spécifique et il est difficile de comprendre comment la compétence qu’ils enseignent s’intègre dans l’image complète
La solution  ;
La science des données est un domaine multidisciplinaire. Il englobe un large éventail de sujets.
- Compréhension du domaine de la science des données et du type d’analyse effectuée
- Mathématiques   ;
- Statistiques  ;
- Python
- Application de techniques statistiques avancées en Python
- Visualisation des données
- Apprentissage automatique  ;
- Apprentissage en profondeur   ;
Chacun de ces sujets s’appuie sur les précédents. Et vous risquez de vous perdre en cours de route si vous n’acquérez pas ces compétences dans le bon ordre. Par exemple, on aurait du mal à appliquer les techniques d’apprentissage automatique avant de comprendre les mathématiques sous-jacentes. Ou bien, il peut être difficile d’étudier l’analyse de régression en Python avant de savoir ce qu’est une régression.
Ainsi, dans le but de créer la formation en science des données la plus efficace, la plus rapide et la plus structurée disponible en ligne, nous avons créé le cours de science des données 2022.   ;
Nous pensons qu’il s’agit du premier programme de formation qui résout le plus grand défi pour entrer dans le domaine de la science des données – avoir toutes les ressources nécessaires en un seul endroit.
De plus, notre objectif est d’enseigner des sujets fluides et complémentaires. Le cours vous apprend tout ce que vous devez savoir pour devenir un data scientist à une fraction du coût des programmes traditionnels (sans parler du temps que vous gagnerez).
Les compétences
1. Introduction aux données et à la science des données
Big data, business intelligence, business analytics, machine learning et intelligence artificielle. Nous savons que ces mots à la mode appartiennent au domaine de la science des données, mais que signifient-ils tous ?
Pourquoi l’apprendre ?
En tant que candidat scientifique des données, vous devez comprendre les tenants et les aboutissants de chacun de ces domaines et reconnaître l’approche appropriée pour résoudre un problème. Cette « Introduction aux données et à la science des données » vous donnera un aperçu complet de tous ces mots à la mode et de leur place dans le domaine de la science des données.
  ;
  ; 2. Mathématiques
L’apprentissage des outils est la première étape pour faire de la science des données. Vous devez d’abord avoir une vue d’ensemble pour ensuite examiner les pièces en détail.
Nous examinons en détail spécifiquement le calcul différentiel et l’algèbre linéaire, car ce sont les sous-domaines sur lesquels repose la science des données.
Le problème
Le scientifique des données est l’une des professions les mieux adaptées pour prospérer au cours de ce siècle. Il est numérique, orienté programmation et analytique. Par conséquent, il n’est pas surprenant que la demande de spécialistes des données soit augmentée sur le marché du travail.
Cependant, l’offre a été très limitée. Il est difficile d’acquérir les compétences nécessaires pour être embauché en tant que data scientist.
Et comment pouvez-vous faire cela ?
Les universités ont mis du temps à créer des programmes spécialisés en science des données. (sans compter que ceux qui existent sont très désignés et chronophages)
La plupart des cours en ligne se concentrent sur un sujet spécifique et il est difficile de comprendre comment la compétence qu’ils enseignent s’intègre dans l’image complète
La solution  ;
La science des données est un domaine multidisciplinaire. Il englobe un large éventail de sujets.
- Compréhension du domaine de la science des données et du type d’analyse effectuée
- Mathématiques ;
- Statistiques ;
- Python
- Application de techniques statistiques avancées en Python
- Visualisation des données
- Apprentissage automatique ;
- Apprentissage en profondeur   ;
Chacun de ces sujets s’appuie sur les précédents. Et vous risquez de vous perdre en cours de route si vous n’acquiérez pas ces compétences dans le bon ordre. Par exemple, on aurait du mal à appliquer les techniques d’apprentissage automatique avant de comprendre les mathématiques sous-jacentes. Ou bien, il peut être difficile d’étudier l’analyse de régression en Python avant de savoir ce qu’est une régression.
Ainsi, dans le but de créer la formation en science des données la plus efficace, la plus rapide et la plus disponible en ligne, nous avons créé le cours de science des données 2022.   ;
Nous pensons qu’il s’agit du premier programme de formation qui résout le plus grand défi pour entrer dans le domaine de la science des données – avoir toutes les ressources nécessaires en un seul endroit.
