Category Archives: Machine Learning
Lasso Regression Explained with Python Example
In this post, you will learn concepts of Lasso regression along with Python Sklearn examples. Lasso regression algorithm introduces penalty against model complexity (large number of parameters) using regularization parameter. Other two similar form of regularized linear regression are Ridge regression and Elasticnet regression which will be discussed in future posts. In this post, the following topics are discussed: What’s Lasso regression? Lasso regression python example Lasso regression cross validation python example What’s Lasso Regression? LASSO stands for least absolute shrinkage and selection operator. Pay attention to words, “least absolute shrinkage” and “selection”. We will refer it shortly. Lasso regression is also called as L1-norm regularization. Lasso regression is an extension …
Top 10 Data Science Skills for Product Managers
In this post, you will learn about some of the top data science skills / concepts which may be required for product managers / business analyst to have, in order to create useful machine learning based solutions. Here are some of the topics / concepts which need to be understood well by product managers / business analysts in order to tackle day-to-day challenges while working with data science / machine learning teams. Knowing these concepts will help product managers / business analyst acquire enough skills in order to solve machine learning based problems. Understanding the difference between AI, machine learning, data science, deep learning Which problems are machine learning problems? …
8 Key AI Challenges for Telemedicine / Telehealth
In this post, you will learn about some of key challenges of implementing Telemedicine / Telehealth. In case you are working in the field of data science / machine learning, you may want to go through some of the challenges, primarily AI related, which is thrown in Telemedicine domain due to upsurge in need of reliable Telemedicine services. Here are the slides I recently presented in Digital Data Science Conclave hosted by KIIT University. The primary focus is to make sure appropriate controls are in place to make responsible use of AI (Responsible AI). Here are the top 8 challenges which need to be addressed to take full advantage of AI, RPA …
RANSAC Regression Explained with Python Examples
In this post, you will learn about the concepts of RANSAC regression algorithm along with Python Sklearn example for RANSAC regression implementation using RANSACRegressor. RANSAC regression algorithm is useful for handling the outliers dataset. Instead of taking care of outliers using statistical and other techniques, one can use RANSAC regression algorithm which takes care of the outlier data. In this post, the following topics are covered: Introduction to RANSAC regression RANSAC Regression Python code example Introduction to RANSAC Regression RANSAC (RANdom SAmple Consensus) algorithm takes linear regression algorithm to the next level by excluding the outliers in the training dataset. The presence of outliers in the training dataset does impact …
Mean Squared Error or R-Squared – Which one to use?
In this post, you will learn about the concepts of mean-squared error (MSE) and R-squared, difference between them and which one to use when working with regression models such as linear regression model. You also learn Python examples to understand the concepts in a better manner. In this post, the following topics are covered: Introduction to Mean Squared Error (MSE) and R-Squared Difference between MSE and R-Squared MSE or R-Squared – Which one to use? MSE and R-Squared Python code example Introduction to Mean Square Error (MSE) and R-Squared In this section, you will learn about the concepts of mean squared error and R-squared. These are used for evaluating the …
Linear Regression Explained with Python Examples
In this post, you will learn about concepts of linear regression along with Python Sklearn examples for training linear regression models. Linear regression belongs to class of parametric models and used to train supervised models. The following topics are covered in this post: Introduction to linear regression Linear regression concepts / terminologies Linear regression python code example Introduction to Linear Regression Linear regression is a machine learning algorithm used to predict the value of continuous response variable. The predictive analytics problems that are solved using linear regression models are called as supervised learning problems as it requires that the value of response / target variables must be present and used for training the models. …
Correlation Concepts, Matrix & Heatmap using Seaborn
In this post, you will learn about the concepts of Correlation and how to draw Correlation Heatmap using Python Seaborn library for different columns in Pandas dataframe. The following are some of the topics covered in this post: Introduction to Correlation What is correlation heatmap? Corrleation heatmap Pandas / Seaborn python example Introduction to Correlation Correlation is a term used to represent the statistical measure of linear relationship between two variables. It can also be defined as the measure of dependence between two different variables. If there are multiple variables and the goal is to find correlation between all of these variables and store them using appropriate data structure, the …
K-Nearest Neighbors Explained with Python Examples
In this post, you will learn about K-nearest neighbors algorithm with Python Sklearn examples. K-nearest neighbors algorithm is used for solving both classification and regression machine learning problems. The following topics will get covered in this post: Introduction to K-nearest neighbors What is the most appropriate value of K? K-NN Python example Introduction to K-nearest neighbors K-nearest neighbors is a supervised learning algorithm which can be used to solve both classification and regression problems. It belongs to the class of non-parametric models. The models don’t learn parameters from training data set to come up with a discriminative function in order to classify the test or unseen data set. Rather model memorizes the training data …
