Machine learning with python cookbook chris albon pdf download

Machine learning with python cookbook chris albon pdf download

Machine Learning with Python Cookbook (en).pdf,Item Preview

machine learning, do not buy this book. Instead, this book is for the machine learning practitioner who, while comfortable with the theory and concepts of machine learning, would 09/10/ · ISBN Formats: PDF, EPub, Kindle, Audiobook. Get book Machine Learning with Python Cookbook: Practical. Solutions from Preprocessing to Deep 29/04/ · machine-learning-with-python-cookbook Identifier-ark ark://t7tn5xz9p Ocr ABBYY FineReader (Extended OCR) Page_number_confidence Ppi Scanner Access full book title Python Machine Learning Cookbook by Chris Albon. Download full books in PDF and EPUB format. Python Machine Learning Cookbook Author: Chris Albon 11/10/ · Download Thid PDF Book: Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning 1st Edition by Chris Albon, for free. This ... read more

The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background. Ê Ê WHAT WILL YOU LEARN Understand the working of the O. framework in Data Science. Ê Get familiar with the end-to-end implementation of Machine Learning Pipeline. Learn how to implement Machine Learning algorithms and concepts using Python. Learn how to build a Predictive Model for a Business case. WHO THIS BOOK IS FORÊ This cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics.

This book also serves as a reference guidebook for intermediate Machine Learning practitioners. Ê TABLE OF CONTENTS 1. Boston Crime 2. World Happiness Report 3. Iris Species 4. Credit Card Fraud Detection 5. Heart Disease UCI. Author : Chris Albon Publisher: "O'Reilly Media, Inc. Author : David Foster Publisher: ISBN: Category : Languages : de Pages : View Book Description Generative Modelle haben sich zu einem der spannendsten Themenbereiche der Künstlichen Intelligenz entwickelt: Mit generativem Deep Learning ist es inzwischen möglich, einer Maschine das Malen, Schreiben oder auch das Komponieren von Musik beizubringen - kreative Fähigkeiten, die bisher dem Menschen vorbehalten waren.

Mit diesem praxisnahen Buch können Data Scientists einige der eindrucksvollsten generativen Deep-Learning-Modelle nachbilden wie z. Generative Adversarial Networks GANs , Variational Autoencoder VAEs , Encoder-Decoder- sowie World-Modelle. David Foster veranschaulicht die Funktionsweise jeder Methode, beginnend mit den Grundlagen des Deep Learning mit Keras, bevor er zu einigen der modernsten Algorithmen auf diesem Gebiet vorstößt. Die zahlreichen praktischen Beispiele und Tipps helfen dem Leser herauszufinden, wie seine Modelle noch effizienter lernen und noch kreativer werden können. Author : Ben Auffarth Publisher: Packt Publishing Ltd ISBN: Category : Computers Languages : en Pages : View Book Description Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook Description Artificial intelligence AI plays an integral role in automating problem-solving.

This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production. Model Evaluation Accuracy Create Baseline Classification Model Create Baseline Regression Model Cross Validation Pipeline Cross Validation With Parameter Tuning Using Grid Search Cross-Validation Custom Performance Metric F1 Score Generate Text Reports On Performance Nested Cross Validation Plot The Learning Curve Plot The Receiving Operating Characteristic Curve Plot The Validation Curve Precision Recall Split Data Into Training And Test Sets.

Model Selection Find Best Preprocessing Steps During Model Selection Hyperparameter Tuning Using Grid Search Hyperparameter Tuning Using Random Search Model Selection Using Grid Search Pipelines With Parameter Optimization. Linear Regression Adding Interaction Terms Create Interaction Features Effect Of Alpha On Lasso Regression Lasso Regression Linear Regression Linear Regression Using Scikit-Learn Ridge Regression Selecting The Best Alpha Value In Ridge Regression. Logistic Regression Fast C Hyperparameter Tuning Handling Imbalanced Classes In Logistic Regression Logistic Regression Logistic Regression On Very Large Data Logistic Regression With L1 Regularization One Vs. Rest Logistic Regression. Trees And Forests Outlier Detection With Isolation Forests Adaboost Classifier Decision Tree Classifier Decision Tree Regression Feature Importance Feature Selection Using Random Forest Handle Imbalanced Classes In Random Forest Random Forest Classifier Random Forest Classifier Example Random Forest Regression Select Important Features In Random Forest Titanic Competition With Random Forest Visualize A Decision Tree.