De plus, notre objectif est d’enseigner des sujets fluides et complémentaires. Le cours vous apprend tout ce que vous devez savoir pour devenir un data scientist à une fraction du coût des programmes traditionnels (sans parler du temps que vous gagnez).
Les compétences
1. Introduction aux données et à la science des données
Big data, business intelligence, business analytics, machine learning et intelligence artificielle. Nous savons que ces mots à la mode appartiennent au domaine de la science des données, mais que signifiant-ils tous ?
Pourquoi l’apprendre ?
En tant que candidat scientifique des données, vous devez comprendre les locataires et les aboutissants de chacun de ces domaines et reconnaître l’approche appropriée pour résoudre un problème. Cette « Introduction aux données et à la science des données » vous donnera un aperçu complet de tous ces mots à la mode et de leur place dans le domaine de la science des données.
  ;
  ; 2. Mathématiques
L’apprentissage des outils est la première étape pour faire de la science des données. Vous devez d’abord avoir une vue d’ensemble pour ensuite examiner les pièces en détail.
Nous examinons en détail le calcul différentiel et l’algèbre linéaire, car ce sont les sous-domaines sur lesquels reposent la science des données.
Part 1: Introduction
1
A Practical Example: What You Will Learn in This Course
2
What Does the Course Cover
3
Download All Resources and Important FAQ
The Field of Data Science - The Various Data Science Disciplines
1
Data Science and Business Buzzwords: Why are there so Many?
2
Data Science and Business Buzzwords: Why are there so Many?
3
What is the difference between Analysis and Analytics
4
What is the difference between Analysis and Analytics
5
Business Analytics, Data Analytics, and Data Science: An Introduction
6
Business Analytics, Data Analytics, and Data Science: An Introduction
7
Continuing with BI, ML, and AI
8
Continuing with BI, ML, and AI
9
A Breakdown of our Data Science Infographic
10
A Breakdown of our Data Science Infographic
The Field of Data Science - Connecting the Data Science Disciplines
1
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
2
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
The Field of Data Science - The Benefits of Each Discipline
1
The Reason Behind These Disciplines
2
The Reason Behind These Disciplines
The Field of Data Science - Popular Data Science Techniques
1
Techniques for Working with Traditional Data
2
Techniques for Working with Traditional Data
3
Real Life Examples of Traditional Data
4
Techniques for Working with Big Data
5
Techniques for Working with Big Data
6
Real Life Examples of Big Data
7
Business Intelligence (BI) Techniques
8
Business Intelligence (BI) Techniques
9
Real Life Examples of Business Intelligence (BI)
10
Techniques for Working with Traditional Methods
11
Techniques for Working with Traditional Methods
12
Real Life Examples of Traditional Methods
13
Machine Learning (ML) Techniques
14
Machine Learning (ML) Techniques
15
Types of Machine Learning
16
Types of Machine Learning
17
Real Life Examples of Machine Learning (ML)
18
Real Life Examples of Machine Learning (ML)
The Field of Data Science - Popular Data Science Tools
1
Necessary Programming Languages and Software Used in Data Science
2
Necessary Programming Languages and Software Used in Data Science
The Field of Data Science - Careers in Data Science
1
Finding the Job - What to Expect and What to Look for
2
Finding the Job - What to Expect and What to Look for
The Field of Data Science - Debunking Common Misconceptions
1
Debunking Common Misconceptions
2
Debunking Common Misconceptions
Part 2: Probability
1
The Basic Probability Formula
2
The Basic Probability Formula
3
Computing Expected Values
4
Computing Expected Values
5
Frequency
6
Frequency
7
Events and Their Complements
8
Events and Their Complements
Probability - Combinatorics
1
Fundamentals of Combinatorics
2
Fundamentals of Combinatorics
3
Permutations and How to Use Them
4
Permutations and How to Use Them
5
Simple Operations with Factorials
6
Simple Operations with Factorials
7
Solving Variations with Repetition
8
Solving Variations with Repetition
9
Solving Variations without Repetition
10
Solving Variations without Repetition
11
Solving Combinations
