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Essential Steps to Mastering Machine Learning with Python

One of the world’s most popular programming languages today, Python is a great tool for Machine Learning (ML) and Artificial Intelligence (AI). It is an open-source, reusable, general-purpose, object-oriented, and interpreted programming tool. Python’s key design ideology is code readability, ease of use and high productivity. The latest trend shows that the interest in Python has grown significantly over the past five years. Python is the top choice for ML/AI enthusiasts when compared to other programming languages.   Image source: Google Trends - comparing Python with other tools in the marketWhat makes Python a perfect recipe for Machine Learning? Python can be used to write Machine Learning algorithms and it computes pretty accurately. Python’s concise and easy readability allows the writing of reliable code very quickly. Another reason for its popularity is the availability of various versatile, ready-to-use libraries.  It has an excellent library ecosystem and a great tool for developing prototypes. Unlike R, Python is a general-purpose programming language which can be used to build web applications and enterprise applications.  The community of Python has developed libraries that adhere to a particular area of data science application. For instance, there are libraries available for handling arrays, performing numerical computation with matrices, statistical computing, machine learning, data visualization and many more. These libraries are highly efficient and make the coding much easier with fewer lines of codes. Let us have a brief look at some of the important Python libraries that are used for developing machine learning models. NumPy: One of the fundamental packages for numerical and scientific computing. It is a mathematical library to work with n-dimensional arrays in Python. Pandas: Provides highly efficient, easy-to-use DataFrame for DataFrame manipulations and Exploratory Data Analysis (EDA). SciPy: SciPy is a functional library for scientific and high-performance computations. It contains modules for optimization and for several statistical distributions and tests. Matplotlib: It is a complete plotting package that provides 2D plotting as well as 3D plotting. It can plot static and interactive plots. Seaborn: Seaborn library is based on Matplotlib. It is used to plot more elegant statistical visualization.  StatsModels: The StatsModels library provides functionalities for estimation of various statistical models and conducting different statistical tests. Scikit-learn: Scikit-Learn is built on NumPy, SciPy and Matplotlib. Free to use, overpowered and provides various range of supervised and unsupervised machine learning algorithms. One should also take into account the importance of IDEs specially designed for Python for Machine Learning. The Jupyter Notebook - an open-source web-based application that enables ML enthusiasts to create, share, quote, visualize, and live-code their projects.  There are various other IDEs that can be used like PyCharm, Spyder, Vim, Visual Studio Code. For beginners, there is a nice simple online compiler available – Programiz. Roadmap to master Machine Learning Using Python Learn Python: Learn Python from basic to advanced. Practice those features that are important for data analysis, statistical analysis and Machine Learning. Start from declaring variables, conditional statements, control flow statements, functions, collection objects, modules and packages. Deep dive into various libraries that are used for statistical analysis and building machine learning models. Descriptive Analytics : Learn the concept of descriptive analytics, understand the data, learn to load structured data and perform Exploratory Data Analysis (EDA). Practice data filtering, ordering, grouping, multiple joining of datasets. Handle missing values, prepare visualization plots in 2D or 3D format (from libraries like seaborn, matplotlib) to find hidden information and insights. Take a break from Python and Learn Stats - Learn the concept of the random variable and its important role in the field of analytics. Learn to draw insights from the measures of dispersion (mean, median, mode, quartiles and other statistical measures like confidence interval and distribution functions. The next step is to understand probability & various probability distributions and their crucial role in analytics. Understand the concept of various hypothesis tests like t-tests, z-test, ANOVA (Analysis of Variance), ANCOVA (Analysis of Covariance), MANOVA (Multivariate Analysis of Variance), MANCOVA (Multivariate Analysis of Covariance) and chi-square test.  Understand Major Machine Learning AlgorithmsImage sourceDifferent algorithms have different tasks. It is advisable to understand the context and select the right algorithm for the right task. Types of ML ProblemDescriptionExamplesClassificationPick one of N labelsPredict if loan is going to be defaulted or notRegressionPredict numerical valuesPredict property priceClusteringGroup similar examplesMost relevant documentsAssociation rule learningInfer likely association patterns in dataIf you buy butter you are likely to buy bread (unsupervisedStructured OutputCreate complex outputNatural language parse trees, images recognition bounding boxesRankingIdentify position on a scale or statusSearch result rankingSourceA. Regression (Prediction):  Regression algorithms are used for predicting numeric values. For example, predicting property price, vehicle mileage, stock prices and so on.   SourceB. Linear Regression – predicting a response variable, which is numeric in nature, using one or more features or variables. Linear regression model is mathematically represented as:  SourceVarious regression algorithms include: Linear Regression Polynomial Regression  Exponential Regression Decision Tree Random Forest Neural Network As a note to new learners, it is suggested to understand the concepts of – Regression assumptions, Ordinary Least Square Method, Dummy Variables (n-1 dummy encoding, one hot encoding), and performance evaluation metrics (RMSE, MSE, MAD). Classification - We use classification algorithms for predicting a set of items’ classes or a categorical feature. For example, predicting loan default (yes/no) or predicting cancer (yes/no) and so on. Various classification algorithms include: Binomial Logistic Regression Fractional Binomial Regression Quasibinomial Logistic regression Decision Tree Random Forest Neural Networks K-Nearest Neighbor Support Vector Machines Some of the classification algorithms are explained here: K-Nearest Neighbors – simple yet often used classification algorithm. It is a non-parametric algorithm (does not make any assumption on the underlying data distribution) It chooses to memorize the learning instances The output is a class membership  There are three key elements in this approach – a set of labelled objects, eg, a set of stored records, a distance between objects, and the value of k, the number of nearest neighbours  Distance measures that the K-NN algorithm uses - Euclidean distance (square root of the sum of the squared distance between a new point and the existing point across all the input attributes.  