Nearest Neighbors Identifying Best Value Of k K-Nearest Neighbors Classification Radius-Based Nearest Neighbor Classifier. Support Vector Machines Calibrate Predicted Probabilities In SVC Find Nearest Neighbors Find Support Vectors Imbalanced Classes In SVM Plot The Support Vector Classifiers Hyperplane Support Vector Classifier SVC Parameters When Using RBF Kernel. Naive Bayes Bernoulli Naive Bayes Classifier Calibrate Predicted Probabilities Gaussian Naive Bayes Classifier Multinomial Logistic Regression Multinomial Naive Bayes Classifier Naive Bayes Classifier From Scratch. Clustering Agglomerative Clustering DBSCAN Clustering Evaluating Clustering k-Means Clustering Meanshift Clustering Mini-Batch k-Means Clustering.

Deep Learning. Setup Prevent Ubuntu Keras Adding Dropout Convolutional Neural Network Feedforward Neural Network For Binary Classification Feedforward Neural Network For Multiclass Classification Feedforward Neural Networks For Regression k-Fold Cross-Validating Neural Networks LSTM Recurrent Neural Network Neural Network Early Stopping Neural Network Weight Regularization Preprocessing Data For Neural Networks Save Model Training Progress Tuning Neural Network Hyperparameters Visualize Loss History Visualize Neural Network Architecutre Visualize Performance History. PyTorch Check If PyTorch Is Using The GPU. Basics Using Iterable As Function Arguments Handling Long Lines Of Code Tuples Vs. Named Tuples Append Using The Operator Function Example List All Files Of Certain Type In A Directory Add Padding Around String All Combinations For A List Of Objects any , all , max , min , sum Apply Operations Over Items In A List Applying Functions To List Items Arithmetic Basics Assignment Operators Basic Operations With NumPy Array Breaking Up String Variables Brute Force D20 Roll Simulator Cartesian Product Chain Together Lists Cleaning Text Compare Two Dictionaries Concurrent Processing Continue And Break Loops Convert HTML Characters To Strings Converting Strings To Datetime Create A New File Then Write To It Create A Temporary File Data Structure Basics Date And Time Basics Dictionary Basics Display JSON Display Scientific Notation As Floats Exiting A Loop Find The Max Value In A Dictionary Flatten Lists Of Lists For Loop Formatting Numbers Function Annotation Examples Function Basics Functions Vs.

Generators Generating Random Numbers With NumPy Generator Expressions Hard Wrapping Text How To Use Default Dicts if and if else If Else On Any Or All Elements Indexing And Slicing NumPy Arrays Indexing And Slicing NumPy Arrays Iterate An Ifelse Over A List Iterate Over Multiple Lists Simultaneously Iterating Over Dictionary Keys Lambda Functions Logical Operations Looping Over Two Lists Mathematical Operations Mocking Functions Nested For Loops Using List Comprehension Nesting Lists Numpy Array Basics Parallel Processing Partial Function Applications Priority Queues Queues And Stacks Recursive Functions repr vs. str Scheduling Jobs In The Future Select Random Element From A List Selecting Items In A List With Filters Set The Color Of A Matplotlib Plot Sort A List Of Names By Last Name Sort A List Of Strings By Length Store API Credentials For Open Source Projects String Formatting String Indexing String Operations Swapping Variable Values Try, Except, and Finally Unpacking A Tuple Unpacking Function Arguments Use Command Line Arguments In A Function Using Named Tuples To Store Data while Statement.

Data Visualization Back To Back Bar Plot In MatPlotLib Bar Plot In MatPlotLib Color Palettes in Seaborn Creating A Time Series Plot With Seaborn And pandas Creating Scatterplots With Seaborn Group Bar Plot In MatPlotLib Histograms In MatPlotLib Making A Matplotlib Scatterplot From A Pandas Dataframe Matplotlib, A Simple Example Pie Chart In MatPlotLib Scatterplot In MatPlotLib Stacked Percentage Bar Plot In MatPlotLib. Web Scraping Beautiful Soup Basic HTML Scraping Drilling Down With Beautiful Soup Monitor A Website For Changes With Python. Testing Simple Unit Test Test Code Speed Test For A Specific Exception Test If Output Is Close To A Value Testable Documentation. Logging Basic Logging. Clean Code Annotate Functions Annotate Nested Function Parameters Static Typing Checking. Other Generate Tweets Using Markov Chains Mine Twitter's Stream For Hashtags Or Words Simple Clustering With SciPy What Is The Probability An Economy Class Seat Is An Aisle Seat?

