The Data Science Course 2019: Complete Data Science Bootcamp

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The Data Science Course 2019: Complete Data Science Bootcamp

What you will learn

    The This course will give you all the tools necessary to become a data analyst
You can fill your resume with data science skills that are in high demand: Statistical analysis and Python programming with NumPy. Seaborn, Seaborn, Machine Learning with Stats Models and scikit–learn. Deep Learning with TensorFlow
Show your knowledge of data science to impress interviewers
Learn how to preprocess data
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
Learn how to code in Python for statistical analysis.
Python allows you to perform logistic and linear regressions.
Perform cluster and factor analyses
You will be able to create Machine Learning algorithms using Python, NumPy and statsmodels.
Use your business skills in real-world 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
Deep neural networks are the secret weapon to unlock the power of deep learning
Machine Learning algorithms can be improved by studying overfitting and underfitting, validation, nfold cross validation, testing, as well as how hyperparameters might improve performance
Get your fingers ready to use the knowledge you’ve gained here in real-life situations.

Download it immediately The Data Science Course 2019: Complete Data Science Bootcamp

Course Content
Expand all 471 Lectures28:52:43
–Part 1: Introduction
19:20
Here’s a practical example: This is what you will learn Course
Preview
05:05
What is the Meaning of the Course Cover
Preview
03:34
Download all resources and important FAQ
10:41
–The Field of Data Science – The Various Data Science Disciplines
31:11
Data Science Business Buzzwords: Why so many?
Preview
05:21
Data Science Business Buzzwords: Why so many?
1 question
What is the difference in Analytics and Analysis?
03:50
What is the difference in Analytics and Analysis?
1 question
Analytics for Business Data Analytics Data ScienceAn Introduction
Preview
08:26
Analytics for Business Data Analytics Data ScienceIntroduction
3 questions
Continuing on with BI, ML and AI
09:31
Continuing on with BI, ML and AI
2 questions
Our Breakdown Data Science Infographic
04:03
Our Breakdown Data Science Infographic
1 question
–The Field of Data Science Connecting the Data Science Disciplines
07:19
Applying Traditional DataBig, Big DataBI, Traditional Data Science ML
07:19
Applying Traditional DataBig, Big DataBI, Traditional Data Science ML
1 question
–The Field of Data Science – The Each discipline has its own benefits
04:44
The These Disciplines: Reasons
04:44
The These Disciplines: Reasons
1 question
–The Field of Data Science – Popular Data Science Techniques
53:34
Techniques to Work with Traditional Data
08:13
Techniques to Work with Traditional Data
1 question
Traditional methods in real life Data
01:44
Techniques for working with big Data
04:26
Techniques for working with big Data
1 question
Real Life Examples: Big Ideas in Real Life Data
01:32
Techniques for Business Intelligence (BI),
06:45
Business Intelligence (BI), Techniques
4 questions
Real-life Examples of Business Intelligence.