Gradient Descent Explained Simply with Examples
In this post, you will learn about gradient descent algorithm with simple examples. It is attempted to make the explanation in layman terms. For a data scientist, it is of utmost importance to get a good grasp on the concepts of gradient descent algorithm as it is widely used for optimising the objective function / loss function related to various machine learning algorithms such as regression, neural network etc in order to learn weights / parameters. The related topics such as the following are covered in this post: Introduction to Gradient Descent algorithm Different types of gradient descent List of top 5 Youtube videos on Gradient descent algorithm Introduction to …
Deep Learning Explained Simply in Layman Terms
In this post, you will get to learn deep learning through simple explanation (layman terms) and examples. Deep learning is part or subset of machine learning and not something which is different than machine learning. Many of us when starting to learn machine learning try and look for the answers to the question “what is the difference between machine learning & deep learning?”. Well, both machine learning and deep learning is about learning from past experience (data) and make predictions on future data. Deep learning can be termed as an approach to machine learning where learning from past data happens based on artificial neural network (a mathematical model mimicking human brain). …
Tensor Broadcasting Explained with Examples
In this post, you will learn about the concepts of Tensor Broadcasting with the help of Python Numpy examples. Recall that Tensor is defined as the container of data (primarily numerical) most fundamental data structure used in Keras and Tensorflow. You may want to check out a related article on Tensor – Tensor explained with Python Numpy examples. Broadcasting of tensor is borrowed from Numpy broadcasting. Broadcasting is technique used for performing arithmetic operations between Numpy arrays / Tensors having different shapes. In this technique, the smaller array is transformed appropriately according to larger array (broadcasted to large array) such that the arithmetic operations can be performed on these arrays. Take a look …
Elbow Method vs Silhouette Score – Which is Better?
In this post, you will learn about two different methods to use for finding optimal number of clusters in K-means clustering. These methods are commonly termed as Elbow method and Silhouette analysis. Selecting optimal number of clusters is key to applying clustering algorithm to the dataset. As a data scientist, knowing these two techniques to find out optimal number of clusters would prove to be very helpful while In this relation, you may want to check out detailed posts on the following: K-means clustering elbow method and SSE plot K-means Silhouette score explained with Python examples In this post, we will use YellowBricks machine learning visualization library for creating the plot related …
KMeans Silhouette Score Explained with Python Example
In this post, you will learn about concepts of KMeans Silhouette Score in relation to assessing the quality of K-Means clusters fit on the data. As a data scientist, it is of utmost important to understand the concepts of Silhouette score as it would help in evaluating the quality of clustering done using K-Means algorithm. In this post, the following topics will be covered: Introduction to Silhouette Score concepts Silhouette score explained using Python example You may want to check some of the following posts in relation to clustering: K-Means clustering explained with Python examples K-Means clustering elbow method and SSE Plot K-Means interview questions and answers Introduction to Silhouette Score Concepts …
K-means Clustering Elbow Method & SSE Plot – Python
In this plot, you will quickly learn about how to find elbow point using SSE or Inertia plot with Python code and You may want to check out my blog on K-means clustering explained with Python example. The following topics get covered in this post: What is Elbow Method? How to create SSE / Inertia plot? How to find Elbow point using SSE Plot What is Elbow Method? Elbow method is one of the most popular method used to select the optimal number of clusters by fitting the model with a range of values for K in K-means algorithm. Elbow method requires drawing a line plot between SSE (Sum of Squared errors) …
K-Means Clustering Explained with Python Example
In this post, you will learn about K-Means clustering concepts with the help of fitting a K-Means model using Python Sklearn KMeans clustering implementation. Before getting into details, let’s briefly understand the concept of clustering. Clustering represents a set of unsupervised machine learning algorithms belonging to different categories such as prototype-based clustering, hierarchical clustering, density-based clustering etc. K-means is one of the most popular clustering algorithm belong to prototype-based clustering category. The idea is to create K clusters of data where data in each of the K clusters have greater similarity with other data in the same cluster. The different clustering algorithms sets out rules based on how the data …
Adaboost Algorithm Explained with Python Example
In this post, you will learn about boosting technique and adaboost algorithm with the help of Python example. You will also learn about the concept of boosting in general. Boosting classifiers are a class of ensemble-based machine learning algorithms which helps in variance reduction. It is very important for you as data scientist to learn both bagging and boosting techniques for solving classification problems. Check my post on bagging – Bagging Classifier explained with Python example for learning more about bagging technique. The following represents some of the topics covered in this post: What is Boosting and Adaboost Algorithm? Adaboost algorithm Python example What is Boosting and Adaboost Algorithm? As …