Basics Linear Interpolation Trimmed Mean. inv matrix array [[ In NumPy we can use linalg. inv to calculate A—1 if it exists. To see this in action, we can multiply a matrix by its inverse and the result is the identity matrix: Multiply matrix and its inverse matrix np. inv matrix array [[ 1. seed 0 Generate three random floats between 0. random 3 array [ 0. In our solution we generated floats; however, it is also common to generate integers: Generate three random integers between 1 and 10 np. randint 0, 11, 3 array [3, 7, 9] Alternatively, we can generate numbers by drawing them from a distribution: Draw three numbers from a normal distribution with mean 0. normal 0. logistic 0. uniform 1. We will use seeds throughout this book so that the code you see in the book and the code you run on your computer produces the same results. The raw data might be a logfile, dataset file, or database. Furthermore, often we will want to retrieve data from multiple sources.

We also cover methods of generating simulated data with desirable properties for experimentation. target View first observation features[0] array [ 0. Luckily, scikit-learn comes with some common datasets we can quickly load. It is a good dataset for exploring regression algorithms. Of those, three methods are particularly useful. pyplot as plt View scatterplot plt. head 2 integer datetime category 0 5 0 1 5 0 Discussion There are two things to note about loading CSV files. First, it is often useful to take a quick look at the contents of the file before loading. It can be very helpful to see how a dataset is structured beforehand and what parameters we need to set to load in the file. Fortunately, those parameters are mostly there to allow it to handle a wide variety of CSV formats. For example, CSV files get their names from the fact that the values are literally separated by commas e. Although it is not always the case, a common formatting issue with CSV files is that the first line of the file is used to define column headers e.

The header parameter allows us to specify if or where a header row exists. head 2 5 0 0 5 0 1 9 0 Discussion This solution is similar to our solution for reading CSV files. The main difference is the additional parameter, sheetname, that specifies which sheet in the Excel file we wish to load. sheetname can accept both strings containing the name of the sheet and integers pointing to sheet positions zero-indexed. If we need to load multiple sheets, include them as a list. head 2 2. The key difference is the orient parameter, which indicates to pandas how the JSON file is structured. For us, data wrangling is only one step in preprocessing our data, but it is an important step. Data frames are tabular, meaning that they are based on rows and columns like you would see in a spreadsheet.

head 5 Name PClass Age Sex Survived SexCode 0 Allen, Miss Elisabeth Walton 1st First, in a data frame each row corresponds to one observation e. For example, by looking at the first observation we can see that Miss Elisabeth Walton Allen stayed in first class, was 29 years old, was female, and survived the disaster. Second, each column contains a name e. We will use these to select and manipulate observations and features. In Sex, a woman is indicated by the string female, while in SexCode, a woman is indicated by using the integer 1. We will want all our features to be unique, and therefore we will need to remove one of these columns. Solution pandas has many methods of creating a new DataFrame object. In the real world, creating an empty DataFrame and then populating it will almost never happen. Instead, our DataFrames will be created from real data we have loading from other sources e.

head 2 3. shape , 6 Additionally, we can get descriptive statistics for any numeric columns using describe: Show statistics dataframe. describe Age Survived SexCode count Ideally, we would view the full data directly. But with most real-world cases, the data could have thousands to hundreds of thousands to millions of rows and columns. Instead, we have to rely on pulling samples to view small slices and calculating summary statistics of the data. In our solution, we are using a toy dataset of the passengers of the Titanic on her last voyage. Using head we can take a look at the first few rows five by default of the data.