01:42
Techniques for working with traditional methods
09:08
Techniques for working with traditional methods
4 questions
Real life examples of traditional methods
02:45
Machine Learning (ML) Techniques
06:55
Machine Learning (ML) Techniques
2 questions
Different types of machine learning
08:13
Different types of machine learning
2 questions
Machine Learning (ML) in Action
02:11
Machine Learning (ML), as it happens in real life
5 questions
–The Field of Data Science – Popular Data Science Tools
05:51
Necessary programming languages and software used in Data Science
05:51
Necessary programming languages and software used in Data Science
4 questions
–The Field of Data Science Careers Data Science
03:29
How to Find the Job: What to Expect and What To Look For
03:29
How to Find the Job: What to Expect and What To Look For
1 question
–The Field of Data Science – Debunking Common Misconceptions
04:10
Common Misconceptions Busted
04:10
Common Misconceptions Busted
1 question
–Part 2: Probability
23:04
The Basic Probability Formula
07:09
The Basic Probability Formula
3 questions
Calculating Expected Values
05:29
Calculating Expected Values
3 questions
Frequency
05:00
Frequency
3 questions
Events and their complements
05:26
Events and their Complements
3 questions
–Probability – Combinatorics
42:56
Combinatorics Fundamentals
01:04
Combinatorics Fundamentals
1 question
Permutations and how to use them
03:21
How to use them and permutations
2 questions
Simple operations with Factorials
03:35
Simple operations using Factorials
3 questions
Repetition is a way to solve variations
02:59
Solving Variations by Repetition
3 questions
Without Repetition, Solve Variations
03:48
Without Repetition, Solving Variations
3 questions
Solving Combinations
04:51
Solving Combinations
4 questions
Symmetry of Combinations
03:26
Symmetry of Combinations
1 question
Solving Combinations Using Separate Sample Spaces
02:52
Solving Combinations Using Separate Sample Spaces
1 question
Combinatorics in Real Life: The Lottery
03:12
Combinatorics in Real Life: The Lottery
1 question
Recap of Combinatorics
02:55
Combinatorics: A Practical Example
10:53
–Probability – Bayesian Inference
54:38
Events and sets
04:25
Sets and Events
3 questions
There are many ways sets can interact
03:45
There are many ways sets can interact
2 questions
Intersection of sets
02:06
Intersection of sets
3 questions
Union of Sets
04:51
Union of Sets
3 questions
Sets that are mutually exclusive
02:09
Sets that are mutually exclusive
4 questions
Dependence and independence of sets
03:01
Independence of Sets and Dependence
3 questions
The Formula for Conditional Probability
04:16
The Formula for Conditional Probability
3 questions
The Law of Total Probability
03:03
The Rule of Addition
02:21
The Rule of Addition
2 questions
The Multiplication Law
04:05
The Multiplication Law
2 questions
Bayes’ Law
05:44
Bayes’ Law
2 questions
Bayesian Inference: A Practical Example
14:52
–Probability – Distributions
01:17:12
The Fundamentals of Probability Distributions
06:29
The Fundamentals of Probability Distributions
3 questions
Types and types of probability distributions
07:32
Types and types of probability distributions
2 questions
Characteristics for Discrete Distributions
02:00
Characteristics for Discrete Distributions
2 questions
Discrete Distributions The Uniform Distribution
02:13
Discrete Distributions The Uniform Distribution
2 questions
Discrete Distributions The Bernoulli Distribution
03:26
Discrete Distributions The Bernoulli Distribution
1 question
Discrete Distributions The Binomial Distribution
07:04
Discrete Distributions The Binomial Distribution
1 question
Discrete Distributions The Poisson Distribution
05:27
Discrete Distributions The Poisson Distribution
1 question
Characteristics of Continuous Distributions
07:12
Characteristics of Continuous Distributions
1 question
Continuous Distributions The Normal Distribution
04:08
Continuous Distributions The Normal Distribution
1 question
Continuous Distributions The Normal distribution
04:25
Continuous Distributions The Normal distribution
1 question
Continuous Distributions The T distribution for students
02:29
Continuous Distributions The T distribution for students
1 question
Continuous Distributions The Chi-Squared Distribution
02:22
Continuous Distributions The Chi-Squared Distribution
1 question