12
Solving Combinations
13
Symmetry of Combinations
14
Symmetry of Combinations
15
Solving Combinations with Separate Sample Spaces
16
Solving Combinations with Separate Sample Spaces
17
Combinatorics in Real-Life: The Lottery
18
Combinatorics in Real-Life: The Lottery
19
A Recap of Combinatorics
20
A Practical Example of Combinatorics
Probability - Bayesian Inference
1
Sets and Events
2
Sets and Events
3
Ways Sets Can Interact
4
Ways Sets Can Interact
5
Intersection of Sets
6
Intersection of Sets
7
Union of Sets
8
Union of Sets
9
Mutually Exclusive Sets
10
Mutually Exclusive Sets
11
Dependence and Independence of Sets
12
Dependence and Independence of Sets
13
The Conditional Probability Formula
14
The Conditional Probability Formula
15
The Law of Total Probability
16
The Additive Rule
17
The Additive Rule
18
The Multiplication Law
19
The Multiplication Law
20
Bayes' Law
21
Bayes' Law
22
A Practical Example of Bayesian Inference
Probability - Distributions
1
Fundamentals of Probability Distributions
2
Fundamentals of Probability Distributions
3
Types of Probability Distributions
4
Types of Probability Distributions
5
Characteristics of Discrete Distributions
6
Characteristics of Discrete Distributions
7
Discrete Distributions: The Uniform Distribution
8
Discrete Distributions: The Uniform Distribution
9
Discrete Distributions: The Bernoulli Distribution
10
Discrete Distributions: The Bernoulli Distribution
11
Discrete Distributions: The Binomial Distribution
12
Discrete Distributions: The Binomial Distribution
13
Discrete Distributions: The Poisson Distribution
14
Discrete Distributions: The Poisson Distribution
15
Characteristics of Continuous Distributions
16
Characteristics of Continuous Distributions
17
Continuous Distributions: The Normal Distribution
18
Continuous Distributions: The Normal Distribution
19
Continuous Distributions: The Standard Normal Distribution
20
Continuous Distributions: The Standard Normal Distribution
21
Continuous Distributions: The Students' T Distribution
22
Continuous Distributions: The Students' T Distribution
23
Continuous Distributions: The Chi-Squared Distribution
24
Continuous Distributions: The Chi-Squared Distribution
25
Continuous Distributions: The Exponential Distribution
26
Continuous Distributions: The Exponential Distribution
27
Continuous Distributions: The Logistic Distribution
28
Continuous Distributions: The Logistic Distribution
29
A Practical Example of Probability Distributions
Probability - Probability in Other Fields
1
Probability in Finance
2
Probability in Statistics
3
Probability in Data Science
Part 3: Statistics
1
Population and Sample
2
Population and Sample
Statistics - Descriptive Statistics
1
Types of Data
2
Types of Data
3
Levels of Measurement
4
Levels of Measurement
5
Categorical Variables - Visualization Techniques
6
Categorical Variables - Visualization Techniques
7
Categorical Variables Exercise
8
Numerical Variables - Frequency Distribution Table
9
Numerical Variables - Frequency Distribution Table
10
Numerical Variables Exercise
11
The Histogram
12
The Histogram
13
Histogram Exercise
14
Cross Tables and Scatter Plots
15
Cross Tables and Scatter Plots
16
Cross Tables and Scatter Plots Exercise
17
Mean, median and mode
18
Mean, Median and Mode Exercise
19
Skewness
20
Skewness
21
Skewness Exercise
22
Variance
23
Variance Exercise
24
Standard Deviation and Coefficient of Variation
25
Standard Deviation
26
Standard Deviation and Coefficient of Variation Exercise
27
Covariance
28
Covariance
29
Covariance Exercise
30
Correlation Coefficient
31
Correlation
32
Correlation Coefficient Exercise
Statistics - Practical Example: Descriptive Statistics
1
Practical Example: Descriptive Statistics
2
Practical Example: Descriptive Statistics Exercise
Statistics - Inferential Statistics Fundamentals
1
Introduction
2
What is a Distribution
3
What is a Distribution
4
The Normal Distribution
5
The Normal Distribution
6
The Standard Normal Distribution
7
The Standard Normal Distribution
8
The Standard Normal Distribution Exercise
9
Central Limit Theorem
10
Central Limit Theorem
11
Standard error
12
Standard Error
13
Estimators and Estimates
14
Estimators and Estimates
Statistics - Inferential Statistics: Confidence Intervals
1
What are Confidence Intervals?
