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The Data Science Course 2019: Complete Data Science Bootcamp

Education May 31

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

365 Careers, 365 Careers Team

What is this course teaching

  1. The course provides the entire toolbox you need to become a data scientist
  2. 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
  3. Impress interviewers by showing an understanding of the data science field
  4. Learn how to pre-process data
  5. Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  6. Start coding in Python and learn how to use it for statistical analysis
  7. Perform linear and logistic regressions in Python
  8. Carry out cluster and factor analysis
  9. Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  10. Apply your skills to real-life business cases
  11. Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  12. Unfold the power of deep neural networks
  13. Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  14. Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

Course Details

The Problem

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.

However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

And how can you do that?

Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)

Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture

The Solution

Data science is a multidisciplinary field. It encompasses a wide range of topics.

  • Understanding of the data science field and the type of analysis carried out
  • Mathematics
  • Statistics
  • Python
  • Applying advanced statistical techniques in Python
  • Data Visualization
  • Machine Learning
  • Deep Learning

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.

So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2019.

We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).

The Skills

   1. Intro to Data and Data Science

Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?

Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.

2. Mathematics

Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.

We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.

Why learn it?

Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.

3. Statistics

You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.

Why learn it?

This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.

4. Python

Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.

Why learn it?

When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.

5. Tableau

Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.

Why learn it?

A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.

6. Advanced Statistics

Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.

Why learn it?

Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.

7. Machine Learning

The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.

Why learn it?

Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.

***What you get***

  • A $1250 data science training program
  • Active Q&A support
  • All the knowledge to get hired as a data scientist
  • A community of data science learners
  • A certificate of completion
  • Access to future updates
  • Solve real-life business cases that will get you the job

You will become a data scientist from scratch

We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and become a part of our data scientist program today.  

 

Who this course is for:
  • You should take this course if you want to become a Data Scientist or if you want to learn about the field
  • This course is for you if you want a great career
  • The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