Continuous Distributions The Exponential Distribution
03:15
Continuous Distributions The Exponential Distribution
1 question
Continuous Distributions The Logistic Distribution
04:07
Continuous Distributions The Logistic Distribution
1 question
A Practical Example Of Probability Distributions
15:03
–Probability – Probability in Other Fields
18:51
Probability in Finance
07:46
Statistics: Probability
06:18
Probability Data Science
04:47
–Part 3: Statistics
04:02
Population and Sample
04:02
Population and Sample
2 questions
–Statistics – Descriptive Statistics
48:11
Types Data
04:33
Types Data
2 questions
Different levels of measurement
03:43
Different levels of measurement
2 questions
Categorical Variables – Visualization Techniques
Preview
04:52
Categorical Variables – Visualization Techniques
1 question
Categorical Variables Exercise
00:03
Frequency Distribution Table for Numerical Variables
03:09
Numerical Variables- Frequency Distribution Tab
1 question
Numerical Variables Exercise
00:03
The Histogram
02:14
The Histogram
1 question
Histogram Exercise
00:03
Cross Tables and Scatter plots
04:44
Cross Tables and Scatter plots
1 question
Cross Tables & Scatter Plots Exercise
00:03
Median, mean and mode
04:20
Exercises for Mean, Median, Mode
00:03
Skewness
02:37
Skewness
1 question
Skewness Exercise
00:03
Variance
05:55
Variance Exercise
00:15
Standard Deviation, Coefficient of Variation
04:40
Standard Deviation
1 question
Standard Deviation, Coefficient of Variation and Exercise
00:03
Covariance
03:23
Covariance
1 question
Exercise in Covariance
00:03
Correlation Coefficient
03:17
Correlation
1 question
Correlation Coefficient Exercise
00:03
–Statistics – A practical example: Descriptive Statistics
16:18
Practical Example: Descriptive Statistics
Preview
16:15
Exercise in descriptive statistics
00:03
–Statistics – Inferential Statistics Fundamentals
21:53
Introduction
01:00
What is Distribution?
04:33
What is Distribution?
1 question
The Normal Distribution
03:54
The Normal Distribution
1 question
The Normal distribution
03:30
The Normal distribution
1 question
The Normal Distribution Exercise
00:03
Theorem of the Central Limit
04:20
Theorem of the Central Limit
1 question
Standard error
01:26
Standard Error
1 question
Estimators & Estimates
03:07
Estimators & Estimates
1 question
–Statistics – Inferential Statistics: Confidence Intervals
44:25
What are Confidence Intervals and How Do They Work?
02:41
What are Confidence Intervals and How Do They Work?
1 question
Confidence Intervals, Population Variance Known; Z-score
08:01
Confidence Intervals and Population Variance Known. z-score. Exercise
00:03
Clarifications regarding Confidence Interval Clarifications
04:38
T distribution for students
03:22
Distribution of T-Shirts for Students
1 question
Confidence Intervals, Population Variance Unknown; T-score
04:36
Confidence Intervals, Population Variance Unknown; T-score; Exercise
00:03
Margin of Error
04:52
Margin of Error
1 question
Confidence intervals Two ways. Dependent samples
06:04
Confidence intervals Two methods Dependent samples Exercise
00:03
Confidence intervals Two methods. Part 1: Independent samples
04:31
Confidence intervals Two ways. Independent samples (Part 1) Exercise
00:03
Confidence intervals Two methods. Part 2: Independent samples
03:57
Confidence intervals Two ways. Independent samples (Part 2) Exercise
00:03
Confidence intervals Two means Two means. (Part 3)
01:27
–Statistics – Practical example: Inferential statistics
10:08
Practical Example: Inferential Statistics
10:05
Practical Example: Inferential Statistic Exercise
00:03
–Statistics – Hypothesis Testing
48:24
Alternative Hypothesis vs. Null
Preview
05:51
Additional reading on Null Hypothesis
01:16
Alternative Hypothesis vs. null
2 questions
Significance and Rejection Zone
07:05
Rejection Zone and Significance level
2 questions
Type I Error, Type II Error
04:14
Type I Error, Type II Error
4 questions
The Mean Test. Variances in Population
06:34
Find the Mean Population Variances Known Exercise
00:03
p-value
04:13
p-value
4 questions
The Mean Test Unknown Variance in Population
04:48
Find the Mean Variance in the Population Unknown Exercise
00:03
The Mean Test Dependent Samples
05:18
Check for the Mean. Dependent Samples Exercise
00:03
The mean is to be tested. Test for the mean with independent samples (Part 1).
