Learning machine learning with Python – Learning machine learning with Python
People are becoming more and more involved in technology, and in the meantime, a branch of artificial intelligence called machine learning has emerged as a new science.
Machine learning, To reduce our hassle and give all technology and software tools the power to progress on their own (meaning the advancement of learning through our data).
Machine learning science can give a software the power that the software can use to solve some problems with the experience it has, or better to say, the software with the knowledge it has.
A good example in this field is Google. You must have understood this. That Google’s powerful search engine has the ability to learn, has not it occurred to you that as soon as you try to search for something, Google immediately guesses it?
Do you think that Google does not know what you are interested in? Doesn’t Google show a list of words related to your search at the bottom of your page? In the following, it is not bad to have a look at the article about hacking Python .
Introduction to Python Learning Machine
First of all, what is the whole Python programming language? Doesn’t Google retain the most searched words? The answer to all these questions is yes, and you should know that Google search engine is extremely smart and learns from all the behaviors of its users and learns to use it through this data that Google has taken and analyzed ( Google is the second largest brand in the world, and it is only through a search engine that it has achieved this)
But our more important discussion is learning to learn machine learning with Python. Python is a very popular high-performance programming language (we will introduce this programming language comprehensively).
That is, we use Python programming language to create software or technologies that are capable of learning from our data, just like humans who learn things using the information they receive. Join us to learn machine learning with Complete the Python (introduction to the Python language, introduction to the learning machine, and finally learning to learn the learning machine with Python).
What is Python?
Here’s how to put one together for use with your Python learning machine :
- Full introduction of Python programming language
- Python is a versatile, high-level object-oriented language
- It is open source and scripted. Nowadays, you can develop any kind of program with Python language. What web applications are websites?
- Mobile applications
- Robotics and Mac applications (operating systems for Apple computers) and Windows (operating systems for most computers in the world).
That is why it is said that Python is a universal language. If you ask the concept of object-oriented, we must say that object-oriented programming languages are languages with which program development will be enjoyable. In the following, it is better to know more about Python programming language and say more.
Because they follow the laws of our world. That is, in these programming languages, there are concepts such as adjective, class (categorization), object (there are thousands of objects in the world), inheritance, and so on. Another feature of the Python language is the ability to build applications by learning machine learning.
Learning machine learning with Python is an important topic, as it is one of the first choices for machine learning. Another important feature of Python programming language is that it is open source.
In the world of open source software, that is, software that usually does not belong to a specific company and anyone can take the source code of this software and with changes in that software or programming language to offer it under another name or the same name. کردن.
This feature is extremely important because it will make Python all-round (all developers in the world can help Python develop).
Important features of Python
One of the most popular features of Python is its selection by the world’s technology giants. Companies like Google, Facebook, NASA, Dropbox, Yahoo, Instagram, Reddit, Mozilla and استفاده use Python, and this shows how important this language is.
Another important feature of Python that is perhaps the most important feature is the simplicity of Python. Python is one of the best options if you want to learn a programming language for the first time. Say above).
Python has many advantages, such as that other teammates in building a project understand the written software and can easily continue their work. You can even show your code to a specialist if needed.
For correction and revision, this feature means that code readability will be very useful for you. Unlike many programming languages, Python is too simple, meaning you can sometimes do just a few lines of code in the language you want to do, and on the other hand it is very similar to human language.
Advantages of using Python
When you want to display a phrase in Python, just write (“print Hello”) The benefits of Python are innumerable. But just know that Python is widely used in machine learning.
One of the disadvantages of Python may be runtime errors, weakness in mobile applications, differences with many programming languages in the world, slowing down and که that if compared to other programming languages, Python certainly has more advantages than them (all languages Programming is flawed).
“There are two categories of programming languages: the first category does not use anyone. The second category has fans who constantly complain about it.”