Other distances include – Hamming distance, Manhattan distance, Minkowski distance  SourceExample of K-NN classification. The test sample (green dot) should be classified either to blue squares or to red triangles. If k = 3 (solid line circle) it is assigned to the red triangles because there are 2 triangles and only 1 square inside the inner circle. In other words the number of triangles is more than the number of squares If k = 5 (dashed line circle) it is assigned to the blue squares (3 squares vs. 2 triangles inside the outer circle). It is to be noted that to avoid equal voting, the value of k should be odd and not even.  Logistic Regression – A supervised algorithm that is used for binary classification. The basis for logistic regression is the logit feature aka sigmoid characteristic which takes any real value and maps it between zero and 1. In other words, Logistic Regression returns a probability value for the class label.   SourceIf the output of the sigmoid function is more than 0.5, we can classify the outcome as 1 or YES, and if it is less than 0.5, we can classify it as 0 or NO For instance, let us take cancer prediction. If the output of the Logistic Regression is 0.75, we can say in terms of probability that, “There is a 75 percent chance that the patient will suffer from cancer.” Decision Tree – Is a type of supervised learning algorithm which is most commonly used in the case of a classification problem. Decision Tree algorithms can also be used for regression problems i.e. to predict a numerical response variable. In other words, Decision Tree works for both categorical and continuous input and output variables. Each branch node of the decision tree represents a choice between some alternatives and each leaf node represents a decision. SourceAs an early learner, it is suggested to understand the concept of ID3 algorithm, Gini Index, Entropy, Information Gain, Standard Deviation and Standard Deviation Reduction. Random Forest – is a collection of multiple decision trees. It is a supervised learning algorithm, that can be used for both classification & regression problems. While algorithms like Decision Tree can cause a problem of overfitting wherein a model performs well in training data but does not perform well in testing or unseen data, algorithms like Random Forest can help avoid overfitting. It achieves uncorrelated decision trees throughout the concept of bootstrapping (i.e. sampling with replacement) and features randomness.  SourceAs a new learner it is important to understand the concept of bootstrapping.  Support Vector Machine – a supervised learning algorithm, used for classification problems. Another flavour of Support Vector Machines (SVM) is Support Vector Regressor (SVR) which can be used for regression problems. In this, we plot each data item as a point in n-dimensional space n here represents the number of features SourceThe value of each feature is the value of a particular coordinate.  Classification is performed by finding hyperplanes that differentiate the two classes.  It is important to understand the concept of margin, support vectors, hyperplanes and tuning hyper-parameters (kernel, regularization, gamma, margin). Also get to know various types of kernels like linear kernel, radial basis function kernel and polynomial kernel Naive Bayes – a supervised learning classifier which assumes features are independent and there is no correlation between them. The idea behind Naïve Bayes algorithm is the Bayes theorem.  SourceC. Clustering Clustering algorithms are unsupervised algorithms that are used for dividing data points into groups such that the data points in each group are similar to each other and very different from other groups.  Some of the clustering algorithms include: K-means – An unsupervised learning algorithm in which the items are grouped into k-cluster The elements of the cluster are similar or homogenous. Euclidean distance is used to calculate the distance between two data points. Data points have a centroid; this centroid represents the cluster. The objective is to minimize the intra-cluster variations or the squared error function.SourceOther types of clustering algorithms: DBSCAN Mean Shift Hierarchical d) Association Association algorithms, which form part of unsupervised learning algorithms, are for associating co-occurring items or events. Association algorithms are rule-based methods for finding out interesting relationships in large sets of data. For example, find out a relationship between products that are being bought together – say, people who buy butter also buy bread. Some of the association algorithms are: Apriori Rules - Most popular algorithm for mining strong associations between variables. To understand how this algorithm works, concepts like Support, Confidence & Lift to be studied. ECLAT - Equivalence Class Clustering and bottom-up Lattice Traversal. This is one of the popular algorithms that is used for association problems. This algorithm is an enhanced version of the Apriori algorithm and is more efficient. FP Growth - Frequent Pattern Growth Algorithm - Another very efficient & scalable algorithm for mining associations between variables e) Anomaly Detection We recommend the use of anomaly detection for discovering abnormal activities and unusual cases like fraud detection. An algorithm that can be used for anomaly detection: Isolation Forest - This is an unsupervised algorithm that can help isolate anomalies from huge volume of data thereby enabling anomaly detection f) Sequence Pattern Mining We use sequential pattern mining for predicting the next data events between data examples in a sequence. Predicting the next dose of medicine for a patient g) Dimensionality ReductionDimensionality reduction is used for reducing the dimension of the original data. The idea is to reduce the set of random features by obtaining a set of principal components or features. The key thing to understand in this is that the components retain or represent some meaningful properties of the original data. It can be divided into feature extraction and selection. Algorithms that can be used for dimensionality reduction are: SourcePrincipal Component Analysis - This is a dimensionality reduction algorithm that is used to reduce the number of dimensions or variables in large datasets that have a very high number of variables. However it is to be noted that though PCA transforms a very large set of features or variables into smaller sets, it helps retain most of the information of the dataset. While the reduction of dimensions comes at a cost of model accuracy, the idea is to bring in simplicity in the model by reducing the number of variables or dimensions.  