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Introduction to Principal Component Analysis (PCA) in Python

Python is no longer an unfamiliar word for professionals from the IT or Web Designing world. It’s one of the most widely used programming languages because of its versatility and ease of usage. It has a focus on object-oriented, as well as functional and aspect-oriented programming. Python extensions also add a whole new dimension to the functionality it supports. The main reasons for its popularity are its easy-to-read syntax and value for simplicity. The Python language can be used as a glue to connect components of existing programmes and provide a sense of modularity.Image SourceIntroducing Principal Component Analysis with Python  Principal Component Analysis definition   Principal Component Analysis is a method that is used to reduce the dimensionality of large amounts of data. It transforms many variables into a smaller set without sacrificing the information contained in the original set, thus reducing the dimensionality of the data.  PCA Python is often used in machine learning as it is easier for machine learning software to analyse and process smaller sets of data and variables. But this comes at a cost. Since a larger set of variables contends, it sacrifices accuracy for simplicity. It preserves as much information as possible while reducing the number of variables involved. The steps for Principal Component Analysis Python include Standardisation, that is, standardising the range of the initial variables so that they contribute equally to the analysis. It is to prevent variables with larger ranges from dominating over those with smaller ranges.  The next step involves complex matrix computation. It involves checking if there is any relationship between variables and shows if they contain redundant information or not. To identify this, the covariance matrix is computed. The next step is determining the principal components of the data. Principal Components are the new variables that are formed from the mixtures of the initial variables. The principal components are formed such that they're Uncorrelated, unlike the initial variables. They follow a descending order where the program tries to put as much information as possible in the first component, the remaining in the second, etc. It helps to discard components with low information and effectively reduces the number of variables. This comes at the cost of the principal components losing the meaning of the initial data. Further steps include computing the eigenvalues and discarding the figures with fewer eigenvalues, meaning that they have less significance. The remaining is a matrix of vectors that can be called the Feature Vector. It effectively reduces the dimensions since we take an eigenvalue. The last step involves reorienting the data obtained in the original axes to recast it along the axes formed by the principal components.Objectives of PCA  The objectives of Principal Component Analysis are the following:  Find and Reduce the dimensionality of a data set As shown above, Principal Component Analysis is a helpful procedure to reduce the dimensionality of a data set by lowering the number of variables to keep track of.  Identify New Variables Sometimes this process can help one identify new underlying pieces of information and find new variables for the data sets which were previously missed.  Remove needless Variables The process reduces the number of needless variables by eliminating those with very little significance or those that strongly correlate with other variables.Image SourceUses of PCA  The uses of Principal Component Analysis are wide and encompass many disciplines, for instance, statistics and geography with applications in image compression techniques etc. It is a huge component of compression technology for data and may be in video form, picture form, data sets and much more.  It also helps to improve the performance of algorithms as more features will increase their workload, but with Principal Component Analysis, the workload is reduced to a great degree. It helps to find correlating values since finding them manually in thousands of sets is almost impossible.  Overfitting is a phenomenon that occurs when there are too many variables in a set of data. Principal Component Analysis reduces overfitting, as the number of variables is now reduced. It is very difficult to carry out the visualisation of data when the number of dimensions being dealt with is too high. PCA alleviates this issue by reducing the number of dimensions, so visualisation is much more efficient, easier on the eyes and concise. We can potentially even use a 2D plot to represent the data after Principal Component Analysis. Applications of PCA  As discussed above, PCA has a wide range of utilities in image compression, facial recognition algorithms, usage in geography, finance sectors, machine learning, meteorological departments and more. It is also used in the medical sector to interpret and process Medical Data while testing medicines or analysis of spike-triggered covariance. The scope of applications of PCA implementation is really broad in the present day and age.  For example, in neuroscience, spike-triggered covariance analysis helps to identify the properties of a stimulus that causes a neutron to fire up. It also helps to identify individual neutrons using the action potential they emit. Since it is a dimension reduction technique, it helps to find a correlation in the activity of large ensembles of neutrons. This comes in special use during drug trials that deal with neuronal actions. Principal Axis Method  In the principal axis method, the assumption is that the common variance in communalities is less than one. The implementation of the method is carried out by replacing the main diagonal of the correlation matrix with the initial communality estimates. The initial matrix consisted of ones as per the PCA methodology. The principal components are now applied to this new and improved version of the correlation matrix.  PCA for Data Visualization Tools like Plotly allow us to visualise data with a lot of dimensions using the method of dimensional reduction and then applying it to a projection algorithm. In this specific example, a tool like Scikit-Learn can be used to load a data set and then the dimensionality reduction method can be applied to it. Scikit learn is a machine learning library. It has an arsenal of software and training machine learning algorithms along with evaluation and testing models. It works easily with NumPy and allows us to use the Principal Component Analysis Python and pandas library.  The PCA technique ranks the various data points based on relevance, combines correlated variables and helps to visualise them. Visualising only the Principal components in the representation helps make it more effective. For example, in a dataset containing 12 features, 3 represent more than 99% of the variance and thus can be represented in an effective manner.  