Python is currently one of the most important programming languages in the world
If you want to learn machine learning with Python, it’s definitely best to get acquainted with the Python programming language job market. Like all other Python programming languages, depending on your skills, the programming tools you know. Your work history, and the city in which you want to work, you will have different incomes.
If you want to do a good job with Python, we must first say that you have made the right choice, and then first count on the companies that are in Tehran, and then cities like Isfahan, Tabriz, Mashhad, Karaj and Shiraz, of course, The number of startups is increasing day by day.
Most of these startups need new employees, and even if you can not get a job in any of these cities, there is still good work for Python in other cities (Abroad requires a lot of Python programmers more than.
If you plan to work abroad, Python should be one of your priorities, especially the topic of learning the Python learning machine, which is needed in most industries).
According to StackOverFlow, the Python language had the highest demand in 2018. In the United States, Python has been the most lucrative programming language in the world (because Python is not currently prevalent in Iran, this salary is between 1 and 8 million, depending on many factors). In the following, it is not bad to have a look at the article for loop iteration in Python .
Learning machine
Learning machine learning has become extremely important in our time. Because everyone knows how big this field of artificial intelligence is going to be, and it has involved several industries.
Learning machine learning with Python can not only enable you to work with large companies, but also cultivate thinking in you and make you think better, and always use your math, algorithms and programming knowledge to be useful. Enjoy (like the feeling of creating something new that gives a person vitality and confidence).
Learning machine labor market
To understand the importance of learning to learn machine learning with Python, we examine the job market of machine learning here. Machine learning is an important part of artificial intelligence.
According to the LinkedIn website, the engineering job in the field of machine learning has grown 10 times between 2012 and 2018. In other words, the field of machine learning is the most prosperous job in 2017.
Due to the importance of data science and machine learning, it is predicted that the demand for this job will increase every year (in Iran, too, machine learning engineers will certainly be needed in the near future, and if you are looking for the future, right now Think about learning machine learning).
Automotive industry with car learning
Learning machine learning with Python allows you to work in the automotive industry as well, because today machine learning has come with the help of self-driving car technology (this technology must learn from its own data and experience to be able to control without The car driver walks the path).
Step-by-step tutorial Learning machine learning with Python
In the topic of learning machine learning with Python, we will teach you all the steps and remember that practice, perseverance and hard work are very important in programming. First, it is necessary to explain the different stages of machine learning to you (to start learning Learning machine with Python)
- Collecting data
- Categorize data
- data analysis
- Algorithm development
- Check the generated algorithm
- Use the algorithm for better conclusions
In machine learning, there are generally three types of learning that you need to know to get started.
Machine learning, unsupervised
The first way to learn machine learning is supervised learning. In this method, the data is not tagged (for example, if the data is a collection of images, we do not indicate with a label that this image is a machine).
This means that the data sets contain only the input and there will be no output commensurate with the inputs. In fact, in this method, the learning machine does not have control over the data and does not aim to establish a connection between input and output.
Unsupervised learning is a way to find patterns in the data, with unsupervised learning we will be able to find hidden structures and patterns in the data (in order to be able to learn machine learning well, you need to have different types. Know).
Machine learning, supervised
The second way to learn machine learning is the monitoring method. In this method, machine learning is labeled on the data and monitored.
In fact, the data are presented with the aim of finally reaching a complete and correct result. An example of supervised learning is, imagine in our emails the text inside the emails is considered as input and the output is labeled as either spam or non-spam.
Semi-supervised learning
You should also know about the third case, called semi-supervised learning, which is actually a combination of unsupervised learning and supervised learning. This method uses both classified data, that is, the same data as we said, and unclassified data (the same as unlabeled data) at the same time (to increase learning accuracy).
The next step is to strengthen math. Without high math knowledge, you will certainly have trouble learning the machine. One of the most important things you should know about linear algebra and mathematical analysis (mathematical analysis includes concepts such as limit, derivative, functions, etc.) that you can start learning linear algebra training from the home school website .
Or if your English is very good, the Khan Academy and MIT OpenCourseWare websites have very good courses in this area.