h) Recommendation Systems - Recommender Systems are used to build recommendation engines. Recommender algorithms are used in various business areas that include online stores to recommend the right product to its buyers like Amazon , content recommendation for online video & music sites like Netflix, Amazon Prime Music and various social media platforms like FaceBook, Twitter and so on.   SourceRecommender Engines can be broadly categorized into the following types: Content-based methods — recommends items to a user based on their profile history. It revolves around customer’s taste and preference.  Collaborating filtering method — it can be further subdivided into two categories Model-based — a stipulation wherein user and item interact. Both user and item interaction are learned from interactions matrix. Memory-based — Unlike model-based it relies on the similarity between the users and the items. Hybrid methods — Mix content which is based on collaborative filtering approaches. Examples: Movie recommendation system Food recommendation system E-commerce recommendation system 5. Choose the Algorithm — Several machine learning models can be used with the given context. These models are chosen depending on the data (image, numerical values, texts, sounds) and the data distribution 6. Train the model — Training the model is a process in which the machine learns from the historical data and provides a mathematical model that can be used for prediction. Different algorithms use different computation methods to compute the weights for each of the variables. Some algorithms like Neural Network initialize the weight of the variables at random. These weights are the values which affect the relationship between the actual and the predicted values.  7. Evaluation metrics to evaluate the model— Evaluation process comprises understanding the output model and evaluating the model accuracy for the result. There are various metrics to evaluate model performance. Regression problems have various metrics like MSE, RMSE, MAD, MAPE as key evaluation metrics while classification problems have metrics like Confusion Matrix, Accuracy, Sensitivity (True Positive Rate), Specificity (True Negative Rate), AUC (Area under ROC Curve), Kappa Value and so on. It is only after the evaluation, the model can be improved or fine-tuned to get more accurate predictions. It is important to know a few more concepts like:  True Positive  True Negative  False Positive  False Negative  Confusion Matrix  Recall (R) F1 Score ROC AUC Log loss When we talk about regression the most commonly used regression metrics are: Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE) Root Mean Squared Logarithmic Error (RMSLE) Mean Percentage Error (MPE) Mean Absolute Percentage Error (MAPE) We must know when to use which metric. It depends on the kind of data and the target variable you have. 8. Tweaking the model or the hyperparameter tuning - With great models, comes the great problem of optimizing hyperparameters to build an improved and accurate ML model. Tuning certain parameters (which are called hyperparameters) is important to ensure improved performance. The hyperparameters vary from algorithm to algorithm and it is important to learn the hyperparameters for each algorithm.  9. Making predictions - The final nail to the coffin. With all these aforementioned steps followed one can tackle real-life problems with advanced Machine Learning models.  Steps to remember while building the ML model: Data assembling or data collection - generally represents the data in the form of the dataset.  Data preparation - understanding the problem statement. This includes data wrangling for building or training models, data cleaning, removing duplicates, checking for missing values, data visualization for understanding the relationship between variables, checking for (imbalanced) bias data, and other exploratory data analysis. It also includes splitting the data into train and test. Choosing the model - the ML model which answers the problem statement. Different algorithms serve different purposes. Training the model - the idea to train the model is to ensure that the prediction is accurate more often. Model evaluation - evaluation metric to measure the performance of the model. How does the model perform against the previously unseen data? The train/test splitting ratio - (70:30) or (80:20), depending on the dataset. There is no exact rule to split the data by (80:20) or (70:30); it depends on the data and the target variable. Some of the data scientists use a range of 60% to 80% for training and the rest for testing the model. Parameter tuning - to ensure improved performance by controlling the model’s learning process. The hyperparameters have to be tuned so that the model can optimally solve the machine learning problem. For parameter tuning, we either specify a grid of parameters known as the grid search or we randomly select a combination of parameters known as the random search.GridSearchCV - It is the process to search the best combination of parameters over the grid. For instance, n_estimator could possibly be 100,250,350,500; max_depth can be 2,5,11,15 and the criterion could be gini or entropy. Though these don’t look like a lot of parameters, just imagine the scenario if the dataset is too large. The grid search has to run on a loop and calculate the score on the validation set. RandomSearchCV - We randomly select a combination of parameters and then calculate the cross-validation score. It computes faster than GridSearch. Note: Cross-validation is the first and most essential step when it comes to building ML models. If the cross-validation score is good, we can say that the validation data is a representation of training or the real-world data. Finally, making predictions - using the test data, of how the model will perform in real-world cases. ConclusionPython has an extensive catalogue of modules and frameworks. It is fast, less complex and thus it saves development time and cost. It makes the program completely readable particularly for novice users. This particular feature makes Python an ideal recipe for Machine Learning.  Both Machine Learning and Deep Learning require work on complex algorithms and several workflows. When using Python, the developer can worry less about the coding, and can focus more on finding the solution. It is open-source and has an abundance of available resources and step-by-step documentation. It also has an active community of developers who are open to knowledge sharing and networking. The benefits and the ease of coding makes Python the go to choice for developers. We saw how Python has an edge over other programming tools, and why knowledge of Python is essential for ML right now.  Summing up we saw the benefits of Python, the way ahead for beginners and finally the steps required in a machine learning project. This article can be considered as a roadmap to your mastery over Machine Learning. 