What's ın Course Content

Part 1: Introduction
The Field of Data Science – The Various Data Science Disciplines
Data Science and Business Buzzwords: Why are there so many?
What is the difference between Analysis and Analytics
What is the difference between Analysis and Analytics
Business Analytics, Data Analytics, and Data Science: An Introduction
Continuing with BI, ML, and AI
Continuing with BI, ML, and AI
A Breakdown of our Data Science Infographic
A Breakdown of our Data Science Infographic
The Field of Data Science – Connecting the Data Science Disciplines
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
The Field of Data Science – The Benefits of Each Discipline
The Reason behind these Disciplines
The Reason behind these Disciplines
The Field of Data Science – Popular Data Science Techniques
Techniques for Working with Traditional Data
Techniques for Working with Traditional Data
Real Life Examples of Traditional Data
Techniques for Working with Big Data
Techniques for Working with Big Data
Real Life Examples of Big Data
Business Intelligence (BI) Techniques
Business Intelligence (BI) Techniques
Real Life Examples of Business Intelligence (BI)
Techniques for Working with Traditional Methods
Techniques for Working with Traditional Methods
Real Life Examples of Traditional Methods
Machine Learning (ML) Techniques
Machine Learning (ML) Techniques
Types of Machine Learning
Types of Machine Learning
Real Life Examples of Machine Learning (ML)
Real Life Examples of Machine Learning (ML)
The Field of Data Science – Popular Data Science Tools
Necessary Programming Languages and Software Used in Data Science
Necessary Programming Languages and Software Used in Data Science
The Field of Data Science – Careers in Data Science
Finding the Job – What to Expect and What to Look for
Finding the Job – What to Expect and What to Look for
The Field of Data Science – Debunking Common Misconceptions
Debunking Common Misconceptions
Debunking Common Misconceptions
Part 2: Probability
The Basic Probability Formula
The Basic Probability Formula
Computing Expected Values
Computing Expected Values
Frequency
Frequency
Events and Their Complements
Events and Their Complements
Combinatorics
Fundamentals of Combinatorics
Fundamentals of Combinatorics
Permutations and How to Use Them
Permutations and How to Use Them
Simple Operations with Factorials
Simple Operations with Factorials
Solving Variations with Repetition
Solving Variations with Repetition
Solving Variations without Repetition
Solving Variations without Repetition
Solving Combinations
Solving Combinations
Symmetry of Combinations
Symmetry of Combinations
Solving Combinations with Separate Sample Spaces
Solving Combinations with Separate Sample Spaces
Combinatorics in Real-Life: The Lottery
Combinatorics in Real-Life: The Lottery
A Recap of Combinatorics
A Practical Example of Combinatorics
Bayesian Inference
Sets and Events
Sets and Events
Ways Sets Can Interact
Ways Sets Can Interact
Intersection of Sets
Intersection of Sets
Union of Sets
Union of Sets
Mutually Exclusive Sets
Mutually Exclusive Sets
Dependence and Independence of Sets
Dependence and Independence of Sets
The Conditional Probability Formula
The Conditional Probability Formula
The Law of Total Probability
The Additive Rule
The Additive Rule
The Multiplication Law
The Multiplication Law
Bayes’ Law
Bayes’ Law
A Practical Example of Bayesian Inference
Probability Distributions
Fundamentals of Probability Distributions
Fundamentals of Probability Distributions
Types of Probability Distributions
Types of Probability Distributions
Characteristics of Discrete Distributions
Characteristics of Discrete Distributions
Discrete Distributions: The Uniform Distribution
Discrete Distributions: The Uniform Distribution
Discrete Distributions: The Bernoulli Distribution
Discrete Distributions: The Bernoulli Distribution
Discrete Distributions: The Binomial Distribution
Discrete Distributions: The Binomial Distribution
Discrete Distributions: The Poisson Distribution
Discrete Distributions: The Poisson Distribution
Characteristics of Continuous Distributions
Characteristics of Continuous Distributions
Continuous Distributions: The Normal Distribution
Continuous Distributions: The Normal Distribution
Continuous Distributions: The Standard Normal Distribution
Continuous Distributions: The Standard Normal Distribution
Continuous Distributions: The Students’ T Distribution
Continuous Distributions: The Students’ T Distribution
Continuous Distributions: The Chi-Squared Distribution
Continuous Distributions: The Chi-Squared Distribution
Continuous Distributions: The Exponential Distribution
Continuous Distributions: The Exponential Distribution
Continuous Distributions: The Logistic Distribution
Continuous Distributions: The Logistic Distribution
A Practical Example of Probability Distributions
Probability in Other Fields
Probability in Finance
Probability in Statistics
Probability in Data Science
Part 3: Statistics