The first in the field of mathematical analysis and linear algebra and the second in the field of mathematics required for machine learning that you can count on (If you want to progress in the subject of learning machine learning with Python, please learn the items in order and do not rush)
Learning machine learning is by learning mathematics
The third step is to learn the syntax of the Python language. That is, you start with Python from the ground up. The syntax of a language is called commands. The following are good books for education.
But if you want to be a good footballer, you can never become a good footballer by reading books. But with repetition, practice, perseverance and reading a great book, you can definitely become a good footballer.
The same is true in programming. You can learn programming with a book. But you have to do a lot of practice and repetition to become a real Python programmer and start learning machine learning as well. Read on for a look at the Java or Python article ? which one is better? Also have.
The next step is to get acquainted with some of the Python libraries for working with artificial intelligence and data analysis (of course you have to learn data analysis at this stage, so only the library for data analysis in Python will be introduced).
Pandas Library
Pandas is an open source library with which you can analyze high-performance data structures. It is also widely used in data preprocessing and visualization, which is also very popular among scientists.
Try using Pandas alongside other available Python libraries for machine learning and deep learning (more libraries for working with machine learning in Python are listed below).
In the fifth step of learning machine learning, we reach the most important part, which is to introduce the libraries needed for learning. If you want to work in the field of machine learning or site design or application development, remember that you will need libraries or frameworks. .
Because usually all the programming languages in the world, except for the work for which they are designed, can be multiplied through frameworks or libraries.
One of the great advantages of the Python programming language is that it has a large number of frameworks. If you want to learn machine learning with Python, you have to use the best frameworks.
For the convenience of working and building machine learning applications, we introduce the best libraries here that you can start using with books and articles.
Scikit-Learn Library
The most famous library to start learning is SkateLearn. This library provides many tools for data analysis and data mining, and you can use functions such as regression clustering, model selection, preprocessing, classification. SKlearn’s great feature is its high speed.
No wonder why platforms like Spotify, Booking.com and JPMorgan use it. There are many resources for learning skateboard on the web, one of the best of which is the skateboard website itself.
Start learning machine learning using Python and Skeet Learn with this website (any developer can upgrade and improve it).
Pybrain Library
One of the best libraries written with Python language to use Python programming language for machine learning, machine learning learning with this library will be enjoyable because it is free, open source and open source.
It is also said that this library is very powerful in terms of machine learning tasks.
The library itself includes neural network algorithms and reinforcement learning. If you want brilliant results in machine learning, use this library along with other Python AI libraries and frameworks.
To learn about machine learning with Pie Brain Library, you can refer to Pie Brain website and website level articles.
Keras Library
The third library for learning machine learning is the cross library. Cross is a high-level, functional programming interface (API), and you can do a quick test in this library.
If you want to implement your idea in the shortest possible time, be sure to think about using cross. Cross also has the ability to run on Tensorflow, Theano and CNTK.
To learn machine learning with the help of Cross Library, you can get help from books and articles or Cross website (get results very quickly).
Pie Name Library (NumPy)
The fourth library to start learning is called Pie. A great library for working with data in Python that can be used for machine learning purposes. With this library you can work with homogeneous multidimensional arrays, large multidimensional arrays and matrices. The library also has functions for linear algebra and number conversion. An example of: NumPy
1 2 3 4 5 6 7 8 9 10 | >>> B = arange(3) >>> B array([0, 1, 2]) >>> exp(B) array([ 1. , 2.71828183, 7.3890561 ]) >>> sqrt(B) array([ 0. , 1. , 1.41421356]) >>> C = array([2., –1., 4.]) >>> add(B, C) array([ 2., 0., 6.]) |
No special work has been done in this example. Just how to call some global math functions (such as sine, cosine or…) NumPy and define or assign an array in Python is given.
XGBoost Library
The fifth learning tool is called XGboot. It is actually an algorithm developed as a separate library for both R and Python programming languages.