The number of features can drastically affect its performance. Hence, reducing the amount of these features helps a lot to boost machine learning algorithms without a measurable decrease in the accuracy of results.PCA as dimensionality reduction  The process of reducing the number of input variables in models, for instance, various forms of predictive models, is called dimensionality reduction. The fewer input variables one has, the simpler the predictive model is. Simple often means better and can encapsulate the same things as a more complex model would. Complex models tend to have a lot of irrelevant representations. Dimensionality reduction leads to sleek and concise predictive models.  Principal Component Analysis is the most common technique used for this purpose. Its origin is in the field of linear algebra and is a crucial method in data projection. It can automatically perform dimensionality reduction and give out principal factors, which can be translated as a new input and make much more concise predictions instead of the previous high dimensionality input.In this process, the features are reconstructed; in essence, the original features don't exist. They are, however, constructed from the same overall data but are not directly compared to it, but they can still be used to train machine learning models just as effectively. PCA for visualisation: Hand-written digits  Handwritten digit recognition is a machine learning system's ability to identify digits written by hand, as on post, formal examinations and more. It's important in the field of exams where OMR sheets are often used. The system can recognise OMRs, but it also needs to recognise the student's information, besides the answers. In Python, a handwritten digit recognition system can be developed using moist Datasets. When handled with conventional PCA strategies of machine learning, these datasets can yield effective results in a practical scenario. It is really difficult to establish a reliable algorithm that can effectively identify handwritten digits in environments like the postal service, banks, handwritten data entry etc. PCA ensures an effective and reliable approach for this recognition.Choosing the number of components  One of the most important parts of Principal Component analysis is estimating the number of components needed to describe the data. It can be found by having a look at the cumulative explained variance ratio and taking it as a function of the number of components.  One of the rules is Kaiser's Stopping file, where one should choose all components with an eigenvalue of more than one. This means that variables that have a measurable effect are the only ones that get chosen.  We can also plot a graph of the component number along with eigenvalues. The trick is to stop including values when the slope becomes close to a straight line in shape.PCA as Noise Filtering  Principal Component Analysis has found a utility in the field of physics. It is used to filter noise from experimental electron energy loss (EELS) spectrum images. It, in general, is a method to remove noise from the data as the number of dimensions is reduced. The nuance is also reduced, and one only sees the variables which have the maximum effect on the situation. The principal component analysis method is used after the conventional demonising agents fail to remove some remnant noise in the data. Dynamic embedding technology is used to perform the principal component analysis. Then the eigenvalues of the various variables are compared, and the ones with low eigenvalues are removed as noise. The larger eigenvalues are used to reconstruct the speech data.  The very concept of principal component analysis lends itself to reducing noise in data, removing irrelevant variables and then reconstructing data which is simpler for the machine learning algorithms without missing the essence of the information input.  PCA to Speed-up Machine Learning Algorithms  The performance of a machine learning algorithm, as discussed above, is inversely proportional to the number of features input in it. Principal component analysis, by its very nature, allows one to drastically reduce the number of features of variables input, allows one to remove excess noise and reduces the dimensionality of the data set. This, in turn, means that there is a lot less strain on a machine learning algorithm, and it can produce near identical results with heightened efficiency. Apply Logistic Regression to the Transformed Data  Logistic regression can be used after a principal component analysis. The PCA is a dimensionality reduction, while the logical regression is the actual brains that make the predictions. It is derived from the logistic function, which has its roots in biology.  Measuring Model Performance After preparing the data for a machine learning model using PCA, the effectiveness or performance of the model doesn’t change drastically. This can be tested by several metrics such as testing true positives, negatives, and false positives and false negatives. The effectiveness is computed by plotting them on a specialised confusion matrix for the machine learning model. Timing of Fitting Logistic Regression after PCA  Principle component regression Python is the technique that can give predictions of the machine learning program after data prepared by the PCA process is added to the software as input. It more easily proceeds, and a reliable prediction is returned as the end product of logical regression and PCA. Implementation of PCA with Python scikit learn can be used with Python to implement a working PCA algorithm, enabling Principal Component Analysis in Python 720 as explained above as well. It is a working form of linear dimensionality reduction that uses singular value decomposition of a data set to put it into a lower dimension space. The input data is taken, and the variables with low eigenvalues can be discarded using Scikit learn to only include ones that matter- the ones with a high eigenvalue. Steps involved in the Principal Component Analysis Standardization of dataset. Calculation of covariance matrix. Complete the eigenvalues and eigenvectors for the covariance matrix. Sort eigenvalues and their corresponding eigenvectors. Determine, k eigenvalues and form a matrix of eigenvectors. Transform the original matrix. Conclusion  In conclusion, PCA is a method that has high possibilities in the field of science, art, physics, chemistry, as well as the fields of graphic image processing, social sciences and much more, as it is effectively a means to compress data without compromising on the value it gives. Only the variables that do not significantly affect the value are removed, and the correlated variables are consolidated.
Introduction to Principal Component Analysis (PCA) in Python
Abhresh