Population and Sample
Population and Sample
2 questions
Statistics – Descriptive Statistics
Types of Data
Types of Data
Levels of Measurement
Levels of Measurement
Categorical Variables – Visualization Techniques
Categorical Variables – Visualization Techniques
Categorical Variables Exercise
Numerical Variables – Frequency Distribution Table
Numerical Variables – Frequency Distribution Table
Numerical Variables Exercise
The Histogram
The Histogram
Histogram Exercise
Cross Tables and Scatter Plots
Cross Tables and Scatter Plots
Cross Tables and Scatter Plots Exercise
Mean, median and mode
Mean, Median and Mode Exercise
Skewness
Skewness
Skewness Exercise
Variance
Variance Exercise
Standard Deviation and Coefficient of Variation
Standard Deviation
Standard Deviation and Coefficient of Variation Exercise
Covariance
Covariance
Covariance Exercise
Correlation Coefficient
Correlation
Correlation Coefficient Exercise
Statistics – Practical Example: Descriptive Statistics
Practical Example: Descriptive Statistics
Practical Example: Descriptive Statistics Exercise
Statistics – Inferential Statistics Fundamentals
Introduction
What is a Distribution
What is a Distribution
The Normal Distribution
The Normal Distribution
The Standard Normal Distribution
The Standard Normal Distribution
The Standard Normal Distribution Exercise
Central Limit Theorem
Central Limit Theorem
Standard error
Standard Error
Estimators and Estimates
Estimators and Estimates
Statistics – Inferential Statistics: Confidence Intervals
What are Confidence Intervals?
What are Confidence Intervals?
Confidence Intervals; Population Variance Known; z-score
Confidence Intervals; Population Variance Known; z-score; Exercise
Confidence Interval Clarifications
Student’s T Distribution
Student’s T Distribution
Confidence Intervals; Population Variance Unknown; t-score
Confidence Intervals; Population Variance Unknown; t-score; Exercise
Margin of Error
Margin of Error
Confidence intervals. Two means. Dependent samples
Confidence intervals. Two means. Dependent samples Exercise
Confidence intervals. Two means. Independent samples (Part 1)
Confidence intervals. Two means. Independent samples (Part 1) Exercise
Confidence intervals. Two means. Independent samples (Part 2)
Confidence intervals. Two means. Independent samples (Part 2) Exercise
Confidence intervals. Two means. Independent samples (Part 3)
Statistics – Practical Example: Inferential Statistics
Practical Example: Inferential Statistics
Practical Example: Inferential Statistics Exercise
Statistics – Hypothesis Testing
Null vs Alternative Hypothesis
Further Reading on Null and Alternative Hypothesis
Null vs Alternative Hypothesis
Rejection Region and Significance Level
Rejection Region and Significance Level
Type I Error and Type II Error
Type I Error and Type II Error
Test for the Mean. Population Variance Known
Test for the Mean. Population Variance Known Exercise
p-value
p-value
Test for the Mean. Population Variance Unknown
Test for the Mean. Population Variance Unknown Exercise
Test for the Mean. Dependent Samples
Test for the Mean. Dependent Samples Exercise
Test for the mean. Independent samples (Part 1)
Test for the mean. Independent samples (Part 1). Exercise
Test for the mean. Independent samples (Part 2)
Test for the mean. Independent samples (Part 2)
Test for the mean. Independent samples (Part 2) Exercise
Statistics – Practical Example: Hypothesis Testing
Practical Example: Hypothesis Testing
Practical Example: Hypothesis Testing Exercise
Part 4: Introduction to Python
Introduction to Programming
Introduction to Programming
Why Python?
Why Python?
Why Jupyter?
Why Jupyter?
Installing Python and Jupyter
Understanding Jupyter’s Interface – the Notebook Dashboard
Prerequisites for Coding in the Jupyter Notebooks
Jupyter’s Interface
Python 2 vs Python 3
Python – Variables and Data Types
Variables
Variables
Numbers and Boolean Values in Python
Numbers and Boolean Values in Python
Python Strings
Python Strings
Python – Basic Python Syntax
Using Arithmetic Operators in Python
Using Arithmetic Operators in Python
The Double Equality Sign
The Double Equality Sign
How to Reassign Values
How to Reassign Values
Add Comments
Add Comments
Understanding Line Continuation
Indexing Elements
01:18
Indexing Elements
1 question
Structuring with Indentation
01:44
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
13:29
The IF Statement
03:04
The IF Statement
1 question
The ELSE Statement
02:39
The ELIF Statement
05:33
A Note on Boolean Values
02:13
A Note on Boolean Values
1 question
Python – Python Functions
18:31
Defining a Function in Python
02:03
How to Create a Function with a Parameter
03:49
Defining a Function in Python – Part II
02:35
How to Use a Function within a Function
01:49
Conditional Statements and Functions
03:06
Functions Containing a Few Arguments
01:13
Built-in Functions in Python
03:56
Python Functions
2 questions
Python – Sequences
19:11
Lists
04:02
Lists
1 question
Using Methods