Essential Steps to Mastering Machine Learning with Python

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Essential Steps to Mastering Machine Learning with Python

One of the world’s most popular programming languages today, Python is a great tool for Machine Learning (ML) and Artificial Intelligence (AI). It is an open-source, reusable, general-purpose, object-oriented, and interpreted programming tool. Python’s key design ideology is code readability, ease of use and high productivity. The latest trend shows that the interest in Python has grown significantly over the past five years. Python is the top choice for ML/AI enthusiasts when compared to other programming languages.   

Essential Steps to Mastering Machine Learning with Python

Image source: Google Trends - comparing Python with other tools in the market

What makes Python a perfect recipe for Machine Learning? 

Python can be used to write Machine Learning algorithms and it computes pretty accurately. Python’s concise and easy readability allows the writing of reliable code very quickly. Another reason for its popularity is the availability of various versatile, ready-to-use libraries.  

It has an excellent library ecosystem and a great tool for developing prototypes. Unlike R, Python is a general-purpose programming language which can be used to build web applications and enterprise applications.  

The community of Python has developed libraries that adhere to a particular area of data science application. For instance, there are libraries available for handling arrays, performing numerical computation with matrices, statistical computing, machine learning, data visualization and many more. These libraries are highly efficient and make the coding much easier with fewer lines of codes. 

Let us have a brief look at some of the important Python libraries that are used for developing machine learning models. 

  • NumPy: One of the fundamental packages for numerical and scientific computing. It is a mathematical library to work with n-dimensional arrays in Python. 
  • Pandas: Provides highly efficient, easy-to-use DataFrame for DataFrame manipulations and Exploratory Data Analysis (EDA). 
  • SciPySciPy is a functional library for scientific and high-performance computations. It contains modules for optimization and for several statistical distributions and tests. 
  • Matplotlib: It is a complete plotting package that provides 2D plotting as well as 3D plotting. It can plot static and interactive plots. 
  • Seaborn: Seaborn library is based on Matplotlib. It is used to plot more elegant statistical visualization.  
  • StatsModels: The StatsModels library provides functionalities for estimation of various statistical models and conducting different statistical tests. 
  • Scikit-learnScikit-Learn is built on NumPy, SciPy and Matplotlib. Free to use, overpowered and provides various range of supervised and unsupervised machine learning algorithms. 

One should also take into account the importance of IDEs specially designed for Python for Machine Learning. 

The Jupyter Notebook  -  an open-source web-based application that enables ML enthusiasts to create, share, quote, visualize, and live-code their projects.  

There are various other IDEs that can be used like PyCharm, Spyder, Vim, Visual Studio Code. For beginners, there is a nice simple online compiler available – Programiz. 

Roadmap to master Machine Learning Using Python 

  1. Learn Python: Learn Python from basic to advanced. Practice those features that are important for data analysis, statistical analysis and Machine Learning. Start from declaring variables, conditional statements, control flow statements, functions, collection objects, modules and packages. Deep dive into various libraries that are used for statistical analysis and building machine learning models. 
  2. Descriptive Analytics : Learn the concept of descriptive analytics, understand the data, learn to load structured data and perform Exploratory Data Analysis (EDA). Practice data filtering, ordering, grouping, multiple joining of datasets. Handle missing values, prepare visualization plots in 2D or 3D format (from libraries like seaborn, matplotlib) to find hidden information and insights. 
  3. Take a break from Python and Learn Stats - Learn the concept of the random variable and its important role in the field of analytics. Learn to draw insights from the measures of dispersion (mean, median, mode, quartiles and other statistical measures like confidence interval and distribution functions. The next step is to understand probability & various probability distributions and their crucial role in analytics. Understand the concept of various hypothesis tests like t-tests, z-test, ANOVA (Analysis of Variance), ANCOVA (Analysis of Covariance), MANOVA (Multivariate Analysis of Variance), MANCOVA (Multivariate Analysis of Covariance) and chi-square test. 
  4.  Understand Major Machine Learning Algorithms

Essential Steps to Mastering Machine Learning with Python

Image source

Different algorithms have different tasks. It is advisable to understand the context and select the right algorithm for the right task. 

Types of ML ProblemDescriptionExamples
ClassificationPick one of N labelsPredict if loan is going to be defaulted or not
RegressionPredict numerical valuesPredict property price
ClusteringGroup similar examplesMost relevant documents
Association rule learningInfer likely association patterns in dataIf you buy butter you are likely to buy bread (unsupervised
Structured OutputCreate complex outputNatural language parse trees, images recognition bounding boxes
RankingIdentify position on a scale or statusSearch result ranking

Source

A. Regression (Prediction):  Regression algorithms are used for predicting numeric values. For example, predicting property price, vehicle mileage, stock prices and so on.   

 Regression

Source

B. Linear Regression – predicting a response variable, which is numeric in nature, using one or more features or variables. Linear regression model is mathematically represented as:  

Linear Regression

Source

Various regression algorithms include: 

  • Linear Regression 
  • Polynomial Regression  
  • Exponential Regression 
  • Decision Tree 
  • Random Forest 
  • Neural Network 

As a note to new learners, it is suggested to understand the concepts of – Regression assumptions, Ordinary Least Square Method, Dummy Variables (n-1 dummy encoding, one hot encoding), and performance evaluation metrics (RMSE, MSE, MAD). 

  • Classification We use classification algorithms for predicting a set of items’ classes or a categorical feature. For example, predicting loan default (yes/no) or predicting cancer (yes/no) and so on. 

Various classification algorithms include: 

  • Binomial Logistic Regression 
  • Fractional Binomial Regression 
  • Quasibinomial Logistic regression 
  • Decision Tree 
  • Random Forest 
  • Neural Networks 
  • K-Nearest Neighbor 
  • Support Vector Machines 

Some of the classification algorithms are explained here: 

  • K-Nearest Neighbors – simple yet often used classification algorithm. 
  • It is a non-parametric algorithm (does not make any assumption on the underlying data distribution) 
  • It chooses to memorize the learning instances 
  • The output is a class membership  
  • There are three key elements in this approach – a set of labelled objects, eg, a set of stored records, a distance between objects, and the value of k, the number of nearest neighbours  
  • Distance measures that the K-NN algorithm uses - Euclidean distance (square root of the sum of the squared distance between a new point and the existing point across all the input attributes.  

Other distances include – Hamming distance, Manhattan distance, Minkowski distance  
Hamming distance

Source

Example of K-NN classification. The test sample (green dot) should be classified either to blue squares or to red triangles. If k = 3 (solid line circle) it is assigned to the red triangles because there are 2 triangles and only 1 square inside the inner circle. In other words the number of triangles is more than the number of squares If k = 5 (dashed line circle) it is assigned to the blue squares (3 squares vs. 2 triangles inside the outer circle). It is to be noted that to avoid equal voting, the value of should be odd and not even.  