2
What are Confidence Intervals?
3
Confidence Intervals; Population Variance Known; Z-score
4
Confidence Intervals; Population Variance Known; Z-score; Exercise
5
Confidence Interval Clarifications
6
Student's T Distribution
7
Student's T Distribution
8
Confidence Intervals; Population Variance Unknown; T-score
9
Confidence Intervals; Population Variance Unknown; T-score; Exercise
10
Margin of Error
11
Margin of Error
12
Confidence intervals. Two means. Dependent samples
13
Confidence intervals. Two means. Dependent samples Exercise
14
Confidence intervals. Two means. Independent Samples (Part 1)
15
Confidence intervals. Two means. Independent Samples (Part 1). Exercise
16
Confidence intervals. Two means. Independent Samples (Part 2)
17
Confidence intervals. Two means. Independent Samples (Part 2). Exercise
18
Confidence intervals. Two means. Independent Samples (Part 3)
Statistics - Practical Example: Inferential Statistics
1
Practical Example: Inferential Statistics
2
Practical Example: Inferential Statistics Exercise
Statistics - Hypothesis Testing
1
Null vs Alternative Hypothesis
2
Further Reading on Null and Alternative Hypothesis
3
Null vs Alternative Hypothesis
4
Rejection Region and Significance Level
5
Rejection Region and Significance Level
6
Type I Error and Type II Error
7
Type I Error and Type II Error
8
Test for the Mean. Population Variance Known
9
Test for the Mean. Population Variance Known Exercise
10
p-value
11
p-value
12
Test for the Mean. Population Variance Unknown
13
Test for the Mean. Population Variance Unknown Exercise
14
Test for the Mean. Dependent Samples
15
Test for the Mean. Dependent Samples Exercise
16
Test for the mean. Independent Samples (Part 1)
17
Test for the mean. Independent Samples (Part 1). Exercise
18
Test for the mean. Independent Samples (Part 2)
19
Test for the mean. Independent Samples (Part 2)
20
Test for the mean. Independent Samples (Part 2). Exercise
Statistics - Practical Example: Hypothesis Testing
1
Practical Example: Hypothesis Testing
2
Practical Example: Hypothesis Testing Exercise
Part 4: Introduction to Python
1
Introduction to Programming
2
Introduction to Programming
3
Why Python?
4
Why Python?
5
Why Jupyter?
6
Why Jupyter?
7
Installing Python and Jupyter
8
Understanding Jupyter's Interface - the Notebook Dashboard
9
Prerequisites for Coding in the Jupyter Notebooks
10
Jupyter's Interface
Python - Variables and Data Types
1
Variables
2
Variables
3
Numbers and Boolean Values in Python
4
Numbers and Boolean Values in Python
5
Python Strings
6
Python Strings
Python - Basic Python Syntax
1
Using Arithmetic Operators in Python
2
Using Arithmetic Operators in Python
3
The Double Equality Sign
4
The Double Equality Sign
5
How to Reassign Values
6
How to Reassign Values
7
Add Comments
8
Add Comments
9
Understanding Line Continuation
10
Indexing Elements
11
Indexing Elements
12
Structuring with Indentation
13
Structuring with Indentation
Python - Other Python Operators
1
Comparison Operators
2
Comparison Operators
3
Logical and Identity Operators
4
Logical and Identity Operators
Python - Conditional Statements
1
The IF Statement
2
The IF Statement
3
The ELSE Statement
4
The ELIF Statement
5
A Note on Boolean Values
6
A Note on Boolean Values
Python - Python Functions
1
Defining a Function in Python
2
How to Create a Function with a Parameter
3
Defining a Function in Python - Part II
4
How to Use a Function within a Function
5
Conditional Statements and Functions
6
Functions Containing a Few Arguments
7
Built-in Functions in Python
8
Python Functions
Python - Sequences
1
Lists
2
Lists
3
Using Methods
4
Using Methods
5
List Slicing
6
Tuples
7
Dictionaries
8
Dictionaries
Python - Iterations
1
For Loops
2
For Loops
3
While Loops and Incrementing
4
Lists with the range() Function
5
Lists with the range() Function
6
Conditional Statements and Loops
7
Conditional Statements, Functions, and Loops
8
How to Iterate over Dictionaries
Python - Advanced Python Tools
1
Object Oriented Programming
2
Object Oriented Programming
3
Modules and Packages
4
Modules and Packages
5
What is the Standard Library?