04:22
The mean is to be tested. Test for the mean using independent samples (Part 1). Exercise
00:03
The mean is to be tested. Part 2: Independent samples
04:26
The mean is to be tested. Part 2: Independent samples
1 question
The mean is to be tested. Independent samples (Part 2) Exercise
00:03
–Statistics – Practical Example: Hypothesis Testing
07:19
Practical Example: Hypothesis Testing
07:16
Practical example: Hypothesis Testing Exercise
00:03
–Part 4: Introduction to Python
32:49
Introduction to Programming
05:04
Introduction to Programming
2 questions
Why Python?
05:11
Why Python?
2 questions
Why Jupyter
03:29
Why Jupyter
2 questions
Installation of Python and Jupyter
06:49
Understanding Jupyter’s Interface: The Notebook Dashboard
03:15
Prerequisites for Coding with the Jupyter Notebooks
06:15
Interface by Jupyter
3 questions
Python 2 vs Python3
02:46
–Python – Variables and Data Types
19:17
Variables
04:52
Variables
1 question
Python numbers and Boolean values
03:05
Python numbers and Boolean values
1 question
Python Strings
11:20
Python Strings
3 questions
–Python – Basic Python Syntax
15:13
Use Arithmetic Operators with Python
03:23
Use Arithmetic Operators with Python
1 question
The Sign of double equality
01:33
The Sign of double equality
1 question
How to Reassign Valuables
01:08
How to Reassign Valuables
1 question
Comment
03:20
Add comments
1 question
Understanding Line Continuation
00:49

Get your instant download The Data Science Course 2019: Complete Data Science Bootcamp
Indexing Elements
01:18
Indexing Elements
1 question
Structuring with Indentation
03:42
Structuring with Indentation
1 question
–Python – Other Python Operators
07:45
Comparison Operators
02:10
Comparison Operators
2 questions
Logical and identity operators
05:35
Logical and identity operators
2 questions
–Python – Conditional Statements
27:44
The IF Statement
06:13
The Statement on IF
1 question
The ELSE Statement
05:37
The ELIF Statement
11:16
A Note on Boolean values
04:38
A Note on Boolean values
1 question
–Python – Python Functions
29:26
Definition of Functions in Python
04:20
How to Create Functions with Parameters
07:58
Part II: Defining Python Functions
05:29
How to Use a Function Within a Function
01:49
Conditional Statements & Functions
03:06
A Few Arguments are Included in Functions
02:48
Built-in Functions for Python
03:56
Python Functions
2 questions
–Python – Sequences
34:49
Listes
08:18
Listes
1 question
Use of Methods
06:54
Use of Methods
1 question
List Slicing
04:30
Tuples
06:40
Dictionaries
08:27
Dictionaries
1 question
–Python – Iterations
32:30
For loops
Preview
05:40
For loops
1 question
While loops and incrementing
05:10
Lists with the range() Function
06:22
Lists with this range() Function
1 question
Loops and Conditional Statements
06:30
Loops, Conditional Statements, and Functions
02:27
How to Iterate Using Dictionaries
06:21
–Python – Advanced Python Tools
12:56
Object-Oriented Programming
05:00
Object-Oriented Programming
2 questions
Modules and packages
01:05
Modules and packages
2 questions
What is the Standard Library exactly?
02:47
What is the Standard Library exactly?
1 question
Importing Modules into Python
04:04
Importing Modules into Python
2 questions
–Part 5: Advanced Statistical Methods in Python
01:27
Introduction to Regression Analysis
01:27
Introduction to Regression Analysis
1 question
–Advanced Statistical Methods – Linear regression with StatsModels
40:55
The Linear Regression Model
05:50
The Linear Regression Model
2 questions
Correlation vs. Regression
01:43
Correlation vs. Regression
1 question
Geometrical Representation Of The Linear Regression Model
01:25
Geometrical Representation Of The Linear Regression Model
1 question
Python Packages Installation
04:39
First Regression in Python
07:11
Exercise: First Regression in Python
00:39
Seaborn for graphs
01:21
How to interpret the regression table
05:47
How to interpret the regression table
3 questions
Variability Decomposition
03:37
Decomposition Variability
1 question
What is the OLS (Optional Life Support)?