Alternatively, we can use tail to view the last few rows. With shape we can see how many rows and columns our DataFrame contains. And finally, with describe we can see some basic descriptive statistics for any numerical column. It is worth noting that summary statistics do not always tell the full story. For example, if Survived equals 1, it indicates that the passenger survived the disaster. iloc[0] Name Allen, Miss Elisabeth Walton PClass 1st Age 29 Sex female Survived 1 SexCode 1 Name: 0, dtype: object We can use : to define a slice of rows we want, such as selecting the second, third, and fourth rows: Select three rows dataframe. iloc[] Name PClass Age Sex Survived SexCode 1 Allison, Miss Helen Loraine 1st 2.

iloc[:4] 3. loc['Allen, Miss Elisabeth Walton'] Name Allen, Miss Elisabeth Walton PClass 1st Age 29 Sex female Survived 1 SexCode 1 Name: Allen, Miss Elisabeth Walton, dtype: object Discussion All rows in a pandas DataFrame have a unique index value. By default, this index is an integer indicating the row position in the DataFrame; however, it does not have to be. DataFrame indexes can be set to be unique alphanumeric strings or customer numbers. For example, iloc[0] will return the first row regardless of whether the index is an integer or a label. It is useful to be comfortable with both loc and iloc since they will come up a lot during data cleaning. head 2 Name PClass Age Sex Survived SexCode 0 Allen, Miss Elisabeth Walton 1st Multiple conditions are easy as well. For example, you might only be interested in stores in certain states or the records of patients over a certain age.

replace "female", "Woman". head 2 0 Woman 1 Woman Name: Sex, dtype: object We can also replace multiple values at the same time: Replace "female" and "male with "Woman" and "Man" dataframe['Sex']. replace ["female", "male"], ["Woman", "Man"]. head 5 0 Woman 1 Woman 2 Man 3 Woman 4 Man Name: Sex, dtype: object We can also find and replace across the entire DataFrame object by specifying the whole data frame instead of a single column: Replace values, show two rows dataframe. replace 1, "One". head 2 Name PClass Age Sex Survived SexCode 0 Allen, Miss Elisabeth Walton 1st 29 female One One 1 Allison, Miss Helen Loraine 1st 2 female 0 One replace also accepts regular expressions: Replace values, show two rows dataframe.

head 2 Name PClass Age Sex Survived SexCode 0 Allen, Miss Elisabeth Walton First head 2 Name Passenger Class Age Sex Survived SexCode 0 Allen, Miss Elisabeth Walton 1st We can use the dictionary to change multiple column names at once: Rename columns, show two rows dataframe. head 2 Name Passenger Class Age Gender Survived SexCode 0 Allen, Miss Elisabeth Walton 1st If we want to rename all columns at once, this helpful snippet of code creates a dictionary with the old column names as keys and empty strings as values: 3. defaultdict str Create keys for name in dataframe. max print 'Minimum:', dataframe['Age']. min print 'Mean:', dataframe['Age']. mean print 'Sum:', dataframe['Age']. sum print 'Count:', dataframe['Age']. count Maximum: Furthermore, we can also apply these methods to the whole DataFrame: Show counts dataframe.

count Name PClass Age Sex Survived SexCode dtype: int64 3. Finally, if we simply want to count the number of unique values, we can use nunique: Show number of unique values dataframe['PClass']. nunique 4 3. isnull ]. head 2 Name PClass Age Sex Survived SexCode 12 Aubert, Mrs Leontine Pauline 1st NaN female 1 1 13 Barkworth, Mr Algernon H 1st NaN male 1 0 Discussion Missing values are a ubiquitous problem in data wrangling, yet many underestimate the difficulty of working with missing data. replace 'male', np. nan Oftentimes a dataset uses a specific value to denote a missing observation, such as NONE, , or.. nan, 'NONE', ] 3. head 2 Name PClass Survived SexCode 0 Allen, Miss Elisabeth Walton 1st 1 1 1 Allison, Miss Helen Loraine 1st 0 1 If a column does not have a name which can sometimes happen , you can drop it by its column index using dataframe.

columns: Drop column dataframe. drop dataframe. head 2 Name Age Sex Survived SexCode 0 Allen, Miss Elisabeth Walton An alternative method is del dataframe['Age'], which works most of the time but is not recommended because of how it is called within pandas the details of which are outside the scope of this book. Many pandas methods include an inplace parameter, which when True edits the DataFrame directly. I recommend treating DataFrames as immutable objects. If you treat your DataFrames as immutable objects, you will save yourself a lot of headaches down the road. We can use boolean conditions to easily delete single rows by matching a unique value: Delete row, show first two rows of output dataframe[dataframe['Name']!

