Use Python to master machine learning in four steps and python to master machines in four steps

Use Python to master machine learning in four steps and python to master machines in four steps

To understand and apply machine learning technology, you need to learn Python or R. Both are programming languages similar to C, Java, and PHP. However, since Python and R are both relatively young and "Far Away" from the CPU, they seem simpler. Compared with R, which is only used to process data, such as machine learning, Statistical algorithms, and beautiful graph analysis data, Pthon is advantageous in its applicability to many other problems. Because Python has a wider distribution (using Jango to host websites, NLP in natural language processing, and APIs for websites such as Twitter and Linkedin), it is similar to more traditional languages, for example, C python is quite popular.

Four steps for learning machine learning in Python

1. First, you must use books, courses, and videos to learn basic Python knowledge.

2. Then you must master different modules, such as Pandas, Numpy, Matplotlib, and NLP, to process, clean up, plot, and understand data.

3. Then you must be able to capture data from the webpage, whether through the website API or the webpage capture module beauul ul Soap. Data can be collected through web crawling and applied to machine learning algorithms.

4. In the last step, you must Learn machine learning tools, such as Scikit-Learn, or execute the machine learning algorithm (ML-algorithm) in the captured data ).

1. Python Getting Started Guide:

One simple and quick way to learn Python is to register with codecademy.com, start programming, and learn basic Python knowledge. Another classic way to learn Python is through learnpythonthehardway, a site recommended by many Python programmers. Then there is an excellent PDF, byte pointer of Your python. The python community has also prepared a list of Python resource lists for beginners. You can also download Think Python from o'reilley for free. The last resource is the Introduction of Python for metering economics, Statistics, and Data Analysis: "Introduction to billing Python Statistics for economic metrics, Statistics and Statistics Data Analysis", it also contains the basic knowledge of Python.

2. Important modules of machine learning

The most important modules of machine learning are NumPy, Pandas, Matplotlib, and IPython. One book covers some of the modules: Data Pipeline Analysis Platform with Open Source pipeline Tools. Then from 1. the free book "Introduction functions to develop Python functions for economic metrics, Statistics and Statistics Data Analysis", also includes Numpy, Pandas, Matplotlib and IPython modules. Another resource is Python plugin for Your Data Pipeline Analysis: Your Data Pipeline Wrangling tables with Pandas, NumPy, runtime and   IPython, which also contains some important modules. Links to other free modules are as follows: Numpy (Numerical tutorial Python, Numpy tutorial Userguide, Guide tutorial to tutorial NumPy), Pandas (Pandas, powerful embedded Python embedded Data into Analysis into Toolkit, Practical embedded Business into Python, Intros into your Pandas embedded Data into Structure) and Matplotlib embedded books.

Other resources:

• 10 minutes attached to Pandas
• Pandas platform for External machine learning
• 100 define NumPy limit exercises

3. Mining and capturing data from a website through APIS

Once you understand the basic knowledge of Python and the most important modules, you must learn how to collect data from different sources. This technology is also called Web page capture. The traditional source is website text, and text data obtained from websites such as twitter or linkedin through APIS. Excellent books on Web page capturing include: Mining beyond the Social networking Web (free books ), web tracking Scraping processing with logging Python and Web tracking Scraping processing with logging Python: Collecting processing Data records from loading the specified Modern processing Web.

Finally, this text data must be converted to numerical data, which is completed by NLP technology, natural neural language processing starts with Natural Python and Natural neural Language processing Annotation algorithms for neural Machine Learning. Other data, including images and videos, can be analyzed using Computer image technology: Programming Computer Vision processing with Programming Python, Programming Computer Programming Vision processing with Programming Python: using Tools between and between algorithms between for Processing analyzing between images and Practical between Python between and between OpenCV, these are typical resources for image analysis.

The following example includes an educational and interesting example that can be implemented using the basic Python command line, as well as web page capturing technology.

• Mini-Tutorial)
• Web tracking Scraping processing Indeed tasks for processing Key statistics Data processing Science tasks Job capture Skills (Web page capturing Key Data Science Skills)
• Case when Study: semantic Sentiment Semantic Analysis Statement On semantic Movie semantic Reviews (Case Study: Sentiment Analysis in Movie Reviews)
• First Web Crawler Scraper (First Web page capture)
• Sentiment emotional Analysis of email)
• Simple plain Text Classification (Simple Text Classification)
• Basic Semantic Sentiment semantic Analysis comes with semantic Python (Python Basic Sentiment Analysis)
• Twitter messaging analysis using messaging Python messaging and messaging NLTK (using Python and NLTK for Twitter sentiment analysis)
• Second retry Try: Kernel Sentiment semantic Analysis Plugin in kernel Python (Second attempt: Python Sentiment Analysis)
• Natural neural Language Processing in every a few Kaggle neural Competition algorithms for Movie Reviews (NLP Natural Language Processing in Movie Reviews related Kaggle Competition)

4. Machine Learning in Python

Machine learning can be divided into four groups: classification, clustering, regression, and dimensionality reduction.