  • Logistic Regression – A supervised algorithm that is used for binary classification. The basis for logistic regression is the logit feature aka sigmoid characteristic which takes any real value and maps it between zero and 1. In other words, Logistic Regression returns a probability value for the class label.  

Logistic Regression Source

  1. If the output of the sigmoid function is more than 0.5, we can classify the outcome as 1 or YES, and if it is less than 0.5, we can classify it as 0 or NO 

  1. For instance, let us take cancer prediction. If the output of the Logistic Regression is 0.75, we can say in terms of probability that, “There is a 75 percent chance that the patient will suffer from cancer.” 

Decision Tree – Is a type of supervised learning algorithm which is most commonly used in the case of a classification problem. Decision Tree algorithms can also be used for regression problems i.e. to predict a numerical response variable. In other words, Decision Tree works for both categorical and continuous input and output variables. 

  • Each branch node of the decision tree represents a choice between some alternatives and each leaf node represents a decision. 

Decision Tree

Source

As an early learner, it is suggested to understand the concept of ID3 algorithm, Gini Index, Entropy, Information Gain, Standard Deviation and Standard Deviation Reduction. 

  • Random Forest – is a collection of multiple decision trees. It is a supervised learning algorithm, that can be used for both classification & regression problems. While algorithms like Decision Tree can cause a problem of overfitting wherein a model performs well in training data but does not perform well in testing or unseen data, algorithms like Random Forest can help avoid overfitting. 
    • It achieves uncorrelated decision trees throughout the concept of bootstrapping (i.e. sampling with replacement) and features randomness.  

Random Forest

Source

As a new learner it is important to understand the concept of bootstrapping.  

  • Support Vector Machine – a supervised learning algorithm, used for classification problems. Another flavour of Support Vector Machines (SVM) is Support Vector Regressor (SVR) which can be used for regression problems. 
    • In this, we plot each data item as a point in n-dimensional space 
    • n here represents the number of features 

Support Vector Machine

Source

The value of each feature is the value of a particular coordinate.  

Classification is performed by finding hyperplanes that differentiate the two classes.  

It is important to understand the concept of margin, support vectors, hyperplanes and tuning hyper-parameters (kernel, regularization, gamma, margin). Also get to know various types of kernels like linear kernel, radial basis function kernel and polynomial kernel 

  • Naive Bayes – a supervised learning classifier which assumes features are independent and there is no correlation between them. The idea behind Naïve Bayes algorithm is the Bayes theorem. Naive Bayes

 Source

C. Clustering 

Clustering algorithms are unsupervised algorithms that are used for dividing data points into groups such that the data points in each group are similar to each other and very different from other groups.  

Some of the clustering algorithms include: 

  • K-means – An unsupervised learning algorithm in which the items are grouped into k-cluster 
    • The elements of the cluster are similar or homogenous. 
    • Euclidean distance is used to calculate the distance between two data points. 
    • Data points have a centroid; this centroid represents the cluster. 
    • The objective is to minimize the intra-cluster variations or the squared error function.

Clustering

Source

Different Clustering

Other types of clustering algorithms: 

  • DBSCAN 
  • Mean Shift 
  • Hierarchical 

d) Association 

Association algorithms, which form part of unsupervised learning algorithms, are for associating co-occurring items or events. Association algorithms are rule-based methods for finding out interesting relationships in large sets of data. For example, find out a relationship between products that are being bought together – say, people who buy butter also buy bread. 

Some of the association algorithms are: 

  • Apriori Rules - Most popular algorithm for mining strong associations between variables. To understand how this algorithm works, concepts like Support, Confidence & Lift to be studied. 
  • ECLAT - Equivalence Class Clustering and bottom-up Lattice Traversal. This is one of the popular algorithms that is used for association problems. This algorithm is an enhanced version of the Apriori algorithm and is more efficient. 
  • FP Growth - Frequent Pattern Growth Algorithm - Another very efficient & scalable algorithm for mining associations between variables 

e) Anomaly Detection 

We recommend the use of anomaly detection for discovering abnormal activities and unusual cases like fraud detection. 

An algorithm that can be used for anomaly detection: 

  • Isolation Forest - This is an unsupervised algorithm that can help isolate anomalies from huge volume of data thereby enabling anomaly detection 

f) Sequence Pattern Mining 

We use sequential pattern mining for predicting the next data events between data examples in a sequence. 

  • Predicting the next dose of medicine for a patient 

g) Dimensionality Reduction

Dimensionality reduction is used for reducing the dimension of the original data. The idea is to reduce the set of random features by obtaining a set of principal components or features. The key thing to understand in this is that the components retain or represent some meaningful properties of the original data. It can be divided into feature extraction and selection. 

Algorithms that can be used for dimensionality reduction are: 

Dimensionality Reduction

Source

Principal Component Analysis - This is a dimensionality reduction algorithm that is used to reduce the number of dimensions or variables in large datasets that have a very high number of variables. However it is to be noted that though PCA transforms a very large set of features or variables into smaller sets, it helps retain most of the information of the dataset. While the reduction of dimensions comes at a cost of model accuracy, the idea is to bring in simplicity in the model by reducing the number of variables or dimensions.  

h) Recommendation Systems - 

Recommender Systems are used to build recommendation engines. Recommender algorithms are used in various business areas that include online stores to recommend the right product to its buyers like Amazon , content recommendation for online video & music sites like Netflix, Amazon Prime Music and various social media platforms like FaceBook, Twitter and so on.   

Recommendation Systems

Source

Recommender Engines can be broadly categorized into the following types: 

  • Content-based methods — recommends items to a user based on their profile history. It revolves around customer’s taste and preference.  
  • Collaborating filtering method — it can be further subdivided into two categories 
    • Model-based — a stipulation wherein user and item interact. Both user and item interaction are learned from interactions matrix. 
    • Memory-based — Unlike model-based it relies on the similarity between the users and the items. 
  • Hybrid methods — Mix content which is based on collaborative filtering approaches. 