Introduction to Principal Component Analysis (PCA) in Python

Python is no longer an unfamiliar word for professionals from the IT or Web Designing world. It’s one of the most widely used programming languages because of its versatility and ease of usage. It has a focus on object-oriented, as well as functional and aspect-oriented programming. Python extensions also add a whole new dimension to the functionality it supports. The main reasons for its popularity are its easy-to-read syntax and value for simplicity. The Python language can be used as a glue to connect components of existing programmes and provide a sense of modularity.Image SourceIntroducing Principal Component Analysis with Python  Principal Component Analysis definition   Principal Component Analysis is a method that is used to reduce the dimensionality of large amounts of data. It transforms many variables into a smaller set without sacrificing the information contained in the original set, thus reducing the dimensionality of the data.  PCA Python is often used in machine learning as it is easier for machine learning software to analyse and process smaller sets of data and variables. But this comes at a cost. Since a larger set of variables contends, it sacrifices accuracy for simplicity. It preserves as much information as possible while reducing the number of variables involved. The steps for Principal Component Analysis Python include Standardisation, that is, standardising the range of the initial variables so that they contribute equally to the analysis. It is to prevent variables with larger ranges from dominating over those with smaller ranges.  The next step involves complex matrix computation. It involves checking if there is any relationship between variables and shows if they contain redundant information or not. To identify this, the covariance matrix is computed. The next step is determining the principal components of the data. Principal Components are the new variables that are formed from the mixtures of the initial variables. The principal components are formed such that they're Uncorrelated, unlike the initial variables. They follow a descending order where the program tries to put as much information as possible in the first component, the remaining in the second, etc. It helps to discard components with low information and effectively reduces the number of variables. This comes at the cost of the principal components losing the meaning of the initial data. Further steps include computing the eigenvalues and discarding the figures with fewer eigenvalues, meaning that they have less significance. The remaining is a matrix of vectors that can be called the Feature Vector. It effectively reduces the dimensions since we take an eigenvalue. The last step involves reorienting the data obtained in the original axes to recast it along the axes formed by the principal components.Objectives of PCA  The objectives of Principal Component Analysis are the following:  Find and Reduce the dimensionality of a data set As shown above, Principal Component Analysis is a helpful procedure to reduce the dimensionality of a data set by lowering the number of variables to keep track of.  Identify New Variables Sometimes this process can help one identify new underlying pieces of information and find new variables for the data sets which were previously missed.  Remove needless Variables The process reduces the number of needless variables by eliminating those with very little significance or those that strongly correlate with other variables.Image SourceUses of PCA  The uses of Principal Component Analysis are wide and encompass many disciplines, for instance, statistics and geography with applications in image compression techniques etc. It is a huge component of compression technology for data and may be in video form, picture form, data sets and much more.  It also helps to improve the performance of algorithms as more features will increase their workload, but with Principal Component Analysis, the workload is reduced to a great degree. It helps to find correlating values since finding them manually in thousands of sets is almost impossible.  Overfitting is a phenomenon that occurs when there are too many variables in a set of data. Principal Component Analysis reduces overfitting, as the number of variables is now reduced. It is very difficult to carry out the visualisation of data when the number of dimensions being dealt with is too high. PCA alleviates this issue by reducing the number of dimensions, so visualisation is much more efficient, easier on the eyes and concise. We can potentially even use a 2D plot to represent the data after Principal Component Analysis. Applications of PCA  As discussed above, PCA has a wide range of utilities in image compression, facial recognition algorithms, usage in geography, finance sectors, machine learning, meteorological departments and more. It is also used in the medical sector to interpret and process Medical Data while testing medicines or analysis of spike-triggered covariance. The scope of applications of PCA implementation is really broad in the present day and age.  For example, in neuroscience, spike-triggered covariance analysis helps to identify the properties of a stimulus that causes a neutron to fire up. It also helps to identify individual neutrons using the action potential they emit. Since it is a dimension reduction technique, it helps to find a correlation in the activity of large ensembles of neutrons. This comes in special use during drug trials that deal with neuronal actions. Principal Axis Method  In the principal axis method, the assumption is that the common variance in communalities is less than one. The implementation of the method is carried out by replacing the main diagonal of the correlation matrix with the initial communality estimates. The initial matrix consisted of ones as per the PCA methodology. The principal components are now applied to this new and improved version of the correlation matrix.  PCA for Data Visualization Tools like Plotly allow us to visualise data with a lot of dimensions using the method of dimensional reduction and then applying it to a projection algorithm. In this specific example, a tool like Scikit-Learn can be used to load a data set and then the dimensionality reduction method can be applied to it. Scikit learn is a machine learning library. It has an arsenal of software and training machine learning algorithms along with evaluation and testing models. It works easily with NumPy and allows us to use the Principal Component Analysis Python and pandas library.  The PCA technique ranks the various data points based on relevance, combines correlated variables and helps to visualise them. Visualising only the Principal components in the representation helps make it more effective. For example, in a dataset containing 12 features, 3 represent more than 99% of the variance and thus can be represented in an effective manner.  