6
What is the Standard Library?
7
Importing Modules in Python
8
Importing Modules in Python
Part 5: Advanced Statistical Methods in Python
1
Introduction to Regression Analysis
2
Introduction to Regression Analysis
Advanced Statistical Methods - Linear Regression with StatsModels
1
The Linear Regression Model
2
The Linear Regression Model
3
Correlation vs Regression
4
Correlation vs Regression
5
Geometrical Representation of the Linear Regression Model
6
Geometrical Representation of the Linear Regression Model
7
Python Packages Installation
8
First Regression in Python
9
First Regression in Python Exercise
10
Using Seaborn for Graphs
11
How to Interpret the Regression Table
12
How to Interpret the Regression Table
13
Decomposition of Variability
14
Decomposition of Variability
15
What is the OLS?
16
What is the OLS
17
R-Squared
18
R-Squared
Advanced Statistical Methods - Multiple Linear Regression with StatsModels
1
Multiple Linear Regression
2
Multiple Linear Regression
3
Adjusted R-Squared
4
Adjusted R-Squared
5
Multiple Linear Regression Exercise
6
Test for Significance of the Model (F-Test)
7
OLS Assumptions
8
OLS Assumptions
9
A1: Linearity
10
A1: Linearity
11
A2: No Endogeneity
12
A2: No Endogeneity
13
A3: Normality and Homoscedasticity
14
A4: No Autocorrelation
15
A4: No autocorrelation
16
A5: No Multicollinearity
17
A5: No Multicollinearity
18
Dealing with Categorical Data - Dummy Variables
19
Dealing with Categorical Data - Dummy Variables
20
Making Predictions with the Linear Regression
Advanced Statistical Methods - Linear Regression with sklearn
1
What is sklearn and How is it Different from Other Packages
2
How are we Going to Approach this Section?
3
Simple Linear Regression with sklearn
4
Simple Linear Regression with sklearn - A StatsModels-like Summary Table
5
A Note on Normalization
6
Simple Linear Regression with sklearn - Exercise
7
Multiple Linear Regression with sklearn
8
Calculating the Adjusted R-Squared in sklearn
9
Calculating the Adjusted R-Squared in sklearn - Exercise
10
Feature Selection (F-regression)
11
A Note on Calculation of P-values with sklearn
12
Creating a Summary Table with P-values
13
Multiple Linear Regression - Exercise
14
Feature Scaling (Standardization)
15
Feature Selection through Standardization of Weights
16
Predicting with the Standardized Coefficients
17
Feature Scaling (Standardization) - Exercise
18
Underfitting and Overfitting
19
Train - Test Split Explained
Advanced Statistical Methods - Practical Example: Linear Regression
1
Practical Example: Linear Regression (Part 1)
2
Practical Example: Linear Regression (Part 2)
3
A Note on Multicollinearity
4
Practical Example: Linear Regression (Part 3)
5
Dummies and Variance Inflation Factor - Exercise
6
Practical Example: Linear Regression (Part 4)
7
Dummy Variables - Exercise
8
Practical Example: Linear Regression (Part 5)
9
Linear Regression - Exercise
Advanced Statistical Methods - Logistic Regression
1
Introduction to Logistic Regression
2
A Simple Example in Python
3
Logistic vs Logit Function
4
Building a Logistic Regression
5
Building a Logistic Regression - Exercise
6
An Invaluable Coding Tip
7
Understanding Logistic Regression Tables
8
Understanding Logistic Regression Tables - Exercise
9
What do the Odds Actually Mean
10
Binary Predictors in a Logistic Regression
11
Binary Predictors in a Logistic Regression - Exercise
12
Calculating the Accuracy of the Model
13
Calculating the Accuracy of the Model
14
Underfitting and Overfitting
15
Testing the Model
16
Testing the Model - Exercise
Advanced Statistical Methods - Cluster Analysis
1
Introduction to Cluster Analysis
2
Some Examples of Clusters
3
Difference between Classification and Clustering
4
Math Prerequisites
Advanced Statistical Methods - K-Means Clustering
1
K-Means Clustering
2
A Simple Example of Clustering
3
A Simple Example of Clustering - Exercise
4
Clustering Categorical Data
5
Clustering Categorical Data - Exercise
6
How to Choose the Number of Clusters
7
How to Choose the Number of Clusters - Exercise
8
Pros and Cons of K-Means Clustering
9
To Standardize or not to Standardize
10
Relationship between Clustering and Regression
11
Market Segmentation with Cluster Analysis (Part 1)
12
Market Segmentation with Cluster Analysis (Part 2)
13
How is Clustering Useful?