Examples: 

  1. Movie recommendation system 
  2. Food recommendation system 
  3. E-commerce recommendation system 

5. Choose the Algorithm  Several machine learning models can be used with the given context. These models are chosen depending on the data (image, numerical values, texts, sounds) and the data distribution 

6. Train the model — Training the model is a process in which the machine learns from the historical data and provides a mathematical model that can be used for prediction. Different algorithms use different computation methods to compute the weights for each of the variables. Some algorithms like Neural Network initialize the weight of the variables at random. These weights are the values which affect the relationship between the actual and the predicted values.  

7. Evaluation metrics to evaluate the model Evaluation process comprises understanding the output model and evaluating the model accuracy for the result. There are various metrics to evaluate model performance. Regression problems have various metrics like MSE, RMSE, MAD, MAPE as key evaluation metrics while classification problems have metrics like Confusion Matrix, Accuracy, Sensitivity (True Positive Rate), Specificity (True Negative Rate), AUC (Area under ROC Curve), Kappa Value and so on. 

It is only after the evaluation, the model can be improved or fine-tuned to get more accurate predictions. It is important to know a few more concepts like:  

  • True Positive  
  • True Negative  
  • False Positive  
  • False Negative  
  • Confusion Matrix  
  • Recall (R) 
  • F1 Score 
  • ROC 
  • AUC 
  • Log loss 

When we talk about regression the most commonly used regression metrics are: 

  • Mean Absolute Error (MAE) 
  • Mean Squared Error (MSE) 
  • Root Mean Squared Error (RMSE) 
  • Root Mean Squared Logarithmic Error (RMSLE) 
  • Mean Percentage Error (MPE) 
  • Mean Absolute Percentage Error (MAPE) 

We must know when to use which metric. It depends on the kind of data and the target variable you have. 

8. Tweaking the model or the hyperparameter tuning  - With great models, comes the great problem of optimizing hyperparameters to build an improved and accurate ML model. Tuning certain parameters (which are called hyperparameters) is important to ensure improved performance. The hyperparameters vary from algorithm to algorithm and it is important to learn the hyperparameters for each algorithm.  

9. Making predictions  - The final nail to the coffin. With all these aforementioned steps followed one can tackle real-life problems with advanced Machine Learning models.  

Steps to remember while building the ML model: 

  • Data assembling or data collection  - generally represents the data in the form of the dataset.  
  • Data preparation - understanding the problem statement. This includes data wrangling for building or training models, data cleaning, removing duplicates, checking for missing values, data visualization for understanding the relationship between variables, checking for (imbalanced) bias data, and other exploratory data analysis. It also includes splitting the data into train and test. 
  • Choosing the model  -  the ML model which answers the problem statement. Different algorithms serve different purposes. 
  • Training the model  -  the idea to train the model is to ensure that the prediction is accurate more often. 
  • Model evaluation -  evaluation metric to measure the performance of the model. How does the model perform against the previously unseen data? The train/test splitting ratio -  (70:30) or (80:20), depending on the dataset. There is no exact rule to split the data by (80:20) or (70:30); it depends on the data and the target variable. Some of the data scientists use a range of 60% to 80% for training and the rest for testing the model. 
  • Parameter tuning - to ensure improved performance by controlling the model’s learning process. The hyperparameters have to be tuned so that the model can optimally solve the machine learning problem. For parameter tuning, we either specify a grid of parameters known as the grid search or we randomly select a combination of parameters known as the random search.
    • GridSearchCV -  It is the process to search the best combination of parameters over the grid. For instance, n_estimator could possibly be 100,250,350,500; max_depth can be 2,5,11,15 and the criterion could be gini or entropy. Though these don’t look like a lot of parameters, just imagine the scenario if the dataset is too large. The grid search has to run on a loop and calculate the score on the validation set. 
    • RandomSearchCV - We randomly select a combination of parameters and then calculate the cross-validation score. It computes faster than GridSearch. 

Note: Cross-validation is the first and most essential step when it comes to building ML models. If the cross-validation score is good, we can say that the validation data is a representation of training or the real-world data. 

  • Finally, making predictions -  using the test data, of how the model will perform in real-world cases. 

Conclusion

Python has an extensive catalogue of modules and frameworks. It is fast, less complex and thus it saves development time and cost. It makes the program completely readable particularly for novice users. This particular feature makes Python an ideal recipe for Machine Learning.  

Both Machine Learning and Deep Learning require work on complex algorithms and several workflows. When using Python, the developer can worry less about the coding, and can focus more on finding the solution. It is open-source and has an abundance of available resources and step-by-step documentation. It also has an active community of developers who are open to knowledge sharing and networking. The benefits and the ease of coding makes Python the go to choice for developers. We saw how Python has an edge over other programming tools, and why knowledge of Python is essential for ML right now.  

Summing up we saw the benefits of Python, the way ahead for beginners and finally the steps required in a machine learning project. This article can be considered as a roadmap to your mastery over Machine Learning. 