The number of features can drastically affect its performance. Hence, reducing the amount of these features helps a lot to boost machine learning algorithms without a measurable decrease in the accuracy of results.PCA as dimensionality reduction  The process of reducing the number of input variables in models, for instance, various forms of predictive models, is called dimensionality reduction. The fewer input variables one has, the simpler the predictive model is. Simple often means better and can encapsulate the same things as a more complex model would. Complex models tend to have a lot of irrelevant representations. Dimensionality reduction leads to sleek and concise predictive models.  Principal Component Analysis is the most common technique used for this purpose. Its origin is in the field of linear algebra and is a crucial method in data projection. It can automatically perform dimensionality reduction and give out principal factors, which can be translated as a new input and make much more concise predictions instead of the previous high dimensionality input.In this process, the features are reconstructed; in essence, the original features don't exist. They are, however, constructed from the same overall data but are not directly compared to it, but they can still be used to train machine learning models just as effectively. PCA for visualisation: Hand-written digits  Handwritten digit recognition is a machine learning system's ability to identify digits written by hand, as on post, formal examinations and more. It's important in the field of exams where OMR sheets are often used. The system can recognise OMRs, but it also needs to recognise the student's information, besides the answers. In Python, a handwritten digit recognition system can be developed using moist Datasets. When handled with conventional PCA strategies of machine learning, these datasets can yield effective results in a practical scenario. It is really difficult to establish a reliable algorithm that can effectively identify handwritten digits in environments like the postal service, banks, handwritten data entry etc. PCA ensures an effective and reliable approach for this recognition.Choosing the number of components  One of the most important parts of Principal Component analysis is estimating the number of components needed to describe the data. It can be found by having a look at the cumulative explained variance ratio and taking it as a function of the number of components.  One of the rules is Kaiser's Stopping file, where one should choose all components with an eigenvalue of more than one. This means that variables that have a measurable effect are the only ones that get chosen.  We can also plot a graph of the component number along with eigenvalues. The trick is to stop including values when the slope becomes close to a straight line in shape.PCA as Noise Filtering  Principal Component Analysis has found a utility in the field of physics. It is used to filter noise from experimental electron energy loss (EELS) spectrum images. It, in general, is a method to remove noise from the data as the number of dimensions is reduced. The nuance is also reduced, and one only sees the variables which have the maximum effect on the situation. The principal component analysis method is used after the conventional demonising agents fail to remove some remnant noise in the data. Dynamic embedding technology is used to perform the principal component analysis. Then the eigenvalues of the various variables are compared, and the ones with low eigenvalues are removed as noise. The larger eigenvalues are used to reconstruct the speech data.  The very concept of principal component analysis lends itself to reducing noise in data, removing irrelevant variables and then reconstructing data which is simpler for the machine learning algorithms without missing the essence of the information input.  PCA to Speed-up Machine Learning Algorithms  The performance of a machine learning algorithm, as discussed above, is inversely proportional to the number of features input in it. Principal component analysis, by its very nature, allows one to drastically reduce the number of features of variables input, allows one to remove excess noise and reduces the dimensionality of the data set. This, in turn, means that there is a lot less strain on a machine learning algorithm, and it can produce near identical results with heightened efficiency. Apply Logistic Regression to the Transformed Data  Logistic regression can be used after a principal component analysis. The PCA is a dimensionality reduction, while the logical regression is the actual brains that make the predictions. It is derived from the logistic function, which has its roots in biology.  Measuring Model Performance After preparing the data for a machine learning model using PCA, the effectiveness or performance of the model doesn’t change drastically. This can be tested by several metrics such as testing true positives, negatives, and false positives and false negatives. The effectiveness is computed by plotting them on a specialised confusion matrix for the machine learning model. Timing of Fitting Logistic Regression after PCA  Principle component regression Python is the technique that can give predictions of the machine learning program after data prepared by the PCA process is added to the software as input. It more easily proceeds, and a reliable prediction is returned as the end product of logical regression and PCA. Implementation of PCA with Python scikit learn can be used with Python to implement a working PCA algorithm, enabling Principal Component Analysis in Python 720 as explained above as well. It is a working form of linear dimensionality reduction that uses singular value decomposition of a data set to put it into a lower dimension space. The input data is taken, and the variables with low eigenvalues can be discarded using Scikit learn to only include ones that matter- the ones with a high eigenvalue. Steps involved in the Principal Component Analysis Standardization of dataset. Calculation of covariance matrix. Complete the eigenvalues and eigenvectors for the covariance matrix. Sort eigenvalues and their corresponding eigenvectors. Determine, k eigenvalues and form a matrix of eigenvectors. Transform the original matrix. Conclusion  In conclusion, PCA is a method that has high possibilities in the field of science, art, physics, chemistry, as well as the fields of graphic image processing, social sciences and much more, as it is effectively a means to compress data without compromising on the value it gives. Only the variables that do not significantly affect the value are removed, and the correlated variables are consolidated.
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Top 12 Python Packages for Machine Learning