This open source library provides a gradient enhancement framework for C ++, Java, Python, Julia, and R languages (gradient reinforcement is a machine learning method for regression and classification problems).
This algorithm is the algorithm of choice for many winning machine learning teams and is very popular on the Kegel website, which is a platform for competing data engineers and machine learning, for huge amounts of money.
Kegel is a great platform for learning and strengthening machine learning and data engineering. The XG Boot Library is one of the most popular algorithms on this website, and groups have been very successful in using it.
Matplotlib
Another great library for learning machine learning is Matt Plot Lip. Using this library, various diagrams can be drawn (it does this using data visualization). In fact, you should first save the data as a frame using the Pandas library.
Next, to better understand the data, visualize the data using the LipLip Metplot. In the following, we will program a plan using Python and Lip Plot Mat (you can get acquainted with the syntax and use of some libraries in Python according to some examples).
If you want to take machine learning seriously, you can start by using the towardsdatascience website.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | import numpy as np import matplotlib.pyplot as plt t = np.arange)0.0, 1.0 + 0.01, 0.01) s = np.cos)2 * 2*np.pi * t) t[41:60] = np.nan plt.subplot)2, 1, 1) plt.plot)t, s, ‘-‘, lw=2) plt.xlabel(‘time)s)’) plt.ylabel(‘voltage (mV)’) plt.title(‘A sine wave with a gap of NaNs between 0.4 and 0.6’) plt.grid)True) plt.subplot(2, 1, 2) t[0] = np.nan t[–1] = np.nan plt.plot(t, s, ‘-‘, lw=2) plt.title(‘Also with NaN in first and last point’) plt.xlabel(‘time (s)’) plt.ylabel(‘more nans’) plt.grid(True) plt.tight_layout() plt.show() |
Eli Five Library (Eli5)
The seventh library for machine learning is the Elie Five Library. Eli Five is actually a package for the Python programming language that supports SKlearn, XGboot, lightning, Kreas, Catboot sklearn-crfsuite frameworks and packages.
In fact, Eli5 is a package of Python language that is used to debug codes (check for errors and run programs), machine learning categories and explain predictions in machine learning.
Catboost Library
Cat Boost is an algorithm for reinforcing decision tree-based gradients. Simply put, this library developed by Yandex engineers and researchers is free and open source.
Generally for suggestion systems, personal assistants, cars with auto-driving capability, weather forecasting and many other tasks.
It should also be noted that the boost jacket has a very high speed and performs factors such as predicting very quickly. The following are examples from this library to learn more about Python coding with Kat Boost:
Learning machine learning with Python and regression analysis
Regression analysis is a statistical process in statistical models for estimating relationships between variables and is extremely useful in machine learning.
If you are a little confused, do not worry, by studying basic sciences and starting programming from the beginner level, you will get acquainted with all the concepts for learning machine learning with Python, respectively)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | from catboost import CatBoostRegressor # Initialize data train_data = [[1, 4, 5, 6], [4, 5, 6, 7], [30, 40, 50, 60]] eval_data = [[2, 4, 6, 8], [1, 4, 50, 60]] train_labels = [10, 20, 30] # Initialize CatBoostRegressor model = CatBoostRegressor(iterations=2, learning_rate=1, depth=2) # Fit model model.fit)train_data, train_labels) # Get predictions preds = model.predict)eval_data) |
Apply a classification model to the GPU
(Another very important thing about learning machine learning is to categorize the data because it would be easier with the Cat Boost library. More examples of learning machine learning in Python are written here)
1 2 3 4 5 6 7 8 9 10 11 12 | from catboost import CatBoostClassifier train_data = [[0, 3], [4, 1], [8, 1], [9, 1]] train_labels = [0, 0, 1, 1] model = CatBoostClassifier(iterations=1000, task_type=“GPU”, devices=‘0:1’) model.fit(train_data, train_labels, verbose=False) |
The last step in learning to learn machine learning with Python
Getting started to install the environment and the initial steps to get started learning machine learning is that you can start reading books as one of the very good solutions. Good books will be introduced below. Choose one and learn machine learning. Start learning as the future awaits you.