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If you are even remotely interested in technology you would have heard of machine learning. In fact machine learning is now a buzzword and there are dozens of articles and research papers dedicated to it.  Machine learning is a technique which makes the machine learn from past experiences. Complex domain problems can be resolved quickly and efficiently using Machine Learning techniques.  We are living in an age where huge amounts of data are produced every second. This explosion of data has led to creation of machine learning models which can be used to analyse data and to benefit businesses.  This article tries to answer a few important concepts related to Machine Learning and informs you about the career path in this prestigious and important domain.What is Machine Learning?So, here’s your introduction to Machine Learning. 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Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses.  Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a  Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.
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Data Science has become one of the most popular interdisciplinary fields. It uses scientific approaches, methods, algorithms, and operations to obtain facts and insights from unstructured, semi-structured, and structured datasets. Organizations use these collected facts and insights for efficient production, business growth, and to predict user requirements. Probability distribution plays a significant role in performing data analysis equipping a dataset for training a model. In this article, you will learn about the types of Probability Distribution, random variables, types of discrete distributions, and continuous distribution.  What is Probability Distribution? A Probability Distribution is a statistical method that determines all the probable values and possibilities that a random variable can deliver from a particular range. 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Consequently, the type of variable also helps determine the type of probability distribution.Common Data Types Before jumping directly into explaining the different probability distributions, let us first understand the different types of probability distributions or the main categories of the probability distribution. Data analysts and data engineers have to deal with a broad spectrum of data, such as text, numerical, image, audio, voice, and many more. Each of these have a specific means to be represented and analyzed. Data in a probability distribution can either be discrete or continuous. Numerical data especially takes one of the two forms. Discrete data: They take specific values where the outcome of the data remains fixed. Like, for example, the consequence of rolling two dice or the number of overs in a T-20 match. In the first case, the result lies between 2 and 12. In the second case, the event will be less than 20. Different types of discrete distributions that use discrete data are: Binomial Distribution Hypergeometric Distribution Geometric Distribution Poisson Distribution Negative Binomial Distribution Multinomial Distribution  Continuous data: It can obtain any value irrespective of bound or limit. Example: weight, height, any trigonometric value, age, etc. Different types of continuous distributions that use continuous data are: Beta distribution Cauchy distribution Exponential distribution Gamma distribution Logistic distribution Weibull distribution Types of Probability Distribution explained Here are some of the popular types of Probability distributions used by data science professionals. (Try all the code using Jupyter Notebook) Normal Distribution: It is also known as Gaussian distribution. It is one of the simplest types of continuous distribution. This probability distribution is symmetrical around its mean value. It also shows that data at close proximity of the mean is frequently occurring, compared to data that is away from it. Here, mean = 0, variance = finite valueHere, you can see 0 at the center is the Normal Distribution for different mean and variance values. Here is a code example showing the use of Normal Distribution: from scipy.stats import norm  import matplotlib.pyplot as mpl  import numpy as np  def normalDist() -> None:      fig, ax = mpl.subplots(1, 1)      mean, var, skew, kurt = norm.stats(moments = 'mvsk')      x = np.linspace(norm.ppf(0.01),  norm.ppf(0.99), 100)      ax.plot(x, norm.pdf(x),          'r-', lw = 5, alpha = 0.6, label = 'norm pdf')      ax.plot(x, norm.cdf(x),          'b-', lw = 5, alpha = 0.6, label = 'norm cdf')      vals = norm.ppf([0.001, 0.5, 0.999])      np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))      r = norm.rvs(size = 1000)      ax.hist(r, normed = True, histtype = 'stepfilled', alpha = 0.2)      ax.legend(loc = 'best', frameon = False)      mpl.show()  normalDist() Output: Bernoulli Distribution: It is the simplest type of probability distribution. It is a particular case of Binomial distribution, where n=1. It means a binomial distribution takes 'n' number of trials, where n > 1 whereas, the Bernoulli distribution takes only a single trial.   Probability Mass Function of a Bernoulli’s Distribution is:  where p = probability of success and q = probability of failureHere is a code example showing the use of Bernoulli Distribution: from scipy.stats import bernoulli  import seaborn as sb    def bernoulliDist():      data_bern = bernoulli.rvs(size=1200, p = 0.7)      ax = sb.distplot(          data_bern,           kde = True,           color = 'g',           hist_kws = {'alpha' : 1},          kde_kws = {'color': 'y', 'lw': 3, 'label': 'KDE'})      ax.set(xlabel = 'Bernouli Values', ylabel = 'Frequency Distribution')  bernoulliDist() Output:Continuous Uniform Distribution: In this type of continuous distribution, all outcomes are equally possible; each variable gets the same probability of hit as a consequence. This symmetric probabilistic distribution has random variables at an equal interval, with the probability of 1/(b-a). Here is a code example showing the use of Uniform Distribution: from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def uniformDist():      sb.distplot(random.uniform(size = 1200), hist = True)      mpl.show()  uniformDist() Output: Log-Normal Distribution: A Log-Normal distribution is another type of continuous distribution of logarithmic values that form a normal distribution. We can transform a log-normal distribution into a normal distribution. Here is a code example showing the use of Log-Normal Distribution import matplotlib.pyplot as mpl  def lognormalDist():      muu, sig = 3, 1      s = np.random.lognormal(muu, sig, 1000)      cnt, bins, ignored = mpl.hist(s, 80, normed = True, align ='mid', color = 'y')      x = np.linspace(min(bins), max(bins), 10000)      calc = (np.exp( -(np.log(x) - muu) **2 / (2 * sig**2))             / (x * sig * np.sqrt(2 * np.pi)))      mpl.plot(x, calc, linewidth = 2.5, color = 'g')      mpl.axis('tight')      mpl.show()  lognormalDist() Output: Pareto Distribution: It is one of the most critical types of continuous distribution. The Pareto Distribution is a skewed statistical distribution that uses power-law to describe quality control, scientific, social, geophysical, actuarial, and many