03:13
What is the OLS?
1 question
R-Squared
05:30
R-Squared
2 questions
–Advanced Statistical Methods – Multiple Linear Regression with StatsModels
42:18
Multiple Linear Regression
02:55
Multiple Linear Regression
1 question
Adjusted R-Squared
06:00
Adjusted R-Squared
3 questions
Multiple Linear Regression Exercise
00:03
Test for Significance (F-Test).
02:01
OLS Assumptions
02:21
OLS Assumptions
1 question
A1: Linearity
01:50
A1: Linearity
2 questions
A2: No Endogeneity
04:09
A2: No Endogeneity
1 question
A3: Normality & Homoscedasticity
05:47
A4: There is no autocorrelation
03:31
A4: There is no autocorrelation
2 questions
A5: No Multicollinearity
03:26
A5: No Multicollinearity
1 question
Dealing with Categorical Data – Dummy Variables
06:43
Dealing with Categorical Data – Dummy Variables
00:03
Linear Regression allows for prediction
03:29
–Advanced Statistical Methods – Linear Regression with sklearn
54:27
What is sklearn, and how does it differ from other packages?
02:14
How will you approach this section?
01:56
Simple Linear regression with sklearn
Preview
05:38
Simple Linear regression with sklearn- A StatsModels like Summary Table
Preview
04:49
A Note on Normalization
00:09
Simple Linear Regression using sklearn – Exercise
00:03
Multiple Linear Regression using sklearn
03:10
Calculating the Adjusted R Squared using sklearn
04:45
Calculating the Adjusted Squared in sklearn-Exercise
00:03
F-regression: Feature Selection
04:41
Note: Calculation of P-values Using sklearn
00:13
Create a Summary Table with the p-values
02:10
Multiple Linear Regression – Exercise
00:03
Feature Scaling (Standardization)
05:38
Standardization of Weights to Allow for Feature Selection
05:22
Predicting with Standardized Coefficients
03:53
Feature Scaling (Standardization) – Exercise
00:03
Both underfitting and overfitting
02:42
The Train-Test Split explained
06:54
–Advanced Statistical Methods – Practical Example: Linear Regression
37:58
Practical example: Linear regression (Part 1).
11:59
Practical example: Linear regression (Part 2)
06:12
A Note on Multicollinearity
00:14
Practical Example: Linear regression (Part 3)
03:15
Exercise for Variance Inflation Factors and Dummies
00:03
Practical Example: Linear regression (Part 4)
08:10
Dummy Variables – Exercise
00:14
Practical Example: Linear regression (Part 5)
07:34
Linear Regression – Exercise
00:16
–Advanced Statistical Methods – Logistic Regression
40:49
Introduction to Logistic Regression
01:19
A simple example in Python
04:42
Logistic vs. logit Function
04:00
Logistic regression: Building a logistic regression
02:48
Building a Logistic regression – Exercise
00:03
A valuable tip for Coding
02:26
Understanding Logistic regression tables
04:06
Exercise: Understanding Logistic Regression Tables
00:03
What are the Odds Actually Mean
04:30
Binary Predictors in Logistic Regression
04:32
Binary Predictors in Logistic Regression – Exercise
00:03
Calculating the accuracy
03:21
Calculating the accuracy of the model
00:03
Both underfitting and overfitting
03:43
The Model is being tested
05:05
Exercise: Testing the Model
00:03
–Advanced Statistical Methods – Cluster Analysis
14:03
Introduction to Cluster Analysis
03:41
Here are some examples of clusters
04:31
There is a difference between Clustering and Classification
02:32
Prerequisites for Math
03:19
–Advanced Statistical Methods – K-Means Clustering
49:01
K-Means Clustering
04:41
One simple example of clustering
07:48
One Simple Example of Clustering – Exercise
00:03
Clustering Categorical Data
02:50
Clustering Categorical Data – Exercise
00:03
How to choose the number of Clusters
06:11
Exercise: How to choose the number of clusters
00:03
K-Means clustering: Pros and cons
03:23
To standardize or not to standardize
04:32
Relationship between clustering and regression
01:31
Market Segmentation and Cluster Analysis (Part 1)
06:03
Part 2: Market Segmentation using Cluster Analysis
06:58
How can clustering be useful?