Lovers of vintage movies would have definitely heard of the Monty Python series. The programming language that it inspired continues to remain among the most popular languages. Guess why Python has consistently topped the charts of the most popular programming languages? Because of its rich environment of libraries and tools, its easy code readability and the fact that it is so easy to pick up.  You name the domain, and you will get Python libraries available, to help you out in solving problems. Right from Artificial Intelligence, Data Science, Machine Learning, Image Processing, Speech Recognition, Computer Vision and more, Python has numerous uses. These libraries and frameworks are open source and can be easily integrated with the development environment that one has.These software frameworks, the platforms which provides necessary libraries and code components, are backbones for devloping applications. Read on to see which are the top ML frameworks and libraries in Python.1. Numpy As the name implies, this is the library which supports numerical calculations and tasks. It supports array operations and basic mathematical functions on the array and other data types of Python. The basic data type of this library is ndArray object.   Numpy has many advantages: The base data structure is N –Dimensional array. Rich functions to handle the N-dimensional array effectively. Supports integration of C, C++ and other language code fragments. Supports many functions related to linear algebra, random numbers, transforms, statistics etc. Disadvantages: No GPU and TPU support. Cannot automatically calculate the derivatives which is required in all ML algorithms. Numpy performance goes down when high complex calculations are required. 2. PandasThis is the most useful library for data preprocessing and preparing the data for the Machine Learning algorithms. The data from various files like CSV, Excel, Data etc. can be easily read using Pandas. The data is available in a spreadsheet like area, which makes processing easy. There are three basic data structures at the core of Pandas library: Series - One-dimensional array like object containing data and label (or index). Dataframe - Spreadsheet-like data structure containing an order collection of columns. It has both a row and column index. Panel – Collection of dataframes but rarely used data structure. Advantages: Structured data can be read easily. Great tool for handling of data. Strong functions for manipulation and preprocessing of data. Data Exploration functions help in better understanding data. Data preprocessing capabilities help in making data ready for the application of ML algorithms. Basic Plotting functions are provided for visualization of data.  Datasets can be easily joined or merged. The functions of Pandas are optimized for large datasets. Disadvantages: Getting to know the Pandas functionalities is time consuming. The syntax is complex when multiple operations are required. Support for 3D metrics is poor. Proper documentation is not available for study. 3. Matplotlib Matplotlib is an important Python library which helps in data visualization. Understanding the data is very important for a data scientist before devising any machine learning based model. This library helps in understanding the data in a visual way. Data can be visualized using various graphical methods like line graph, bar graph, pie chart etc. This is a 2D visualization library with numerous ways of visualizing data. Image SourceAdvantages: Simple and easy to learn for beginners. Integrated with Pandas for visualization of data in effective way. Various plots are provided for better understanding of data like Bar Chart, Stacked Bar chart, Pie chart, Scatter Plot etc. Forms a base for many advanced plotting libraries. Supports storing of the various graphs as images so that they can be integrated with other applications. Can plot timeseries data (with date) very easily. Disadvantages: Complex Syntax for plotting simple graphs. The code becomes lengthy and complex for visualizations. Support for plotting of categorial data is not provided. It is a 2D visualization library. When multiple fields are required to be plotted and visualized effectively, matplotlib code can become lengthy. Managing multiple figures is difficult. 4. Seaborn Visualizations are made simpler and more advanced with the help of Seaborn library. The base for Seaborn is Matplotlib. It is a boon for programmers as statistical visualizations are simplified. Image sourceAdvantages: Best high-level interface for drawing statistical graphics. Provides support for plotting of categorial data effectively. The library provides default themes and many visualization patterns. Multiple figures are automatically created. The syntax is very simple and compact. There are many methods to integrate with Pandas dataframe, making this library most useful for visualization. Disadvantages: Memory issues due to creation of multiple figures. Less customizable and flexible as compared to Matplotlib. Scalability issues. 5. Scipy   Scipy is a Scientific Python library based on Numpy. It has functions which are best suitable for Mathematics, Science and Engineering. Many libraries are provided for Image and Signal Processing, Fourier Transform, Linear Algebra, Integration and Optimization. The functions are useful for ML algorithms and programs. Advantages: The base library is Numpy. Many ML related functions are provided like Linear Algebra, Optimization, Compressed Sparce Data Structure etc. Useful Linear Algebra functions are available which are required for implementation of ML related algorithms. The functions can be applied with Pandas Dataframe directly. Disadvantages: Complex functions are available and domain knowledge is needed to understand and implement these functions. There are performance issues when data size increases. Many other effective alternative libraries are available with the needed functionality. 6. Scikit-Learn Scikit-Learn is a useful open access library for use to Python developers. It is an extensive and popular library with many Machine Learning Supervised and Unsupervised algorithms implemented. These algorithms can be fine-tuned with the help of hyperparameters. This library contains many useful functions for preprocessing of data, useful metrics to measure performance of algorithms and optimization techniques.  Advantages: It is a general Machine Learning library built on top of Numpy, Pandas and Matplotlib. Simple to understand and use even for novice programmers. Useful Machine Learning Algorithms, both Supervised and Unsupervised, are implemented. Popular library for doing Machine Learning related tasks. Rich in Data Preprocessing and Data Sampling functions and techniques. Plethora of evaluation measures implemented to track the performance of algorithms. Very effective for quick coding and building Machine Learning Models. Disadvantages: Scikit learn, as is based on Numpy, requires additional support to run on GTP and TPU Performance is an issue with size of data. Best suitable for basic Machine Learning applications. This library may be useful if one wants to write easy code, but it’s not the best choice for more detailed learning. 