03:22
Using Methods
1 question
List Slicing
04:30
Tuples
03:13
Dictionaries
04:04
Dictionaries
1 question
Python – Iterations
15:53
For Loops
02:26
For Loops
1 question
While Loops and Incrementing
02:26
Lists with the range() Function
02:22
Lists with the range() Function
1 question
Conditional Statements and Loops
03:05
Conditional Statements, Functions, and Loops
02:27
How to Iterate over Dictionaries
03:07
Python – Advanced Python Tools
12:56
Object Oriented Programming
05:00
Object Oriented Programming
2 questions
Modules and Packages
01:05
Modules and Packages
2 questions
What is the Standard Library?
02:47
What is the Standard Library?
1 question
Importing Modules in Python
04:04
Importing Modules in Python
2 questions
Part 5: Advanced Statistical Methods in Python
01:27
Introduction to Regression Analysis
01:27
Introduction to Regression Analysis
1 question
Advanced Statistical Methods – Linear regression with StatsModels
40:55
The Linear Regression Model
05:50
The Linear Regression Model
2 questions
Correlation vs Regression
01:43
Correlation vs Regression
1 question
Geometrical Representation of the Linear Regression Model
01:25
Geometrical Representation of the Linear Regression Model
1 question
Python Packages Installation
04:39
First Regression in Python
07:11
First Regression in Python Exercise
00:39
Using Seaborn for Graphs
01:21
How to Interpret the Regression Table
05:47
How to Interpret the Regression Table
3 questions
Decomposition of Variability
03:37
Decomposition of Variability
1 question
What is the OLS?
03:13
What is the OLS
1 question
R-Squared
05:30
R-Squared
2 questions
Advanced Statistical Methods – Multiple Linear Regression with StatsModels
42:18
Multiple Linear Regression
02:55
Multiple Linear Regression
1 question
Adjusted R-Squared
06:00
Adjusted R-Squared
3 questions
Multiple Linear Regression Exercise
00:03
Test for Significance of the Model (F-Test)
02:01
OLS Assumptions
02:21
OLS Assumptions
1 question
A1: Linearity
01:50
A1: Linearity
2 questions
A2: No Endogeneity
04:09
A2: No Endogeneity
1 question
A3: Normality and Homoscedasticity
05:47
A4: No Autocorrelation
03:31
A4: No autocorrelation
2 questions
A5: No Multicollinearity
03:26
A5: No Multicollinearity
1 question
Dealing with Categorical Data – Dummy Variables
06:43
Dealing with Categorical Data – Dummy Variables
00:03
Making Predictions with the Linear Regression
03:29
Advanced Statistical Methods – Linear Regression with sklearn
54:29
What is sklearn and How is it Different from Other Packages
02:14
How are Going to Approach this Section?
01:56
Simple Linear Regression with sklearn
05:38
Simple Linear Regression with sklearn – A StatsModels-like Summary Table
04:49
A Note on Normalization
00:11
Simple Linear Regression with sklearn – Exercise
00:03
Multiple Linear Regression with sklearn
03:10
Calculating the Adjusted R-Squared in sklearn
04:45
Calculating the Adjusted R-Squared in sklearn – Exercise
00:03
Feature Selection (F-regression)
04:41
A Note on Calculation of P-values with sklearn
00:13
Creating a Summary Table with p-values
02:10
Multiple Linear Regression – Exercise
00:03
Feature Scaling (Standardization)
05:38
Feature Selection through Standardization of Weights
05:22
Predicting with the Standardized Coefficients
03:53
Feature Scaling (Standardization) – Exercise
00:03
Underfitting and Overfitting
02:42
Train – Test Split Explained
06:54
Advanced Statistical Methods – Practical Example: Linear Regression
38:01
Practical Example: Linear Regression (Part 1)
11:59
Practical Example: Linear Regression (Part 2)
06:12
A Note on Multicollinearity
00:16
Practical Example: Linear Regression (Part 3)
03:15
Dummies and Variance Inflation Factor – Exercise
00:03
Practical Example: Linear Regression (Part 4)
08:10
Dummy Variables – Exercise
00:15
Practical Example: Linear Regression (Part 5)
07:34
Linear Regression – Exercise
00:16
Advanced Statistical Methods – Logistic Regression
40:49
Introduction to Logistic Regression
01:19
A Simple Example in Python
04:42
Logistic vs Logit Function
04:00
Building a Logistic Regression
02:48
Building a Logistic Regression – Exercise
00:03
An Invaluable Coding Tip
02:26
Understanding Logistic Regression Tables
04:06
Understanding Logistic Regression Tables – Exercise
00:03
What do the Odds Actually Mean
04:30
Binary Predictors in a Logistic Regression
04:32
Binary Predictors in a Logistic Regression – Exercise
00:03
Calculating the Accuracy of the Model
03:21
Calculating the Accuracy of the Model
00:03
Underfitting and Overfitting
03:43
Testing the Model
05:05
Testing the Model – Exercise
00:03
Advanced Statistical Methods – Cluster Analysis
14:03
Introduction to Cluster Analysis
03:41
Some Examples of Clusters
04:31
Difference between Classification and Clustering
02:32
Math Prerequisites
03:19
Advanced Statistical Methods – K-Means Clustering
49:01
K-Means Clustering
04:41
A Simple Example of Clustering
07:48
A Simple Example of Clustering – Exercise
00:03
Clustering Categorical Data
02:50
Clustering Categorical Data – Exercise
00:03
How to Choose the Number of Clusters
06:11
How to Choose the Number of Clusters – Exercise
00:03
Pros and Cons of K-Means Clustering
03:23
To Standardize or not to Standardize
04:32
Relationship between Clustering and Regression
01:31
Market Segmentation with Cluster Analysis (Part 1)
06:03
Market Segmentation with Cluster Analysis (Part 2)
06:58
How is Clustering Useful?
04:47
EXERCISE: Species Segmentation with Cluster Analysis (Part 1)
00:03
EXERCISE: Species Segmentation with Cluster Analysis (Part 2)
00:03
Advanced Statistical Methods – Other Types of Clustering
13:34
Types of Clustering
03:39
Dendrogram
05:21
Heatmaps
04:34
Part 6: Mathematics
51:01
What is a matrix?
03:37
What is a Matrix?
6 questions
Scalars and Vectors
02:58
Scalars and Vectors
5 questions
Linear Algebra and Geometry
03:06
Linear Algebra and Geometry
3 questions
Arrays in Python – A Convenient Way To Represent Matrices
05:09
What is a Tensor?
03:00
What is a Tensor?
2 questions
Addition and Subtraction of Matrices
03:36
Addition and Subtraction of Matrices
3 questions
Errors when Adding Matrices
02:01
Transpose of a Matrix
05:13
Dot Product
03:48
Dot Product of Matrices
08:23
Why is Linear Algebra Useful?
10:10
Part 7: Deep Learning
03:07
What to Expect from this Part?
03:07
What is Machine Learning
4 questions
Deep Learning – Introduction to Neural Networks
42:38
Introduction to Neural Networks
04:09
Introduction to Neural Networks
1 question
Training the Model
02:54
Training the Model
3 questions
Types of Machine Learning
03:43
Types of Machine Learning
4 questions
The Linear Model (Linear Algebraic Version)
03:08
The Linear Model
2 questions
The Linear Model with Multiple Inputs
02:25
The Linear Model with Multiple Inputs
2 questions
The Linear model with Multiple Inputs and Multiple Outputs
04:25
The Linear model with Multiple Inputs and Multiple Outputs
3 questions
Graphical Representation of Simple Neural Networks
01:47
Graphical Representation of Simple Neural Networks
1 question
What is the Objective Function?
01:27
What is the Objective Function?
2 questions
Common Objective Functions: L2-norm Loss
02:04
Common Objective Functions: L2-norm Loss
3 questions
Common Objective Functions: Cross-Entropy Loss
03:55
Common Objective Functions: Cross-Entropy Loss
4 questions
Optimization Algorithm: 1-Parameter Gradient Descent
06:33
Optimization Algorithm: 1-Parameter Gradient Descent
4 questions
Optimization Algorithm: n-Parameter Gradient Descent
06:08
Optimization Algorithm: n-Parameter Gradient Descent
3 questions
Deep Learning – How to Build a Neural Network from Scratch with NumPy
20:36
Basic NN Example (Part 1)
03:06
Basic NN Example (Part 2)
04:58
Basic NN Example (Part 3)
03:25
Basic NN Example (Part 4)
08:15
Basic NN Example Exercises
00:52
Deep Learning – TensorFlow: Introduction
28:26
How to Install TensorFlow
02:20
A Note on Installing Packages in Anaconda
01:09
TensorFlow Outline and Logic
03:46
Actual Introduction to TensorFlow
01:40
Types of File Formats, supporting Tensors
02:38
Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases
06:05
Basic NN Example with TF: Loss Function and Gradient Descent
03:41
Basic NN Example with TF: Model Output
06:05
Basic NN Example with TF Exercises
01:02
Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
25:44
What is a Layer?
01:53
What is a Deep Net?
02:18
Digging into a Deep Net
04:58
Non-Linearities and their Purpose
02:59
Activation Functions
03:37
Activation Functions: Softmax Activation
03:24
Backpropagation
03:12
Backpropagation picture
03:02
Backpropagation – A Peek into the Mathematics of Optimization
00:21
Deep Learning – Overfitting
19:36
What is Overfitting?
03:51
Underfitting and Overfitting for Classification
01:52
What is Validation?
03:22
Training, Validation, and Test Datasets
02:30
N-Fold Cross Validation
03:07
Early Stopping or When to Stop Training
04:54
Deep Learning – Initialization
08:04
What is Initialization?
02:32
Types of Simple Initializations
02:47
State-of-the-Art Method – (Xavier) Glorot Initialization
02:45
Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
20:40
Stochastic Gradient Descent
03:24
Problems with Gradient Descent
02:02
Momentum
02:30
Learning Rate Schedules, or How to Choose the Optimal Learning Rate
04:25
Learning Rate Schedules Visualized
01:32
Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
04:08
Adam (Adaptive Moment Estimation)
02:39
Deep Learning – Preprocessing
14:33
Preprocessing Introduction
02:51
Types of Basic Preprocessing
01:17
Standardization
04:31
Preprocessing Categorical Data
02:15
Binary and One-Hot Encoding
03:39
Deep Learning – Classifying on the MNIST Dataset
39:31
MNIST: What is the MNIST Dataset?
02:26
MNIST: How to Tackle the MNIST
02:48
MNIST: Relevant Packages
01:34
MNIST: Model Outline
06:51
MNIST: Loss and Optimization Algorithm
02:39
Calculating the Accuracy of the Model
04:18
MNIST: Batching and Early Stopping
02:08
MNIST: Learning
07:35
MNIST: Results and Testing
06:11
MNIST: Exercises
01:30
MNIST: Solutions
01:31
Deep Learning – Business Case Example
50:58
Business Case: Getting acquainted with the dataset
07:55
Business Case: Outlining the Solution
01:57
The Importance of Working with a Balanced Dataset
03:39
Business Case: Preprocessing
11:35
Business Case: Preprocessing Exercise
00:14
Creating a Data Provider
06:37
Business Case: Model Outline
05:34
Business Case: Optimization
05:10
Business Case: Interpretation
02:05
Business Case: Testing the Model
02:04
Business Case: A Comment on the Homework
03:51
Business Case: Final Exercise
00:17
Deep Learning – Conclusion
17:42
Summary on What You’ve Learned
03:41
What’s Further out there in terms of Machine Learning
01:47
An overview of CNNs
04:55
DeepMind and Deep Learning
00:25
An Overview of RNNs
02:50
An Overview of non-NN Approaches
03:52
Download All Resources
00:12
Software Integration
29:38
What are Data, Servers, Clients, Requests, and Responses
04:43
What are Data, Servers, Clients, Requests, and Responses
2 questions
What are Data Connectivity, APIs, and Endpoints?
07:05
What are Data Connectivity, APIs, and Endpoints?
2 questions
Taking a Closer Look at APIs
08:05
Taking a Closer Look at APIs
2 questions
Communication between Software Products through Text Files
04:20
Communication between Software Products through Text Files
1 question
Software Integration – Explained
05:25
Software Integration – Explained
2 questions
Case Study – What’s Next in the Course?
10:14
Game Plan for this Python, SQL, and Tableau Business Exercise
04:08
The Business Task
02:48
Introducing the Data Set
03:18
Introducing the Data Set
1 question
Case Study – Preprocessing the ‘Absenteeism_data’
01:29:17
What to Expect from the Following Sections?
01:28
Importing the Absenteeism Data in Python
03:23
Checking the Content of the Data Set
05:53
Introduction to Terms with Multiple Meanings
03:27
What’s Regression Analysis – a Quick Refresher
01:51
Using a Statistical Approach towards the Solution to the Exercise
02:17
Dropping a Column from a DataFrame in Python
06:27
EXERCISE – Dropping a Column from a DataFrame in Python
00:27
SOLUTION – Dropping a Column from a DataFrame in Python
00:02
Analyzing the Reasons for Absence
05:04
Obtaining Dummies from a Single Feature
08:37
EXERCISE – Obtaining Dummies from a Single Feature
00:04
SOLUTION – Obtaining Dummies from a Single Feature
00:01
Dropping a Dummy Variable from the Data Set
01:34
More on Dummy Variables: A Statistical Perspective
01:28
Classifying the Various Reasons for Absence
08:35
Using .concat() in Python
04:35
EXERCISE – Using .concat() in Python
00:04
SOLUTION – Using .concat() in Python
00:02
Reordering Columns in a Pandas DataFrame in Python
01:43
EXERCISE – Reordering Columns in a Pandas DataFrame in Python
00:06
SOLUTION – Reordering Columns in a Pandas DataFrame in Python
00:13
Creating Checkpoints while Coding in Jupyter
02:52
EXERCISE – Creating Checkpoints while Coding in Jupyter
00:04
SOLUTION – Creating Checkpoints while Coding in Jupyter
00:01
Analyzing the Dates from the Initial Data Set
07:48
Extracting the Month Value from the “Date” Column
07:00
Extracting the Day of the Week from the “Date” Column
03:36
EXERCISE – Removing the “Date” Column
00:37
Analyzing Several “Straightforward” Columns for this Exercise
03:17
Working on “Education”, “Children”, and “Pets”
04:38
Final Remarks of this Section
01:59
Case Study – Applying Machine Learning to Create the ‘absenteeism_module’
01:07:05
Exploring the Problem with a Machine Learning Mindset
03:20
Creating the Targets for the Logistic Regression
06:32
Selecting the Inputs for the Logistic Regression
02:41
Standardizing the Data
03:26
Splitting the Data for Training and Testing
06:12
Fitting the Model and Assessing its Accuracy
05:39
Creating a Summary Table with the Coefficients and Intercept
05:16
Interpreting the Coefficients for Our Problem
06:14
Standardizing only the Numerical Variables (Creating a Custom Scaler)
04:12
Interpreting the Coefficients of the Logistic Regression
05:10
Backward Elimination or How to Simplify Your Model
04:02
Testing the Model We Created
04:43
Saving the Model and Preparing it for Deployment
04:06
ARTICLE – A Note on ‘pickling’
01:15
EXERCISE – Saving the Model (and Scaler)
00:13
Preparing the Deployment of the Model through a Module
04:04
Case Study – Loading the ‘absenteeism_module’
11:00
Are You Sure You’re All Set?
00:15
Deploying the ‘absenteeism_module’ – Part I
03:50
Deploying the ‘absenteeism_module’ – Part II
06:23
Exporting the Obtained Data Set as a *.csv
00:32
Case Study – Analyzing the Predicted Outputs in Tableau
23:30
EXERCISE – Age vs Probability
00:14
Analyzing Age vs Probability in Tableau
08:49
EXERCISE – Reasons vs Probability
00:15
Analyzing Reasons vs Probability in Tableau
07:49
EXERCISE – Transportation Expense vs Probability
00:22
Analyzing Transportation Expense vs Probability in Tableau

Student Reviews

Before I started this course I had no background neither in Data Science or Python and honestly, sometimes I was struggling a lot with some of the topics, covered. However, this is a no brainer 5+ star course to me! One can see with a “naked eye” that these guys have put so much effort into this! While the lectures cover A LOT of stuff, there are things that I had to learn on my own (mostly about Python syntax), before continuing through the chapters (which is a great thing). Basically, this course gives a very good basis to build upon.

Finally, I would like to thank Martin and Iliya, because I feel that I got my inspiration for Machine Learning triggered and I can’t wait to learn more and more!

Keep up the good work!

Best regards,

Dimitar Lyubchev
⭐️⭐️⭐️⭐️⭐️

Two words – simply amazing! This is a second course from 365 Careers that I’m taking – and won’t be the last. Positive and encouraging, it keeps me alert even through the most difficult lectures. Thank you 365 Team!
Rositsa Velikova
⭐️⭐️⭐️⭐️⭐️

Much more volume of knowledge than I expected.

Math coaching is fairly adequate.

The examples are concrete and vivid.

Good training course for starting data science journey.

Shaochong Li
⭐️⭐️⭐️⭐️⭐️

This course provides a great overview of Blender, an extremely powerful (and also incredibly complicated) 3D modeling and animation program. Mikey, the main instructor, is amusing and informative. Both he and Ben (the other instructor, who is more behind-the-scenes) actively engage with students on course the forums. They also have a TA who is very attentive and helpful. No question is missed by this team. Also, the course forums are rich with entertaining and inspiring student work.
C Sed
⭐️⭐️⭐️⭐️⭐️

I thoroughly enjoyed this course primarily presented by Ben. Great instructor, took the time to explain in depth and provide real-world uses for that discussion. Would highly recommend this course for both beginning Blender users and the moderately experienced. It’s good to learn the fundamentals…
John Freeman
⭐️⭐️⭐️⭐️⭐️

Explains the techniques and use cases for different tools very well and is easy to follow. The challenges are fun and engaging. Having a great time learning Blender basics.

Adrian Serrato
⭐️⭐️⭐️⭐️⭐️

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