14
EXERCISE: Species Segmentation with Cluster Analysis (Part 1)
15
EXERCISE: Species Segmentation with Cluster Analysis (Part 2)
Advanced Statistical Methods - Other Types of Clustering
1
Types of Clustering
2
Dendrogram
3
Heatmaps
Part 6: Mathematics
1
What is a Matrix?
2
What is a Matrix?
3
Scalars and Vectors
4
Scalars and Vectors
5
Linear Algebra and Geometry
6
Linear Algebra and Geometry
7
Arrays in Python - A Convenient Way To Represent Matrices
8
What is a Tensor?
9
What is a Tensor?
10
Addition and Subtraction of Matrices
11
Addition and Subtraction of Matrices
12
Errors when Adding Matrices
13
Transpose of a Matrix
14
Dot Product
15
Dot Product of Matrices
16
Why is Linear Algebra Useful?
Part 7: Deep Learning
1
What to Expect from this Part?
Deep Learning - Introduction to Neural Networks
1
Introduction to Neural Networks
2
Introduction to Neural Networks
3
Training the Model
4
Training the Model
5
Types of Machine Learning
6
Types of Machine Learning
7
The Linear Model (Linear Algebraic Version)
8
The Linear Model
9
The Linear Model with Multiple Inputs
10
The Linear Model with Multiple Inputs
11
The Linear model with Multiple Inputs and Multiple Outputs
12
The Linear model with Multiple Inputs and Multiple Outputs
13
Graphical Representation of Simple Neural Networks
14
Graphical Representation of Simple Neural Networks
15
What is the Objective Function?
16
What is the Objective Function?
17
Common Objective Functions: L2-norm Loss
18
Common Objective Functions: L2-norm Loss
19
Common Objective Functions: Cross-Entropy Loss
20
Common Objective Functions: Cross-Entropy Loss
21
Optimization Algorithm: 1-Parameter Gradient Descent
22
Optimization Algorithm: 1-Parameter Gradient Descent
23
Optimization Algorithm: n-Parameter Gradient Descent
24
Optimization Algorithm: n-Parameter Gradient Descent
Deep Learning - How to Build a Neural Network from Scratch with NumPy
1
Basic NN Example (Part 1)
2
Basic NN Example (Part 2)
3
Basic NN Example (Part 3)
4
Basic NN Example (Part 4)
5
Basic NN Example Exercises
Deep Learning - TensorFlow 2.0: Introduction
1
How to Install TensorFlow 2.0
2
TensorFlow Outline and Comparison with Other Libraries
3
TensorFlow 1 vs TensorFlow 2
4
A Note on TensorFlow 2 Syntax
5
Types of File Formats Supporting TensorFlow
6
Outlining the Model with TensorFlow 2
7
Interpreting the Result and Extracting the Weights and Bias
8
Customizing a TensorFlow 2 Model
9
Basic NN with TensorFlow: Exercises
Deep Learning - Digging Deeper into NNs: Introducing Deep Neural Networks
1
What is a Layer?
2
What is a Deep Net?
3
Digging into a Deep Net
4
Non-Linearities and their Purpose
5
Activation Functions
6
Activation Functions: Softmax Activation
7
Backpropagation
8
Backpropagation Picture
9
Backpropagation - A Peek into the Mathematics of Optimization
Deep Learning - Overfitting
1
What is Overfitting?
2
Underfitting and Overfitting for Classification
3
What is Validation?
4
Training, Validation, and Test Datasets
5
N-Fold Cross Validation
6
Early Stopping or When to Stop Training
Deep Learning - Initialization
1
What is Initialization?