"Classification" can also be called supervised learning. It helps to classify images and identify features or faces in images. It can also classify users by user shape and assign different score values to them. "Clustering" occurs in unsupervised learning and allows users to identify groups/clusters in data. "Regression" allows you to estimate a value through a parameter set and can be used to predict the optimal price of a house, apartment, or car.

Modules,   packages lists all the important modules, packages, and techniques for machine learning in Python, C, Scala, Java, Julia, MATLAB, Go, R, and Ruby. For Python Machine learning books, I recommend Machine learning in artificial action. Although a little short, it is likely to be a classic in machine learning because it mentions the "Collective Smart Programming era": Programming into Collective into Intelligence. These two books help you build machine learning by capturing data. Most recent Machine Learning Publications are based on the module scikit-learn. All algorithms have been implemented in the module, making machine learning very simple. The only thing you have to do is to tell Python which machine learning technique (ML-technique) should be used to analyze data.

The free scikit-learn tutorial can be found on the official scikit-learn website. Other posts can be obtained through the following link:

• Introduction to automated Machine Learning processes with automated Python programming and automated Scikit-Learn (Introduction to Python and Scikit-Learn in Machine Learning)
• Data Science basics in Alibaba Python (Data Science in Python)
• Machine Learning Algorithms for Predicting algorithms Bad program Loans (using Machine Learning to predict Bad debt)
• A Classification Generic Classification Architecture Classification for Classification Text Classification with Classification Machine Learning (general Architecture of Classification Text through Machine Learning)
• Using Neural Python neural and artificial AI into neural predict neural types neural of artificial wine (Using Python and AI to predict wine Varieties)
• Advice networking for artificial applying artificial Machine Learning (recommended for applying Machine Learning)
• Predicting predict customer churn (use scikit-learn to predict user churn)
• Mapping Your Music collections Collection (ing Your Music favorites)
• Data Science basics in Alibaba Python (Data Science in Python)
• Case when Study: semantic Sentiment Semantic Analysis Statement on semantic Movie semantic Reviews (Case Study: Sentiment Analysis in Movie Reviews)
• Document Clustering with Document Python (Document Clustering in Python)
• Five specified most extends popular extends similarity extends measures implements implementation within six python (implementation of the Five most popular Python similarity measurement)
• Case when Study: semantic Sentiment Semantic Analysis Statement on semantic Movie semantic Reviews (Case Study: Sentiment Analysis in Movie Reviews)
• Will it upgrade Python? (Will it be Python ?)
• Text Processing in Machine Learning)
• Hacking into an issue epic into the non-state-of-the-state online goal startup celebration program starts with getting a picture of hue available light show tables and real-time machine learning (black with color light show and real-time machine learning) into the semi-finals (North American Ice Hockey League) goal celebration)
• Vancouver guest Room pricing Prices (Vancouver Room price)
• Mining and forecasting Predicting University Education Faculty education Salaries (Exploring and Predicting University Instructor Salaries)
• Predicting delayed Airline delayed Delays (Predicted flight Delays)

Books about the scikit-learn module in machine learning and Python:

• Collection history of recent books published on Alibaba reddit (collect books on the reddit news website)
• Building automated Machine Learning automated Systems developed with automated Python (using Python to build a Machine Learning system)
• Building automated Machine Learning automated Systems developed with automated Python, developed 2nd automated Edition (build a Machine Learning system using Python, Second Edition)
• Learning Skills scikit-learn: artificial Machine Learning skills in artificial Python (Learning scikit-learn: Machine Learning in Python)
• Machine Learning Algorithm (Machine Learning Algorithm)
• Data processing Science starts from Scratch when-fetch First packet Principles conflicts with capture Python (capture Data Science-top Principles about Python)
• Machine Learning comes in handy Python (Machine Learning in Python)

Books to be released in the next few months include:

• Introduction to Machine Learning with artificial Python)
• Thoughtful neural Machine Learning platform with objective Python: connecting A neural Test-Driven neural Approach (think Python Machine Learning: close to the Test drive)

Machine Learning courses and blogs

Do you want to get a degree, join an online course, or join an offline workshop, base camp, or university course? Here are some links to online education sites on logical analysis, big data, data mining, and data science: Collection types of dynamic links. We also recommend some online courses-Coursera courses from Udacity: machine learning and Data Processing Analyst tutorial Nanodegree. There are also some blogs about Machine Learning: List tables of refreshing frequently updated blogs.

Last, it was an excellent youtube video course on exploring machine learning, from Jake vandreplas and Olivier Grisel.

Machine Learning Theory

Want to learn machine learning theory? Therefore, The typical examples of The semantic Elements recognition of semantic statistical semantic Learning and Introduction recognition to semantic Statistical semantic Learning are often cited. There are two other books: Introduction generation to artificial machine neural learning and A pair of Course classes in artificial Machine neural Learning. These links include free PDF files, and you do not need to pay! If you do not want to read these books, watch the video: 15 seconds hours about theory knowledge of neural machine learning!

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