7. NLTK Natural Language processing is a great field of study for developers who like to research and challenge themselves. This library provides a base for Natural Language processing by providing simple functionalities to work with and understand languages.Advantages: Very simple to use for processing natural language data. Many basic functionalities like tokenizing the words, removal of stop words, conversion to word vectors etc. are provided which forms the basis to start with natural language processing models. It is an amazing library to play with natural language using Python. It has more than 50 trained models and lexical resources like wordnet available for use. Rich discussion forums and many examples are available to discuss how to use this library effectively. Disadvantages: It is based on string processing, which itself has many limitations. Slower as compared to other Natural Language processing libraries like Spacy.8. Keras Keras is a library written in Python for Neural Network programming. It offers a very simple interface to code the neural network and related algorithms. It is an incredibly popular library for Deep Learning algorithms, models and applications and can also be combined with various deep learning frameworks. It provides support for GPU and TPU computation of algorithms. The API provided is simple, same as Scikit-learn. Keras is totally based on Models and Graphs. A model has Input, output and intermediate layers to perform the various tasks as per requirement. Effective functionalities and models provided to code deep learning algorithms like Neural Network, Recurrent Neural Network, Long Short-Term memory, Autoencoders etc. Allows to create products easily supporting multiple backends Supports multi-platform use. Can be used with TensorFlow, can be used in browser using web based keras and provides native ML support for iPhone app development. 9. TensorFlow TensorFlow is the talk of the town because of its capabilities suitable for Machine Learning and Deep Learning models. It is one of the best, and most popular frameworks, adopted by companies around the world for Machine Learning and Deep Learning. Its support for Web as well as Mobile application coupled with Deep Learning models has made it popular among engineers and researchers. Many giants like IBM, Dropbox, Nvidia etc. use TensorFlow for creating and deploying Machine Learning Models. This library has many applications like image recognition, video analysis, speech recognition, Natural Language Processing, Recommendation System etc. TensorFlow lite and TensorFlow JS has made it more popular for web applications and Mobile Applications. Advantages Developed by Google, it is one of the best deep learning frameworks. Simple Machine Learning tasks are also supported in TensorFlow. Supports many famous libraries like scikit learn, Keras etc. which are part of TensorFlow. The basic unit is Tensor which is an n-dimensional array. The basic derivatives are inherently computed which helps in developing many Machine learning Models easily. The models developed are supported on CPT, TPU and GPU. Tensorboard is the effective tool for data visualization. Many other supported tools are available to facilitate Web Development, App Development and IoT Applications using Machine Learning. Disadvantages Understanding Tensor and computational graphs is tedious. Computational graphs make the code complex and sometimes face performance problems. 10. Pytorch A popular Python framework, Pytorch supports machine learning and deep learning algorithms and is a scientific computing framework. This is a framework which is widely used by Twitter, Google and Facebook. The library supports complex Tensor computations and is used to construct deep neural networks. Advantages: The power of Pytorch lies in construction of Deep Neural Networks. Rich functions and utilities are provided to construct and use Neural Networks. Powerful when it comes to creation of production ready models. It supports GPU operations with rich math-based library functions. Unlike Numpy, it provides the functions which calculates gradient of the function, useful for the construction of the neural network. Provides support for Gradient based optimization which helps in scaling up the models easily to large data. Disadvantages It is a complex framework, so learning is difficult. Documentation support for learning is not readily available. Scalability may be an issue as compared to TensorFlow. 11. Theano Theano is a library for evaluating and optimizing the mathematical computations. It is based on NumPy but provides support for both the GPU and CPU. Advantages: It is a fast computation library in Python. Uses native libraries like BIAS to turn the code in faster computation. Best suited to handle computations in Deep Learning algorithms. Industry standard for Deep Learning research and development. Disadvantages: It is not very popular among researchers as it is one of the older frameworks. It is not as easy to use as TensorFlow.12. CNTK CNTK is Microsoft’s Cognitive Toolkit for the development of Deep Learning based models. It is a commercial distributed deep learning tool. Advantages: It is a distributed open-source deep learning framework. Popular models like Deep Neural Network, Convolutional Neural Network models can be combined easily to form new models. Provides interface with C, C++ and Java to include Machine Learning models. Can be used to build reinforcement learning models as wide functions are available. Can be used to develop GAN (Generative Adversarial Networks). Provides various ways to measure the performance of the models built. High accuracy parallel computation on Multiple GPU is provided. Disadvantages: Proper documentation is not available. There is inadequate community support. Conclusion: Python, being one of the most popular languages for the development of Machine Learning models, has a plethora of tools and frameworks available for use. The choice of tool depends on the developer’s experience as well as the type of application to be developed. Every tool has some strong points and some weaknesses, so one has to carefully choose the tool or framework for the development of Machine Learning based applications. The documentation and support available are also important criteria to be kept in mind while choosing the most appropriate tool. 
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Top 12 Python Packages for Machine Learning