2
Types of Simple Initializations
3
State-of-the-Art Method - (Xavier) Glorot Initialization
Deep Learning - Digging into Gradient Descent and Learning Rate Schedules
1
Stochastic Gradient Descent
2
Problems with Gradient Descent
3
Momentum
4
Learning Rate Schedules, or How to Choose the Optimal Learning Rate
5
Learning Rate Schedules Visualized
6
Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
7
Adam (Adaptive Moment Estimation)
Deep Learning - Preprocessing
1
Preprocessing Introduction
2
Types of Basic Preprocessing
3
Standardization
4
Preprocessing Categorical Data
5
Binary and One-Hot Encoding
Deep Learning - Classifying on the MNIST Dataset
1
MNIST: The Dataset
2
MNIST: How to Tackle the MNIST
3
MNIST: Importing the Relevant Packages and Loading the Data
4
MNIST: Preprocess the Data - Create a Validation Set and Scale It
5
MNIST: Preprocess the Data - Scale the Test Data - Exercise
6
MNIST: Preprocess the Data - Shuffle and Batch
7
MNIST: Preprocess the Data - Shuffle and Batch - Exercise
8
MNIST: Outline the Model
9
MNIST: Select the Loss and the Optimizer
10
MNIST: Learning
11
MNIST - Exercises
12
MNIST: Testing the Model
Deep Learning - Business Case Example
1
Business Case: Exploring the Dataset and Identifying Predictors
2
Business Case: Outlining the Solution
3
Business Case: Balancing the Dataset
4
Business Case: Preprocessing the Data
5
Business Case: Preprocessing the Data - Exercise
6
Business Case: Load the Preprocessed Data
7
Business Case: Load the Preprocessed Data - Exercise
8
Business Case: Learning and Interpreting the Result
9
Business Case: Setting an Early Stopping Mechanism
10
Setting an Early Stopping Mechanism - Exercise
11
Business Case: Testing the Model
12
Business Case: Final Exercise
Deep Learning - Conclusion
1
Summary on What You've Learned
2
What's Further out there in terms of Machine Learning
3
DeepMind and Deep Learning
4
An overview of CNNs
5
An Overview of RNNs
6
An Overview of non-NN Approaches
Appendix: Deep Learning - TensorFlow 1: Introduction
1
READ ME!!!!
2
How to Install TensorFlow 1
3
A Note on Installing Packages in Anaconda
4
TensorFlow Intro
5
Actual Introduction to TensorFlow
6
Types of File Formats, supporting Tensors
7
Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases
8
Basic NN Example with TF: Loss Function and Gradient Descent
9
Basic NN Example with TF: Model Output
10
Basic NN Example with TF Exercises
Appendix: Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset
1
MNIST: What is the MNIST Dataset?
2
MNIST: How to Tackle the MNIST
3
MNIST: Relevant Packages
4
MNIST: Model Outline
5
MNIST: Loss and Optimization Algorithm
6
Calculating the Accuracy of the Model
7
MNIST: Batching and Early Stopping
8
MNIST: Learning
9
MNIST: Results and Testing
10
MNIST: Exercises
11
MNIST: Solutions
Appendix: Deep Learning - TensorFlow 1: Business Case
1
Business Case: Getting Acquainted with the Dataset
2
Business Case: Outlining the Solution
3
The Importance of Working with a Balanced Dataset
4
Business Case: Preprocessing
5
Business Case: Preprocessing Exercise
6
Creating a Data Provider
7
Business Case: Model Outline
8
Business Case: Optimization
9
Business Case: Interpretation
10
Business Case: Testing the Model
11
Business Case: A Comment on the Homework
12
Business Case: Final Exercise
Software Integration
1
What are Data, Servers, Clients, Requests, and Responses
2
What are Data, Servers, Clients, Requests, and Responses
3
What are Data Connectivity, APIs, and Endpoints?
4
What are Data Connectivity, APIs, and Endpoints?
5
Taking a Closer Look at APIs
6
Taking a Closer Look at APIs
7
Communication between Software Products through Text Files
8
Communication between Software Products through Text Files
9
Software Integration - Explained
10
Software Integration - Explained
Case Study - What's Next in the Course?
1
Game Plan for this Python, SQL, and Tableau Business Exercise
2
The Business Task
3
Introducing the Data Set
4
Introducing the Data Set
Case Study - Preprocessing the 'Absenteeism_data'
1
What to Expect from the Following Sections?