04:47
EXERCISE – Species Segmentation and Cluster Analysis (Part 1)
00:03
EXERCISE Part 2: Species Segmentation by Cluster Analysis
00:03
–Advanced Statistical Methods – Other Different types of clustering
13:34
Types of Clustering
03:39
Dendrogram
05:21
Heatmaps
Preview
04:34
–Part 6: Mathematics
51:01
What is a matrix?
03:37
What is a Matrix?
6 questions
Scalars and vectors
02:58
Scalars and vectors
5 questions
Geometry and Linear Algebra
03:06
Geometry and Linear Algebra
3 questions
Python Arrays – A Convenient Method To Represent Matrixes
05:09
What is a Tensor?
03:00
What is a Tensor?
2 questions
Addition and subtraction of Matrices
03:36
Addition and subtraction of Matrices
3 questions
Addition of Matrices: Errors
02:01
Transpose a Matrix
05:13
Dot Product
03:48
Dot Product of Matrixes
08:23
What is Linear Algebra?
10:10
–Part 7: Deep Learning
03:07
What are you to expect from this Part?
03:07
What is Machine Learning?
4 questions
–Deep Learning – Introduction to Neural Networks
42:38
Introduction to Neural Networks
04:09
Introduction to Neural Networks
1 question
Training the Model
02:54
Training the Model
3 questions
Different types of machine learning
03:43

Download it immediately The Data Science Course 2019: Complete Data Science Bootcamp
Different types of machine learning
4 questions
The Linear Model (Linear Algebraic Version)
03:08
The Linear Model
2 questions
The Linear model with multiple inputs
02:25
The Linear model with multiple inputs
2 questions
The Linear model with multiple inputs, and multiple outputs
04:25
The Linear model with multiple inputs, and multiple outputs
3 questions
Simple Neural Networks: Graphical representation
01:47
Simple Neural Networks: Graphical representation
1 question
What is the Objective Function?
01:27
What is the Objective Function of the Function?
2 questions
Common Objective Functions: L2-norm loss
02:04
Common Objective Functions: L2-norm loss
3 questions
Common Objective Functions – Cross-Entropy Loss
03:55
Cross-Entropy Loss is one of the Common Objective Functions
4 questions
Optimization Algorithm: 1-Parameter Gradient Descend
06:33
Optimization Algorithm: 1-Parameter Gradient Descend
4 questions
Optimization Algorithm – n-Parameter Gradient Descend
06:08
Optimization Algorithm – n-Parameter Gradient Descend
3 questions
–Deep Learning – How to Build a Neural Network from Scratch with NumPy
20:35
Basic NN Example (Part 1).
03:06
Basic NN Example (Part 2
04:58
Basic NN Example (Part 3)
03:25
Basic NN Example Part 4
08:15
Basic NN Examples
00:51
–Deep Learning – TensorFlow 2.0: Introduction
28:10
How to install TensorFlow 2.0
05:02
Comparison of TensorFlow Outline with Other Libraries
03:28
TensorFlow 1 or TensorFlow2
02:33
Note on TensorFlow2 Syntax
00:58
Types Of File Formats That Support TensorFlow
02:34
TensorFlow 2 is used to outline the model
05:48
Interpreting the Results and Extracting the Weights & Bias
04:09
Customizing a TensorFlow2 model
02:51
Basic NN with TensorFlow Exercises
00:47
–Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
25:44
What is a layer?