If you are still not motivated enough, please read again about the applications of machine learning and the job market and its importance in this article or on the Internet, and with motivation and eagerness to learn, you are ready to read one of the comprehensive books you see below.
Introducing machine learning training books with Python
Many books have been published on each of the different programming languages and technologies (note that books are usually more comprehensive and better than instructional videos). We continue with the topic of learning machine learning with Python. Introducing that you can read them to learn machine learning in Python.
Of course, it is necessary to mention that unfortunately there is no good book in Persian about learning machine learning with Python and all the books you see below are in English, but each of them may be translated if you speak English well You do not know do not worry.
You can use a dictionary (you will find many specialized words, of course, if your field is computer) and the second point is that the language of these books is very simple, so your average familiarity with English can also be useful for understanding learning books. Learning machine in Python.
Tips for data scientists
One of the best learning machine learning books, this book is published by Media O’reilly Publishing and you can also read the electronic version. The book has 400 pages and was written by Anders C. Mueller and Sarah Guido about the contents of the book. It can be said that if you use Python programming language.
Even as a beginner, this book will teach you the scientific methods for building learning machine solutions and projects that you are considering. You will learn the steps needed to build your successful learning machine application.
Using the Python language and SKlearn’s popular library, the authors focus on the scientific aspects of using machine learning algorithms instead of the mathematics behind them. If you are familiar with NumPy and matplotlib libraries, you will learn more about the book. With this book, you will learn the following:
Basic concepts and applications of machine learning
- Advantages and disadvantages of widely used machine learning algorithms
- How to display processed data by machine learning including aspects of data that are focused on
- Advanced methods for model evaluation and parameter setting in machine learning learning
- Methods for working with textual data in machine learning and special word processing techniques
- Finally, suggestions for improving machine learning and data science skills for machine learning
Python Thinking Machine Learning Book (with a Test-Based Approach)
The second book to start learning machine learning is again a 220-page book by O’Reilly Media, authored by Matthew Kirk. In this book, you will learn machine learning in a practical way without being a bit confused.
In this book, you will work with the libraries and technologies of NumPy, Pandas, SckitLearn, SciPy. A chapter of the contents of the book is also dedicated to neural networks. From what you will learn in this book:
- Real-world examples for learning machine learning and testing each algorithm ahead
- Before programming began, Test Axis Development (TDD) was applied to write and run tests
- Teaching techniques to improve machine learning models with data extraction and feature development is provided.
- In this book, for better learning of learning machine, various algorithms such as K-Nearest Neighbors, neural network, clustering and… have been worked.
- This book also looks at the dangers of machine learning such as underfitting or overfitting.
- It seems that this book has taken on the task of learning a learning machine in a simpler and, of course, more practical way than the first book, without engaging you in academic studies. You can download the electronic version of the book or the printed version of this book. Order (no translation of the book has been published yet and is only available in English).
Learning machine learning with Python
A great book from Pocket Publishing for learning machine learning with Python, which uses algorithms, techniques, machine learning components, real-world examples and Python language libraries such as TensorFlow and SckitLearn to drive machine learning.
Key features of the book to teach learning machine learning with Python:
Has used Python libraries such as TensorFlow and Keras Books to create intelligent cognitive actions for their projects
Using machine learning algorithms to solve the challenges that data scientists face today
Has used the power of Python to explore the world of data mining and data analysis.
Things you will learn in this book:
Understand the important components in machine learning and data science
Use Python to explore the world of data mining and data analysis
Run ML algorithms from scratch in Python, TensorFlow and scikit-Learn
Who is this book suitable for?
If you are an avid learner, data analyst, or data engineer interested in learning machine learning and want to start working on learning machine learning tasks, this book is for you. Although it is not necessary, it will definitely be useful.