2
Importing the Absenteeism Data in Python
3
Checking the Content of the Data Set
4
Introduction to Terms with Multiple Meanings
5
What's Regression Analysis - a Quick Refresher
6
Using a Statistical Approach towards the Solution to the Exercise
7
Dropping a Column from a DataFrame in Python
8
EXERCISE - Dropping a Column from a DataFrame in Python
9
SOLUTION - Dropping a Column from a DataFrame in Python
10
Analyzing the Reasons for Absence
11
Obtaining Dummies from a Single Feature
12
EXERCISE - Obtaining Dummies from a Single Feature
13
SOLUTION - Obtaining Dummies from a Single Feature
14
Dropping a Dummy Variable from the Data Set
15
More on Dummy Variables: A Statistical Perspective
16
Classifying the Various Reasons for Absence
17
Using .concat() in Python
18
EXERCISE - Using .concat() in Python
19
SOLUTION - Using .concat() in Python
20
Reordering Columns in a Pandas DataFrame in Python
21
EXERCISE - Reordering Columns in a Pandas DataFrame in Python
22
SOLUTION - Reordering Columns in a Pandas DataFrame in Python
23
Creating Checkpoints while Coding in Jupyter
24
EXERCISE - Creating Checkpoints while Coding in Jupyter
25
SOLUTION - Creating Checkpoints while Coding in Jupyter
26
Analyzing the Dates from the Initial Data Set
27
Extracting the Month Value from the "Date" Column
28
Extracting the Day of the Week from the "Date" Column
29
EXERCISE - Removing the "Date" Column
30
Analyzing Several "Straightforward" Columns for this Exercise
31
Working on "Education", "Children", and "Pets"
32
Final Remarks of this Section
33
A Note on Exporting Your Data as a *.csv File
Case Study - Applying Machine Learning to Create the 'absenteeism_module'
1
Exploring the Problem with a Machine Learning Mindset
2
Creating the Targets for the Logistic Regression
3
Selecting the Inputs for the Logistic Regression
4
Standardizing the Data
5
Splitting the Data for Training and Testing
6
Fitting the Model and Assessing its Accuracy
7
Creating a Summary Table with the Coefficients and Intercept
8
Interpreting the Coefficients for Our Problem
9
Standardizing only the Numerical Variables (Creating a Custom Scaler)
10
Interpreting the Coefficients of the Logistic Regression
11
Backward Elimination or How to Simplify Your Model
12
Testing the Model We Created
13
Saving the Model and Preparing it for Deployment
14
ARTICLE - A Note on 'pickling'
15
EXERCISE - Saving the Model (and Scaler)
16
Preparing the Deployment of the Model through a Module
Case Study - Loading the 'absenteeism_module'
1
Are You Sure You're All Set?
2
Deploying the 'absenteeism_module' - Part I
3
Deploying the 'absenteeism_module' - Part II
4
Exporting the Obtained Data Set as a *.csv
Case Study - Analyzing the Predicted Outputs in Tableau
1
EXERCISE - Age vs Probability
2
Analyzing Age vs Probability in Tableau
3
EXERCISE - Reasons vs Probability
4
Analyzing Reasons vs Probability in Tableau
5
EXERCISE - Transportation Expense vs Probability
6
Analyzing Transportation Expense vs Probability in Tableau
Appendix - Additional Python Tools
1
Using the .format() Method
2
Iterating Over Range Objects
3
Introduction to Nested For Loops
4
Triple Nested For Loops
5
List Comprehensions
6
Anonymous (Lambda) Functions
Appendix - pandas Fundamentals
1
Introduction to pandas Series
2
Working with Methods in Python - Part I
3
Working with Methods in Python - Part II
4
Parameters and Arguments in pandas
5
Using .unique() and .nunique()
6
Using .sort_values()
7
Introduction to pandas DataFrames - Part I
8
Introduction to pandas DataFrames - Part II
9
pandas DataFrames - Common Attributes
10
Data Selection in pandas DataFrames
11
pandas DataFrames - Indexing with .iloc[]
12
pandas DataFrames - Indexing with .loc[]
Bonus Lecture
1
Bonus Lecture: Next Steps