Lovers of vintage movies would have definitely hea... Read More

What Is Memoization in Python

The term memoization gets its name from the Latin word memorandum meaning — ‘to be remembered’. Donald Michie, a British researcher in AI, introduced the term in the year 1968. It may look like a misspelling of the word memorization, but it comprises recording a value to look upon the function later. Above all, it is often a crucial technique in solving problems using Dynamic Programming.Definition of Memoization Memoization is an efficient software optimization technique used to speed up programs. It allows you to optimize a python function by catching its output based on the supplied input parameters. Memoization ensures that a method runs for the same input only once. Moreover, it keeps the output records for the given set of inputs in a hash map. That means, when you memorize a function, it will only compute the output once for every set of parameters called-with. Fibonacci sequence  A Fibonacci sequence is a series where each term is the sum of the preceding two terms. It plays a vital role in testing the memorization decorator recursively. We begin by defining the python function that calculates the nth Fibonacci number.  Program to find Fibonacci Series using recursive functions.def fibonacci(n):      if (n 
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What Is Memoization in Python

The term memoization gets its name from the Latin ... Read More

What are Membership Operators in Python

The membership operators are, as the name explains, used to verify a value membership. The operators are used to figure out if there are two forms of value as a part of a sequence, such as string or list membership operators: in and not in. To check whether two variables point to the same location or not, identity operators are used. Two forms of identity operators are is and is not. In general, operators are used to work on Python values and variables. These are regular symbols used for logical and arithmetical operations.Identity Operators: The Python identity operators are used to figure out whether a value is of a certain class or type. Typically, they are used for evaluating the type of information in a given variable. For example, to make sure you work with the variable type, you can combine identity operators with the built-in type() function. Python’s two identity operators are (is, is not).is: When evaluated, is Operator in Python returns true if the variables point to the same variable on either side of the operator and return false otherwise. Example 1: x = 5  if (type(x) is int):  print(“true”)  else:  print(“false”) Output: true Example 2: x =6  if(type(x) is int):  print("true")  else:  print("false") Output: true Example 3: list1 = [9, 8, 7, ‘i’]  list2 = list1  if list1 is list2:  print(“True”)  else:  print(“False”) Output: True The output here is true because list2 also refers to a list1 referenced by the variable list1. We may also use the is operator to verify if two python objects of the same type have other functions such as the type() function. is not: The operator ‘is not’ is the exact opposite of ‘is operator’ in Python. When evaluated, the operator returns false if the variables point to the same object on either side of the operator and return true otherwise. Example 1: x = 5.2  if (type(x) is not int):  print(“true”)  else:  print(“false”) Output: true Example 2: x =7.2  if(type(x) is not int):  print("true")  else:  print("false") Output: true Example 3: new_list = [9,8,7, 'i']  new_tuple = (9,8,7, 'i')  type(my_new_tuple)  if type(my_new_list) is not type(my_new_tuple):      print('True!, They are not of the same type')  else:      print("False, They are the same type") Output: True!, They are not of the same type Since the tuple and the list are not the same and the operator does not check their inequality, it returns True. Let us see a combined example of “is” and “is not”. Example: x = "identity operator"  if (type(x) is str):       print ("This is a string")   else:       print ("This is not a string")   y=987  if (type(y) is not str):       print ("This is a string")   else:       print ("This is not a string") Output: This is a string  This is not string Declare the value for variable x and y. Use the operator “is” to check if the value of x is the same as y. Next, we use the operator “is not” to check if the value of x is not the same as y. Example 2: a1 = 10  b1 = 10  a2 = ‘PythonProgramming’  b2 = ‘Programming’  a3 = [1,2,6]  b3 = [1,2,3]  print(a1 is not b1)  print(a2 is b2)  print(a3 is b3) Output: True  False  False Membership Operators: These operators evaluate membership in lists, sequences, or tuples in one sequence. In Python, there are two membership operators. (in, not in). It displays the result in the given sequence or string centred on the present variable.Membership Operators as a whole comprise a number of different operators. in Operator: It tests whether or not the value is present in the data sequence. It analyses the true value if the component is in the series and the false value if the component is not present.  Example 1: list1 = ['Aman', 'Bhuvan', 'Ashok', 'Vijay', 'Anil']  if 'Aman' in list1: print('Name Aman exists in list1') Output: Name Aman exists in list1 Example 2: list1=[1,2,4,5]  list2=[6,7,9]  for item in list1:  if item in list2:  print("overlapping")      else:  print("not overlapping") Output: not overlapping Example 3: new_list = [1,2,3,'a']  # loop around the list  for i in new_list:      print(i) Output: 1  2  3  a The in operator allows the variable i to refer to every element in the list iterated by the for loop. You have to think that the operator is used to check whether or not an element is present in a sequence, but what exactly happens? Well, when used in various ways, in a loop and in a conditional statement like if statement, the operator behaves differently. Let us remove the in operator in the example and modify it. Example: def overlapping(list1,list2):  c=0  d=0  for i in list1:  c+=1  for i in list2:  d+=1  for i in range(0,c):  for j in range(0,d):  if(list1[i]==list2[j]):  return1  return 0  list1=[1,2,3,4,5]  list2=[6,7,8,9]  if(overlapping(list1,list2)):  print("overlapping")  else:  print("not overlapping")  Output: not overlapping not in Operator: This operator verifies that a value is not present in a sequence. This is exactly contrary to the in operator. It evaluates to true when the element is not found or missing from the sequence and returns false when the element is found in the data sequence. The searchable element is the left operand and the right operand is the sequence in which it is searched. Example 1: x = 'Hello world'  y = {1:'a',2:'b'}  print('H' in x)  print('hello' not in x)  print(1 in y)  print('a' in y) Output: True  True  True  False Example 2: list=[10, 20, 30, 40, 50];  if( x not in list):  print("x is NOT present in the given list")  else:  print("x is present in the given list")  if( y in list):  print("y is present in the given list")  else:  print("y is NOT present in the given list") Output: x is NOT present in the given list  y is present in the given listExample 3: my_new_list = [1,2,3, 'a']  event = 'Studytonight'  if event not in my_new_list:      print('True') Output: True event not in my_new_list returns the negation of the in operator. The if condition checks if the special variable is included in the list or not. Since the special element isn’t in the list, it will return true. Example 4: list_one = [1, 2, 3]  list_two = [1, 2, 3]  list_one is not list_two Output: True This is because the lists apply to different objects in different memory locations.Conclusion: Identity and membership operators are useful to verify certain elements in a series of data and to verify the data identity respectively. Identity operators can be used with the type() function to check if a variable is of a certain type or class prior to any operation. 
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What are Membership Operators in Python