01:53
What is a Deep Net, and what does it do?
02:18
Digging into a Deep Net
04:58
Non-linearities and their purpose
02:59
Activation Functions
03:37
Softmax activation
03:24
Backpropagation
03:12
Backpropagation picture
03:02
Backpropagation – A peek into the Mathematics of Optimization
00:21
–Deep Learning – Overfitting
19:36
What is overfitting?
03:51
For Classification, Over- and Underfitting
01:52
What is Validation?
03:22
Validation, Training, and Testing Datasets
02:30
Cross Validation of N-Fold
03:07
Training Stopping Early or When to Stop
04:54
–Deep Learning – Initialization
08:04
What is Initialization?
02:32
Types of simple initializations
02:47
State-of-the-Art Method – (Xavier) Glorot Initialization
02:45
–Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
20:40
Stochastic Gradient Descent
03:24
Problems with Gradient Descent
02:02
Momentum
02:30
Learning Rate Schedules or How to Select the Best Learning Rate
04:25
Learning Rate Schedules Visualized
01:32
Schedules of Adaptive Learn Rate (AdaGrad & RMSprop ).
04:08
Adam (Adaptive Moment Estimation)
02:39
–Deep Learning – Preprocessing
14:33
Introduction to Preprocessing
02:51
Different types of basic preprocessing
01:17
Standardization
04:31
Preprocessing Categorical Data
02:15
Binary and One-Hot Coding
03:39
–Deep Learning – Classifying on the MNIST Dataset
36:34
MNIST: The Dataset
02:25
How to tackle the MNIST
02:44
MNIST: Importing and loading the relevant packages Data
02:11
MNIST: Preprocessing the Data – Make a Validation set and scale it
04:43
MNIST: Preprocessing the Data Scale the Test Data – Exercise
00:03
MNIST: Preprocessing the Data – Batch and Shuffle
06:30
MNIST: Preprocessing the Data – Shuffle & Batch – Exercise
00:03
MNIST: Outline of the Model
04:54
MNIST: Choose the Loss and the Optimizer
02:05
MNIST: Learning
05:38
MNIST – Exercises
01:21
MNIST: Testing of the Model
03:56
–Deep Learning – Business Case Example
39:19
Business Case: Exploring Datasets and Identifying Predictors
07:54
Business Case: Outlining the Solution
01:31
Business Case: Balancing Dataset
03:39
Business Case: Preprocessing Data
11:32
Business Case: Preprocessing Data – Exercise
00:12
Business Case: Load Preprocessed Data
03:23
Business Case: Load Preprocessed Data – Exercise
00:03
Business Case: Interpreting and Learning from the Results
04:15
Business Case: Setting up an early stopping mechanism
05:01
Establishing an Early Stopping Mechanism – Exercise
00:08
Test the Model in a Business Case
01:23
Final Exercise: Business Case
00:16
–Deep Learning – Conclusion
17:26
Summary of What You Have Learned
03:41
What is Machine Learning?
01:47
DeepMind and Deep Learning
00:21
An overview of CNNs
04:55
A Review of RNNs
02:50
An overview of non-NN Approaches
03:52
–Appendix: Deep Learning – TensorFlow 1: Introduction
28:52
READ ME!!!
00:21
How to Install TensorFlow1
02:20
A Note Concerning Anaconda Package Installation
01:14
TensorFlow Intro
03:46
Actual introduction to TensorFlow
01:40
Types of File formats, supporting Tensors
02:38
Basic NN Example With TF: Inputs/Outputs/Targets/Weights, Biases, Targets/Biases
06:05
Basic NN Example with Loss Function and Gradient Descent
03:41
Basic NN with TF: Model Output
06:05
Basic NN Example with TF Exercises
01:01
–Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Dataset
39:31
MNIST: