**Introduction to Machine Learning for Data Science (Updated)**

.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 48000 Hz, 2ch | 2.63 GB

Duration: 5.5 hours | Genre: eLearning Video | Language: English

**Category: Tutorial** | **Comment: 0**

.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 48000 Hz, 2ch | 2.63 GB

Duration: 5.5 hours | Genre: eLearning Video | Language: English

**Author: voska89**

**Category: Tutorial** | **Comment: 0**

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 6.5 Hours | Lec: 87 | 909 MB

**Author: everest555**

**Category: Tutorial** | **Comment: 0**

MP4 | Video: AVC 1280 x 720 | Audio: AAC 48 KHz 2ch | Duration: 20:29:34 | 13.13 GB

Genre: eLearning | Language: English

If nothing else, by the end of this video course you will have analyzed a number of datasets from the wild, built a handful of applications, and applied machine learning algorithms in meaningful ways to get real results. And all along the way you learn the best practices and computational techniques used by professional data scientists. You get hands-on experience with the PyData ecosystem by manipulating and modeling data. You explore and transform data with the pandas library, perform statistical analysis with SciPy and NumPy, build regression models with statsmodels, and train machine learning algorithms with scikit-learn. All throughout the course you learn to test your assumptions and models by engaging in rigorous validation. Finally, you learn how to share your results through effective data visualization.

**Author: voska89**

**Category: Tutorial** | **Comment: 0**

MP4 | Video: AVC 1280 x 720 | Audio: AAC 48 KHz 2ch | Duration: 12:29:58 | 8.95 GB

Genre: eLearning | Language: English

**Author: voska89**

**Category: Tutorial** | **Comment: 0**

MP4 | Video: AVC 1280 x 720 | Audio: AAC 44 KHz 2ch | Duration: 12:19:02 | 8.82 GB

Genre: eLearning | Language: English

**Author: voska89**

**Category: Tutorial** | **Comment: 0**

MP4 | Video: AVC 1280 x 720 | Audio: AAC 48KHz 2ch | Duration: 6H 38M | 13.68 GB

Genre: eLearning | Language: English

**Author: voska89**

**Category: Tutorial** | **Comment: 0**

https://i110.fastpic.ru/big/2019/0316/f1/df1209c4e3e166be4736a6a7ad0edcf1.png[/img]

SKILLSHARE APPLIED DATA SCIENCE PART 1 OVERVIEW-iLLiTERATE

**General:Training**

Size: 208.39 MB

SKILLSHARE APPLIED DATA SCIENCE PART 1 OVERVIEW-iLLiTERATE

Size: 208.39 MB

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

Data Science: Supervised Machine Learning in Python (Updated 10/2018)

Video: .MP4, 1280x720 | Audio: AAC, 48kHz, 2ch | Duration: 6h

Genre: eLearning | Language: English | Size: 0,99 GB

What Will I Learn?

Understand and implement K-Nearest Neighbors in Python

Understand the limitations of KNN

User KNN to solve several binary and multiclass classification problems

Understand and implement Naive Bayes and General Bayes Classifiers in Python

Understand the limitations of Bayes Classifiers

Understand and implement a Decision Tree in Python

Understand and implement the Perceptron in Python

Understand the limitations of the Perceptron

Understand hyperparameters and how to apply cross-validation

Understand the concepts of feature extraction and feature selection

Understand the pros and cons between classic machine learning methods and deep learning

Use Sci-Kit Learn

Implement a machine learning web service

Requirements

Python, Numpy, and Pandas experience

Probability and statistics (Gaussian distribution)

Strong ability to write algorithms

Description

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It's extremely simple and intuitive, and it's a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we'll look at some ways in which KNN can fail.

It's important to know both the advantages and disadvantages of each algorithm we look at.

Next we'll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.

We'll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next we'll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we'll study, and most courses you'll look at won't implement them. We will, since I believe implementation is good practice.

The last algorithm we'll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One we've studied these algorithms, we'll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.

We'll do a comparison with deep learning so you understand the pros and cons of each approach.

We'll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We'll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

NOTES:

In the directory: supervised_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)

Python coding: if/else, loops, lists, dicts, sets

Numpy, Scipy, Matplotlib

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

(The Numpy Stack in Python)

Linear Regression in Python

Logistic Regression in Python

(Supervised Machine Learning in Python)

(Bayesian Machine Learning in Python: A/B Testing)

Deep Learning in Python

Practical Deep Learning in Theano and TensorFlow

(Supervised Machine Learning in Python 2: Ensemble Methods)

Convolutional Neural Networks in Python

(Easy NLP)

(Cluster Analysis and Unsupervised Machine Learning)

Unsupervised Deep Learning

(Hidden Markov Models)

Recurrent Neural Networks in Python

Artificial Intelligence: Reinforcement Learning in Python

Natural Language Processing with Deep Learning in Python

Who is the target audience?

Students and professionals who want to apply machine learning techniques to their datasets

Students and professionals who want to apply machine learning techniques to real world problems

Anyone who wants to learn classic data science and machine learning algorithms

Anyone looking for an introduction to artificial intelligence (AI)

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

http://nitroflare.com/view/63CE39A35249E1D/Data_Science_Supervised_Machine_Learning_in_Python.part1.rar

http://nitroflare.com/view/C2061E0C3E2B70A/Data_Science_Supervised_Machine_Learning_in_Python.part2.rar

**Rapidgator**

https://rapidgator.net/file/cfd9ec146f982c7d34f98a6f8c215473/Data_Science_Supervised_Machine_Learning_in_Python.part1.rar.html

https://rapidgator.net/file/8ea3e14aad16c5d7dc6ddcac3215aac5/Data_Science_Supervised_Machine_Learning_in_Python.part2.rar.html

[/center]

Video: .MP4, 1280x720 | Audio: AAC, 48kHz, 2ch | Duration: 6h

Genre: eLearning | Language: English | Size: 0,99 GB

What Will I Learn?

Understand and implement K-Nearest Neighbors in Python

Understand the limitations of KNN

User KNN to solve several binary and multiclass classification problems

Understand and implement Naive Bayes and General Bayes Classifiers in Python

Understand the limitations of Bayes Classifiers

Understand and implement a Decision Tree in Python

Understand and implement the Perceptron in Python

Understand the limitations of the Perceptron

Understand hyperparameters and how to apply cross-validation

Understand the concepts of feature extraction and feature selection

Understand the pros and cons between classic machine learning methods and deep learning

Use Sci-Kit Learn

Implement a machine learning web service

Requirements

Python, Numpy, and Pandas experience

Probability and statistics (Gaussian distribution)

Strong ability to write algorithms

Description

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It's extremely simple and intuitive, and it's a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we'll look at some ways in which KNN can fail.

It's important to know both the advantages and disadvantages of each algorithm we look at.

Next we'll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability.

We'll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next we'll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we'll study, and most courses you'll look at won't implement them. We will, since I believe implementation is good practice.

The last algorithm we'll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One we've studied these algorithms, we'll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification.

We'll do a comparison with deep learning so you understand the pros and cons of each approach.

We'll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We'll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

NOTES:

In the directory: supervised_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule)

Python coding: if/else, loops, lists, dicts, sets

Numpy, Scipy, Matplotlib

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

USEFUL COURSE ORDERING:

(The Numpy Stack in Python)

Linear Regression in Python

Logistic Regression in Python

(Supervised Machine Learning in Python)

(Bayesian Machine Learning in Python: A/B Testing)

Deep Learning in Python

Practical Deep Learning in Theano and TensorFlow

(Supervised Machine Learning in Python 2: Ensemble Methods)

Convolutional Neural Networks in Python

(Easy NLP)

(Cluster Analysis and Unsupervised Machine Learning)

Unsupervised Deep Learning

(Hidden Markov Models)

Recurrent Neural Networks in Python

Artificial Intelligence: Reinforcement Learning in Python

Natural Language Processing with Deep Learning in Python

Who is the target audience?

Students and professionals who want to apply machine learning techniques to their datasets

Students and professionals who want to apply machine learning techniques to real world problems

Anyone who wants to learn classic data science and machine learning algorithms

Anyone looking for an introduction to artificial intelligence (AI)

[i][/i]Screenshots

http://nitroflare.com/view/63CE39A35249E1D/Data_Science_Supervised_Machine_Learning_in_Python.part1.rar

http://nitroflare.com/view/C2061E0C3E2B70A/Data_Science_Supervised_Machine_Learning_in_Python.part2.rar

https://rapidgator.net/file/cfd9ec146f982c7d34f98a6f8c215473/Data_Science_Supervised_Machine_Learning_in_Python.part1.rar.html

https://rapidgator.net/file/8ea3e14aad16c5d7dc6ddcac3215aac5/Data_Science_Supervised_Machine_Learning_in_Python.part2.rar.html

[/center]

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

Data Science: Deep Learning in Python (Updated)

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 9.5 hour | Size: 1.43 GB

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow

What you'll learn

Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)

Learn how a neural network is built from basic building blocks (the neuron)

Code a neural network from scratch in Python and numpy

Code a neural network using Google's TensorFlow

Describe different types of neural networks and the different types of problems they are used for

Derive the backpropagation rule from first principles

Create a neural network with an output that has K > 2 classes using softmax

Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"

Install TensorFlow

Requirements

How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)

Install Numpy and Python (approx. latest version of Numpy as of Jan 2016)

Don't worry about installing TensorFlow, we will do that in the lectures.

Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Description

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.

NOTE:

If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.

I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

linear algebra

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course

Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

People who already know how to take partial derivatives and log-likelihoods. Since we cover this in more detail in my logistic regression class, it is not covered quite as thoroughly here.

People who already know how to code in Python and Numpy. You will need some familiarity because we go through it quite fast. Don't worry, it's not that hard.

[i][/i]Screenshots

**I recommends Buy premimum account for High speed+parallel downloads!**

**Nitroflare**

http://nitroflare.com/view/5D46D8A71FD0754/Data_Science_Deep_Learning_in_Python.part1.rar

http://nitroflare.com/view/B332160B4B76249/Data_Science_Deep_Learning_in_Python.part2.rar

**Rapidgator**

https://rapidgator.net/file/57c57664526fbbe816b19cd52ae83910/Data_Science_Deep_Learning_in_Python.part1.rar.html

https://rapidgator.net/file/c7dcb5ac599a2087a245ebf8da0a5aa5/Data_Science_Deep_Learning_in_Python.part2.rar.html

[/center]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .VTT | Duration: 9.5 hour | Size: 1.43 GB

The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow

What you'll learn

Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)

Learn how a neural network is built from basic building blocks (the neuron)

Code a neural network from scratch in Python and numpy

Code a neural network using Google's TensorFlow

Describe different types of neural networks and the different types of problems they are used for

Derive the backpropagation rule from first principles

Create a neural network with an output that has K > 2 classes using softmax

Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"

Install TensorFlow

Requirements

How to take partial derivatives and log-likelihoods (ex. finding the maximum likelihood estimations for a die)

Install Numpy and Python (approx. latest version of Numpy as of Jan 2016)

Don't worry about installing TensorFlow, we will do that in the lectures.

Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course

Description

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.

NOTE:

If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow.

I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus

linear algebra

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

TIPS (for getting through the course):

Watch it at 2x.

Take handwritten notes. This will drastically increase your ability to retain the information.

Write down the equations. If you don't, I guarantee it will just look like gibberish.

Ask lots of questions on the discussion board. The more the better!

Realize that most exercises will take you days or weeks to complete.

Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course

Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

People who already know how to take partial derivatives and log-likelihoods. Since we cover this in more detail in my logistic regression class, it is not covered quite as thoroughly here.

People who already know how to code in Python and Numpy. You will need some familiarity because we go through it quite fast. Don't worry, it's not that hard.

[i][/i]Screenshots

http://nitroflare.com/view/5D46D8A71FD0754/Data_Science_Deep_Learning_in_Python.part1.rar

http://nitroflare.com/view/B332160B4B76249/Data_Science_Deep_Learning_in_Python.part2.rar

https://rapidgator.net/file/57c57664526fbbe816b19cd52ae83910/Data_Science_Deep_Learning_in_Python.part1.rar.html

https://rapidgator.net/file/c7dcb5ac599a2087a245ebf8da0a5aa5/Data_Science_Deep_Learning_in_Python.part2.rar.html

[/center]

**Author: hunterlucky**

**Category: Tutorial** | **Comment: 0**

WEBRip | English | MP4 | 1920 x 1080 | AVC ~384 Kbps | 30 fps

AAC | 158 Kbps | 48.0 KHz | 2 channels | 13h 09mn | 3.12 GB

This course is designed to teach you the basics of Python and Data Science in a practical way, so that you can acquire, test, and master your Python skills gradually.

**Author: voska89**

Prev
Next