head 2 Name PClass Age Sex Survived SexCode 1 Allison, Miss Helen Loraine 1st 2. Under this condition, every row in our DataFrame, data frame, is actually unique. Now we are left with a DataFrame of only two rows: one man and one woman. We can control this behavior using the keep parameter: Drop duplicates dataframe. groupby 'Sex'. mean Age Survived SexCode Sex female It is very common to have a DataFrame where each row is a person or an event and we want to group them according to some criterion and then calculate a statistic. We can accomplish this by grouping rows by individual resturants and then calculating the sum of each group. Users new to groupby often write a line like this and are confused by what is returned: Group rows dataframe. The reason is because groupby needs to be paired with some operation we want to apply to each group, such as calculating an aggregate statistic e.

groupby 'Survived' ['Name']. count Survived 0 1 Name: Name, dtype: int64 Notice Name added after the groupby? That is because particular summary statistics are only meaningful to certain types of data. In this case we group the data into survived or not, then count the number of names i. We can also group by a first column, then group that grouping by a second column: Group rows, calculate mean dataframe. groupby ['Sex','Survived'] ['Age']. mean Sex Survived female 0 randint 1, 10, 3. resample 'W'. The raw data looks like this: Show three rows dataframe. Using resample we can group the rows by a wide array of time periods offsets and then we can calculate some statistic on each time group: Group by two weeks, calculate mean dataframe.

resample '2W'. resample 'M'. We can control this behavior using the label parameter: Group by month, count rows dataframe. upper 3. upper Apply function, show two rows dataframe['Name']. apply uppercase [] 0 ALLEN, MISS ELISABETH WALTON 1 ALLISON, MISS HELEN LORAINE Name: Name, dtype: object Discussion apply is a great way to do data cleaning and wrangling. and then map that function to every element in a column. apply lambda x: x. count Name PClass Age Sex Survived SexCode Sex female male Discussion In Recipe 3.

apply is particularly useful when you want to apply a function to groups. The informal definition of concatenate is to glue two objects together. In the solution we glued together two small DataFrames using the axis parameter to indicate whether we wanted to stack the two DataFrames on top of each other or place them side by side. If we want to do an outer join, we can specify that with the how parameter: 3. To get all that data into one place, we can load each data query or data file into pandas as individual DataFrames and then merge them together into a single DataFrame. There are three aspects to specify with any merge operation.

First, we have to specify the two DataFrames we want to merge together. Second, we have to specify the name s of the columns to merge on—that is, the columns whose values are shared between the two DataFrames. If these two columns use the same name, we can use the on parameter. What is the left and right DataFrame? The simple answer is that the left DataFrame is the first one we specified in merge and the right DataFrame is the second one. This language comes up again in the next sets of parameters we will need. This is specified by the how parameter. merge supports the four main types of joins: Inner Return only the rows that match in both DataFrames e. Outer Return all rows in both DataFrames. If a row exists in one DataFrame but not in the other DataFrame, fill NaN values for the missing values e. Left Return all rows from the left DataFrame but only rows from the right DataFrame that matched with the left DataFrame. Fill NaN values for the missing values e.

Right Return all rows from the right DataFrame but only rows from the left DataFrame that matched with the right DataFrame. If you did not understand all of that right now, I encourage you to play around with the how parameter in your code and see how it affects what merge returns. The natural way to represent these quantities is numerically e. array [[ There are a number of rescaling techniques, but one of the simplest is called min-max scaling. One option is to use fit to calculate the minimum and maximum values of the feature, then use trans form to rescale the feature. There is no mathematical difference between the two options, but there is sometimes a practical benefit to keeping the operations separate because it allows us to apply the same transformation to different sets of the data. To achieve this, we use standardization to transform the data such that it has a mean, x̄, of 0 and a standard deviation, σ, of 1. However, it depends on the learning algorithm.

mean print "Standard deviation:", standardized. std Mean: 0. In this scenario, it is often helpful to instead rescale the feature using the median and quartile range. array [[0. transform features array [[ 0. Normalizer rescales the values on individual observations to have unit norm the sum of their lengths is 1. Solution Even though some choose to create polynomial and interaction features manually, scikit-learn offers a built-in method: Load libraries import numpy as np from sklearn. A simple example would be if we were trying to predict whether or not our coffee was sweet and we had two features: 1 whether or not the coffee was stirred and 2 if we added sugar.