other types of observable phenomena. The distribution shows slow or heavy-decaying tails in the plot, where much of the data reside at its extreme end. Here is a code example showing the use of Pareto Distribution – import numpy as np  from matplotlib import pyplot as plt  from scipy.stats import pareto  def paretoDist():      xm = 1.5        alp = [2, 4, 6]       x = np.linspace(0, 4, 800)      output = np.array([pareto.pdf(x, scale = xm, b = a) for a in alp])      plt.plot(x, output.T)      plt.show()  paretoDist() Output:Exponential Distribution: It is a type of continuous distribution that determines the time elapsed between events (in a Poisson process). Let’s suppose, that you have the Poisson distribution model that holds the number of events happening in a given period. We can model the time between each birth using an exponential distribution.Here is a code example showing the use of Pareto Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb  def expDist():      sb.distplot(random.exponential(size = 1200), hist = True)      mpl.show()   expDist()Output:Types of the Discrete probability distribution – There are various types of Discrete Probability Distribution a Data science aspirant should know about. Some of them are – Binomial Distribution: It is one of the popular discrete distributions that determine the probability of x success in the 'n' trial. We can use Binomial distribution in situations where we want to extract the probability of SUCCESS or FAILURE from an experiment or survey which went through multiple repetitions. A Binomial distribution holds a fixed number of trials. Also, a binomial event should be independent, and the probability of obtaining failure or success should remain the same. Here is a code example showing the use of Binomial Distribution – from numpy import random  import matplotlib.pyplot as mpl  import seaborn as sb    def binomialDist():      sb.distplot(random.normal(loc = 50, scale = 6, size = 1200), hist = False, label = 'normal')      sb.distplot(random.binomial(n = 100, p = 0.6, size = 1200), hist = False, label = 'binomial')      plt.show()    binomialDist() Output:Geometric Distribution: The geometric probability distribution is one of the crucial types of continuous distributions that determine the probability of any event having likelihood ‘p’ and will happen (occur) after 'n' number of Bernoulli trials. Here 'n' is a discrete random variable. In this distribution, the experiment goes on until we encounter either a success or a failure. The experiment does not depend on the number of trials. Here is a code example showing the use of Geometric Distribution – import matplotlib.pyplot as mpl  def probability_to_occur_at(attempt, probability):      return (1-p)**(attempt - 1) * probability  p = 0.3  attempt = 4  attempts_to_show = range(21)[1:]  print('Possibility that this event will occur on the 7th try: ', probability_to_occur_at(attempt, p))  mpl.xlabel('Number of Trials')  mpl.ylabel('Probability of the Event')  barlist = mpl.bar(attempts_to_show, height=[probability_to_occur_at(x, p) for x in attempts_to_show], tick_label=attempts_to_show)  barlist[attempt].set_color('g')  mpl.show() Output:Poisson Distribution: Poisson distribution is one of the popular types of discrete distribution that shows how many times an event has the possibility of occurrence in a specific set of time. We can obtain this by limiting the Bernoulli distribution from 0 to infinity. Data analysts often use the Poisson distributions to comprehend independent events occurring at a steady rate in a given time interval. Here is a code example showing the use of Poisson Distribution from scipy.stats import poisson  import seaborn as sb  import numpy as np  import matplotlib.pyplot as mpl  def poissonDist():       mpl.figure(figsize = (10, 10))      data_binom = poisson.rvs(mu = 3, size = 5000)      ax = sb.distplot(data_binom, kde=True, color = 'g',                       bins=np.arange(data_binom.min(), data_binom.max() + 1),                       kde_kws={'color': 'y', 'lw': 4, 'label': 'KDE'})      ax.set(xlabel = 'Poisson Distribution', ylabel='Data Frequency')      mpl.show()      poissonDist() Output:Multinomial Distribution: A multinomial distribution is another popular type of discrete probability distribution that calculates the outcome of an event having two or more variables. The term multi means more than one. The Binomial distribution is a particular type of multinomial distribution with two possible outcomes - true/false or heads/tails. Here is a code example showing the use of Multinomial Distribution – import numpy as np  import matplotlib.pyplot as mpl  np.random.seed(99)   n = 12                      pvalue = [0.3, 0.46, 0.22]     s = []  p = []     for size in np.logspace(2, 3):      outcomes = np.random.multinomial(n, pvalue, size=int(size))        prob = sum((outcomes[:,0] == 7) & (outcomes[:,1] == 2) & (outcomes[:,2] == 3))/len(outcomes)      p.append(prob)      s.append(int(size))  fig1 = mpl.figure()  mpl.plot(s, p, 'o-')  mpl.plot(s, [0.0248]*len(s), '--r')  mpl.grid()  mpl.xlim(xmin = 0)  mpl.xlabel('Number of Events')  mpl.ylabel('Function p(X = K)') Output:Negative Binomial Distribution: It is also a type of discrete probability distribution for random variables having negative binomial events. It is also known as the Pascal distribution, where the random variable tells us the number of repeated trials produced during a specific number of experiments.  Here is a code example showing the use of Negative Binomial Distribution – import matplotlib.pyplot as mpl   import numpy as np   from scipy.stats import nbinom    x = np.linspace(0, 6, 70)   gr, kr = 0.3, 0.7        g = nbinom.ppf(x, gr, kr)   s = nbinom.pmf(x, gr, kr)   mpl.plot(x, g, "*", x, s, "r--") Output: Apart from these mentioned distribution types, various other types of probability distributions exist that data science professionals can use to extract reliable datasets. In the next topic, we will understand some interconnections & relationships between various types of probability distributions. Relationship between various Probability distributions – It is surprising to see that different types of probability distributions are interconnected. In the chart shown below, the dashed line is for limited connections between two families of distribution, whereas the solid lines show the exact relationship between them in terms of transformation, variable, type, etc. Conclusion  Probability distributions are prevalent among data analysts and data science professionals because of their wide usage. Today, companies and enterprises hire data science professionals in many sectors, namely, computer science, health, insurance, engineering, and even social science, where probability distributions appear as fundamental tools for application. It is essential for Data analysts and data scientists. to know the core of statistics. Probability Distributions perform a requisite role in analyzing data and cooking a dataset to train the algorithms efficiently. If you want to learn more about data science - particularly probability distributions and their uses, check out KnowledgeHut's comprehensive Data science course. 
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Types of Probability Distributions Every Data Scie...

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