The membership operators are, as the name explains... Read More

What Is Multithreading in Python

Basically, a thread is an independent flow of execution. Multithreading allows the execution of multiple parts of a program at the same time. For example, if you are playing a game on your PC, the whole game is one process but it contains several threads, which are used by the user, which are used synchronously to run the opponent, etc.These are all separate threads responsible for carrying out these different tasks in the same program. Each process has a main thread that’s always running. This main thread creates objects for the child thread. The thread of the child is also started by the main thread. How to Take Advantage of Multithreaded Programming and Parallel Programming in C/C++ The software needs to make decisions quickly in many applications. Parallel programming in C/C++ and multiple threading are the best ways to do this. What Is Parallel Programming? It is the use of multiple resources, in this case, processors, to solve a problem. The programming of this type takes a problem, splits it into a number of smaller steps, provides instructions and processors to simultaneously execute the solutions. It is also a form of programming that provides the same programming results, but in less time and more effectively. This programming is used by many computers, such as laptops and private desktops in their hardware to ensure that tasks are done in the background quickly. Using parallel structures, all processes are speeded up, increasing performance and energy to produce fast results. Parallel computing is easier than concurrent programming simply because the effects are the same in less time. This is extremely important because parallel processes are necessary to collect large data in data sets that can be processed easily or to solve complicated problems. Parallel programming has several disadvantages. The first is that it can be hard to understand. It takes time for programming that aims at parallel architectures first to be fully understood. Moreover, code tweaking is not simple and must be modified to improve performance for various target architectures. Consistent results are also hard to estimate because the communication of results for certain architectures may be problematic. For those establishing many processors for different architectures, the energy consumption is a challenge; a range of cooling technologies are necessary to cool down the parallel clusterConcurrent vs Parallel: How Does Parallel Programming Differ From Multithreaded Programming? There’s a broad concept of parallel programming. It is possible to define various types of processes, operating on different machines or on the same machine. Multithreading refers specifically to the simultaneous execution of more than one series of instructions (thread). Multithreaded programming consists of several concurrent threads for execution. The threads could be running on one processor, or there might be multiple threads running on multiple processor cores. Multithreading on a single processor gives the illusion of running in parallel. In reality, a scheduling algorithm is used to switch the processor. Or it switches on how the threads were prioritized by a combination of external inputs (interrupts). Multithreading is completely parallel on many processor cores. In order to accomplish the result more effectively, individual microprocessors operate together. There are many overlapping, simultaneous activities.Why Is Multithreaded Programming Important? Many threads in a network share the same data space with the main thread and thus can share or interact better than if different processes are involved. Threads often relate to lightweight processes and don’t need a lot of overhead memory. Processors Are at Maximum Clock Speed: The full clock speed of the processors is achieved. Parallelism is the only way to break out of CPUs. Multithreading allows multiple, concurrent threads to be spawned by a single processor. Each thread executes its own sequence of instructions. They all access the same common memory space and if necessary, communicate with each other. The threads should be optimized carefully for performance. Parallelism Is Important For AI: When we reach the limits of what can be achieved on a single processor, multiple processor cores are used to perform additional tasks. This is critical for AI in particular. Autonomous driving is an example of this. Humans have to make fast choices in a traditional car, and human reaction time is 0.25 seconds on average. AI must take these decisions very rapidly within autonomous vehicles — within tenths of a second. The best way to ensure that these decisions are taken in a necessary time frame is by use of C multi-threading and parallel programming in C.C/C++ Languages Now Include Multithreading Libraries: The switch from single-threaded to multi-threaded programs increases complexity. Programming languages, including C and C++, have been developed to allow the use and management of several threads. Both C and C++ now have libraries for the thread. Modern C++ has made parallel programming much simpler in particular. A basic threading library was included in C++11. C++17 has added parallel algorithms—and parallel implementations of many standard algorithms. Common Multithreaded Programming Issues: Multithreading in C has many advantages. But there are also concurrency issues that may arise, and these mistakes will impact the software and lead to safety risks. Multithreading is very useful, but cannot be used everywhere for saving time and better performance. It can also only be used if there is no dependency between threads. Through the use of multiple threads, you can get more from a single processor. But then these threads have to synchronize their work into a common memory. That can be hard to do — and much harder to do without concurrency issues. These potential issues are unlikely to be found by conventional testing and debugging methods. You might run a test or a debugger once—and you won’t see any errors. However, there’s a bug when you run it again. You could potentially continue to keep testing and still may not find the issue. Here are two common forms of multi-threading problems with testing and debugging alone. Race Conditions (Including Data Race): When two threads concurrently access a common variable, a race condition occurs. The variable is read in the first thread and the variable reads the same value in the second thread. Then the first and second threads work on the value and they aim to see which thread will add the last value to the shared variable. The thread value is retained since the thread is written over the value that the previous thread wrote.  A data race occurs whenever two or more threads access shared data and try to change it simultaneously — without correct synchronization. This kind of mistake may cause crashes or corruption of memory. The most common symptom of a race condition is that variables shared by multiple threads are unpredictable values. This is due to the unpredictability of the sequence of threads. Sometimes one thread wins and sometimes the other. Execution functions properly on all occasions. Also, the value of the variable is right if each thread is executed separately. Deadlock: A deadlock occurs if two threads at the same time lock a different variable and then try to lock the variable already locked by the other thread. Each thread then stops running and waits to release the variable for the other thread. Since each thread holds the variable that the other thread requires, there is nothing happening, and the threads remain deadlocked. This type of error can cause programs to get stuck. How to Avoid Multithreaded Programming Defects in C/C++ The programming languages C and C++ have introduced enabling of multi-threading. But there are additional steps you would have to take to ensure stable multithreading without any errors or security problems. Apply a Coding Standard that Covers Concurrency: The key to secure multithreading in C/C++ is to use a coding standard. Standards such as CERT allow possible security vulnerabilities to be detected easily. CERT also protects concurrency sections. Run Dataflow Analysis on Threads: Dataflow Analysis will allow you to identify threads of redundancy and concurrency. Dataflow analysis is also used in static analysis. Static source code analyses are used to evaluate a program’s runtime behaviour. Data flow analysis may detect serious problems, including data races and deadlocks. Use a Static Analyzer: With a static analyzer you can apply a secure code standard and perform automated dataflow analysis. A static analysis tool can detect potential errors, and you will find the bugs you might not have seen before. The method of identification of possible multi-threading errors is far more accurate. In the earlier development phase, you can use static analyzers if errors are cheaper to fix. How to Take Advantage of Parallel Programming in C/C++ Helix QAC and Klocwork make parallel programming and multi-threading simple for you without thinking about security problemsHelix QAC was the preferred tool to ensure compliance with MISRA, AUTOSAR, and other functional safety standards. Klocwork static application security testing (SAST) for C, C++, C#, and Java identifies software security and quality issues and problems with reliability that lead to the implementation of standards enforcement. They will: Give you more out of your processors. Build AI that can think fast. Manage the complexity of your code. Conclusion: Multithreading is very useful, but it cannot be used everywhere to save time and improve efficiency. It can only be used if there is no dependence between threads. Importing the threading module enables multithreading in Python. Parallel computing is easier than concurrent programming simply because you get the same effects in less time. Dataflow Analysis and static analyzer helps to avoid Multithreaded Programming defects in C/C++. You can take advantage of parallel programming in C/C++ using Helix QAC and Klocwork. 
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What Is Multithreading in Python

Basically, a thread is an independent flow of exec... Read More

What Is Operator Overloading in Python?

Programmers can straightaway use pre-defined operators like +, =, *, >, Returns True if the values at operand on the left are greater than the value of operator are equal.print(a >= b)False= b)False
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What Is Operator Overloading in Python?

Programmers can straightaway use pre-defined opera... Read More