Individually, each feature does not predict coffee sweetness, but the combination of their effects does. That is, a coffee would only be sweet if the coffee had sugar and was stirred. The effects of each feature on the target sweetness are dependent on each other. Solution In scikit-learn, use FunctionTransformer to apply a function to a set of features: Load libraries import numpy as np from sklearn. For example, we might want to create a feature that is the natural log of the values of the different feature. Solution Detecting outliers is unfortunately more of an art than a science. covariance import EllipticEnvelope from sklearn. Think of contamination as our estimate of the cleanliness of our data. If we expect our data to have few outliers, we can set contamination to something small. However, if we believe that the data is very likely to have outliers, we can set it to a higher value.

You can think of IQR as the spread of the bulk of the data, with outliers being observations far from the main concentration of data. Outliers are commonly defined as any value 1. Discussion There is no single best technique for detecting outliers. Instead, we have a collection of techniques all with their own advantages and disadvantages. Our best strategy is often trying multiple techniques e. If at all possible, we should take a look at observations we detect as outliers and try to understand them. Solution Typically we have three strategies we can use to handle outliers. How we handle them should be based on two aspects. First, we should consider what makes them an outlier. However, if we believe the outliers are genuine extreme values e. Second, how we handle outliers should be based on our goal for machine learning. For example, if we want to predict house prices based on features of the house, we might reasonably assume the price for mansions with over bathrooms is driven by a different dynamic than regular family homes.

Furthermore, if we are training a model to use as part of an online home loan web application, we might assume that our potential users will not include billionaires looking to buy a mansion. So what should we do if we have outliers? Think about why they are outliers, have an end goal in mind for the data, and, most importantly, remember that not making a decision to address outliers is itself a decision with implications. In this case, use a rescaling method more robust against outliers like RobustScaler. Solution Depending on how we want to break up the data, there are two techniques we can use. First, we can binarize the feature according to some threshold: Load libraries import numpy as np from sklearn.

For example, the 20 argument does not include the element with the value of 20, only the two values smaller than We can switch this behavior by setting the parameter right to True: Bin feature np. For example, we might believe there is very little difference in the spending habits of and year-olds, but a significant difference between and year-olds the age in the United States when young adults can consume alcohol. In that example, it could be useful to break up individuals in our data into those who can drink alcohol and those who cannot. Similarly, in other cases it might be useful to discretize our data into three or more bins. predict features View first few observations dataframe.

However, I wanted to point out that we can use clustering as a preprocessing step. array [[1. nan, 55]] Keep only observations that are not denoted by ~ missing features[~np. isnan features. For this reason, we cannot ignore missing values in our data and must address the issue during preprocessing. That said, we should be very reluctant to delete observations with missing values. Just as important, depending on the cause of the missing values, deleting observations can introduce bias into our data. There are three types of missing data: Missing Completely At Random MCAR The probability that a value is missing is independent of everything.

For example, a survey respondent rolls a die before answering a question: if she rolls a six, she skips that question. Missing At Random MAR The probability that a value is missing is not completely random, but depends on the information captured in other features. It is sometimes acceptable to delete observations if they are MCAR or MAR. preprocessing import StandardScaler from sklearn. However, we will typically get worse results than KNN: Load library from sklearn. First, we can use machine learning to predict the values of the missing data. To do this we treat the feature with missing values as a target vector and use the remaining subset of features to predict missing values. While we can use a wide range of machine learning algorithms to impute values, a popular choice is KNN. KNN is addressed in depth later in Chapter 14, but the short explanation is that the algorithm uses the k nearest observations according to some distance metric to predict the missing value.

The downside to KNN is that in order to know which observations are the closest to the missing value, it needs to calculate the distance between the missing value and every single observation. This is reasonable in smaller datasets, but quickly becomes problematic if a dataset has millions of observations. An alternative and more scalable strategy is to fill in all missing values with some average value. If we use imputation, it is a good idea to create a binary feature indicating whether or not the observation contains an imputed value. However, not all categorical data is the same. Sets of categories with no intrinsic ordering is called nominal. The problem is that most machine learning algorithms require inputs be numerical values. The k-nearest neighbor algorithm provides a simple example.

However, the distance calculation obviously is impossible if the value of xi is a string e. Instead, we need to convert the string into some numerical format so that it can be inputted into the Euclidean distance equation. However, when our classes have no intrinsic ordering e. The proper strategy is to create a binary feature for each class in the original feature. This is often called one-hot encoding in machine learning literature or dummying in statistical and research literature. In one-hot encoding, each class becomes its own feature with 1s when the class appears and 0s otherwise. Because our feature had three classes, one-hot encoding returned three binary features one for each class.

Finally, it is worthwhile to note that it is often recommended that after one-hot encoding a feature, we drop one of the one-hot encoded features in the resulting matrix to avoid linear dependence. The most common approach is to create a dictionary that maps the string label of the class to a number and then apply that map to the feature. It is important that our choice of numeric values is based on our prior information on the ordinal classes. In our solution, high is literally three times larger than low. Solution Use DictVectorizer: Import library from sklearn. This can be very helpful when we have massive matrices often encountered in natural language processing and want to minimize the memory requirements. For example, we might have a collection of documents and for each document we have a dictionary containing the number of times every word appears in the document.

array [[0, 2. array [[np. nan, 0. nan, We can accomplish this by treating the feature with the missing values as the target vector and the other features as the feature matrix. Alternatively, we can fill in missing values with the most frequent class of the feature. While less sophisticated than KNN, it is much more scalable to larger data. In either case, it is advisable to include a binary feature indicating which observations contain imputed values. Solution Collect more data. We cover evaluation metrics in a later chapter, so for now let us focus on class weight parameters, downsampling, and upsampling.

To demonstrate our solutions, we need to create some data with imbalanced classes. The result is 10 observations of Iris setosa class 0 and observations of not Iris setosa class 1 : Load libraries import numpy as np from sklearn. ensemble import RandomForestClassifier from sklearn. In downsampling, we randomly sample without replacement from the majority class i. For this reason, handling imbalanced classes is a common activity in machine learning. Our best strategy is simply to collect more observations—especially observations from the minority class. However, this is often just not possible, so we have to resort to other options. A second strategy is to use a model evaluation metric better suited to imbalanced classes.

Accuracy is often used as a metric for evaluating the performance of a model, but when imbalanced classes are present accuracy can be ill suited. For example, if only 0. Clearly this is not ideal. A third strategy is to use the class weighing parameters included in implementations of some models. This allows us to have the algorithm adjust for imbalanced classes. In upsampling we repeatedly sample with replacement from the minority class to make it of equal size as the majority class. In this chapter, we will cover strategies for transforming text into information-rich features. This is not to say that the recipes covered here are comprehensive. By Aishwarya Henriette ", "Parking And Going. By Karl Gautier", " Today Is The night. By Aishwarya Henriette', 'Parking And Going. By Karl Gautier', 'Today Is The night. replace ". In the real world we will most likely define a custom cleaning function e. text 'Masego Azra' Discussion Despite the strange name, Beautiful Soup is a powerful Python library designed for scraping HTML.

Typically Beautiful Soup is used scrape live websites, but we can just as easily use it to extract text data embedded in HTML. The full range of Beautiful Soup operations is beyond the scope of this book, but even the few methods used in our solution show how easily we can parse HTML code to extract the data we want. LoveIT', 'Right?!?! fromkeys i for i in range sys. maxunicode if unicodedata. category chr i. startswith 'P' For each string, remove any punctuation characters [string. In our solution, first we created a dictionary, punctuation, with all punctuation characters according to Unicode as its keys and None as its values. Next we translated all characters in the string that are in punctuation into None, effectively removing them. There are more readable ways to remove punctuation, but this somewhat hacky solution has the advantage of being far faster than alternatives. It is important to be conscious of the fact that punctuation contains information e. Removing punctuation is often a necessary evil to create features; however, if the punctuation is important we should make sure to take that into account.

Tomorrow is today. corpus import stopwords You will have to download the set of stop words the first time import nltk 6. By stemming our text data, we transform it to something less readable, but closer to its base meaning and thus more suitable for comparison across observations. NLTK uses the Penn Treebank parts for speech tags. Some examples of the Penn Treebank tags are: Tag Part of speech NNP Proper noun, singular NN Noun, singular or mass RB Adverb VBD Verb, past tense VBG Verb, gerund or present participle JJ Adjective PRP Personal pronoun Once the text has been tagged, we can use the tags to find certain parts of speech. The major downside of training a tagger is that we need a large corpus of text where the tag of each word is known. Constructing this tagged corpus is obviously labor intensive and is probably going to be a last resort. All that said, if we had a tagged corpus and wanted to train a tagger, the following is an example of how we could do it.

The corpus we are using is the Brown Corpus, one of the most popular sources of tagged text. To examine the accuracy of our tagger, we split our text data into two parts, train our tagger on one part, and test how well it predicts the tags of the second part: Load library from nltk. corpus import brown from nltk. tag import UnigramTagger from nltk. tag import BigramTagger from nltk. evaluate test 0. array ['I love Brazil. Bag-of-words models output a feature for every unique word in text data, with each feature containing a count of occurrences in observations. For example, in our solution the sentence I love Brazil. The text data in our solution was purposely small. Since our bag-of-words model creates a feature for every unique word in the data, the resulting matrix can contain thousands of features. This means that the size of the matrix can sometimes become very large in memory. This will save us memory when we have large feature matrices.

One of the nice features of CountVectorizer is that the output is a sparse matrix by default. CountVectorizer comes with a number of useful parameters to make creating bag- of-words feature matrices easy. First, while by default every feature is a word, that does not have to be the case. Instead we can set every feature to be the combination of two words called a 2-gram or even three words 3-gram. For example, 2,3 will return all 2- grams and 3-grams. Finally, we can restrict the words or phrases we want to consider to a certain list of words using vocabulary.

Solution Compare the frequency of the word in a document a tweet, movie review, speech transcript, etc. with the frequency of the word in all other documents using term frequency-inverse document frequency tf-idf. scikit-learn makes this easy with TfidfVectorizer: Load libraries import numpy as np from sklearn. However, if we want to view the output as a dense matrix, we can use. toarray array [[ 0. For example, if the word economy appears frequently, it is evidence that the document might be about economics. We call this term frequency tf.

In contrast, if a word appears in many documents, it is likely less important to any individual document. For example, if every document in some text data contains the word after then it is probably an unimportant word. We call this document frequency df. By combining these two statistics, we can assign a score to every word representing how important that word is in a document. There are a number of variations in how tf and idf are calculated. In scikit-learn, tf is simply the number of times a word appears in the document and idf is calculated as: 6. By default, scikit-learn then normalizes the tf-idf vectors using the Euclidean norm L2 norm. In this chapter, we will build a toolbox of strategies for handling time series data including tackling time zones and creating lagged time features. Discussion When dates and times come as strings, we need to convert them into a data type Python can understand.

One obstacle to strings representing dates and times is that the format of the strings can vary significantly between data sources. We can use the format parameter to specify the exact format of the string.

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Download Ebook Machine Learning With Python Cookbook Chris Albon Getting the books Machine Learning With Python Cookbook Chris Albon now is not type of challenging 11/10/ · Download Thid PDF Book: Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning 1st Edition by Chris Albon, for free. This Access full book title Python Machine Learning Cookbook by Chris Albon. Download full books in PDF and EPUB format. Python Machine Learning Cookbook Author: Chris Albon 09/10/ · ISBN Formats: PDF, EPub, Kindle, Audiobook. Get book Machine Learning with Python Cookbook: Practical. Solutions from Preprocessing to Deep Chris Albon. Notes Machine Learning About Chris Twitter ML Book ML Flashcards. RSS; Notes On Using Data Science & Machine Learning Check out my Machine Learning 29/04/ · machine-learning-with-python-cookbook Identifier-ark ark://t7tn5xz9p Ocr ABBYY FineReader (Extended OCR) Page_number_confidence Ppi Scanner ... read more

The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios. So how do we know which values to use? However, if your data is not linearly separable e. When we include candidate component values in the search space, they are treated like any other hyperparameter to be searched over. Finally, the degree parameter determines the maximum number of features to create interaction terms from in case we wanted to create an interaction term that is the combination of three features. Change language.

array [[1, 4], [2, 5]] Calculate inverse of matrix np. Uploaded by SaeedCollection95 on April 29, Handling Numerical Data. NumPy makes this easy with det. Privacy policy.