Matplotlib Pyplot Plot Python Example
Matplotlib is a matlab-like plotting library for python. It can create both 2D and 3D plots, with the help of matplotlib pyplot. Matplotlib can be used in interactive environments such as IPython notebook, Matlab, octave, qt-console and wxpython terminal. Matplotlib has a modular architecture with each layer having its own dependencies which makes matplotlib very versatile and allows users to use only those modules they need for their applications. matplotlib provides many hooks that allow developers to customize matplotlib features as they need. Matplotlib architecture has a clear separation between user interface and drawing code which makes it easy to customize or create new interfaces for matplotlib. In this blog …
K-Fold Cross Validation – Python Example
In this post, you will learn about K-fold Cross Validation concepts with Python code example. K-fold cross validation is a data splitting technique that can be implemented with k > 1 folds. K-Fold Cross Validation is also known as k-cross, k-fold cross validation, k-fold CV and k-folds. The k-fold cross validation technique can be implemented easily using Python with scikit learn package which provides an easy way to calculate k-fold cross validation models. It is important to learn the concepts cross validation concepts in order to perform model tuning with an end goal to choose model which has the high generalization performance. As a data scientist / machine learning Engineer, you must …
Survival Analysis Modeling for Customer Churn
Customer churn is a prevalent problem for many businesses. It can happen in several different ways, such as when customers stop using the product, or when they leave because of an issue with customer service. This blog post will explore survival analysis modeling and what it can do to help you better understand customer churn problems. First, we will discuss survival analysis itself and why it is beneficial for analyzing customer behavior. Then we will show some examples on how survival analysis has been used to analyze customer churn problems. As data scientists, it will be good to familiarize ourselves with survival analysis, as it is a popular modeling technique …
First Principles Thinking Explained with Examples
First principles thinking is the first step in critical thinking, and it’s a way of approaching problems by first identifying what you know to be true. It’s important for people to learn first principles thinking because they give us a foundation on which we can build our understanding of any problem. The first principles thinking can be used to solve any type of problem including real-life problem, product related problems, science related problems etc. Thinking about first principals isn’t always easy, but once you’ve mastered the process of first thinking through a problem in this way rather than making assumptions or substituting your knowledge for an easier answer then you …
Elbow Method vs Silhouette Score – Which is Better?
In K-means clustering, elbow method and silhouette analysis or score techniques are used to find the number of clusters in a dataset. The elbow method is used to find the “elbow” point, where adding additional data samples does not change cluster membership much. Silhouette score determines whether there are large gaps between each sample and all other samples within the same cluster or across different clusters. In this post, you will learn about these two different methods to use for finding optimal number of clusters in K-means clustering. Selecting optimal number of clusters is key to applying clustering algorithm to the dataset. As a data scientist, knowing these two techniques to find …
Hold-out Method for Training Machine Learning Models
The hold-out method for training the machine learning models is a technique that involves splitting the data into different sets: one set for training, and other sets for validating and testing. The hold out method is used to check how well a machine learning model will perform on the new data. In this post, you will learn about the hold out method used during the process of training machine learning model. Do check out my post on what is machine learning? concepts & examples for detailed understanding on different aspects related to basics of machine learning. When evaluating machine learning (ML) models, the question that arises is whether the model …
Hello World – Altair Python Install in Jupyter Notebook
This blog post will walk you through the steps needed to install Altair graphical libraries in Jupyter Notebook. For data scientists, Altair visualization library can prove to very useful. In this blog, we’ll look at how to download and install Altair, as well as some examples of using Altair capabilities for data visualization. What is Altair? Altair is a free statistical visualization library that can be used with python (2 or 3). It provides high-quality interactive graphics via an integrated plotting function ́plot() that produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms. Altair is also easy to learn, with intuitive commands like ‘plot’, ‘hist’ …
Most In-Demand Skills for Data Scientists in 2022
The data science field is growing rapidly and data scientists are in high demand. If you want to enter this field, it’s important that you have the right skills. In this blog post, we’ll explore the most in-demand skills of data scientist employers are looking for the most and how to develop these skills so that you can find a job as a data scientist. Strong experience with statistical/ML methods: Strong familiarity with probability distributions (e.g., normal distribution), concepts of hypothesis testing, regression analysis is essential for becoming a great data scientist. Data scientists are required to model data, assess the suitability of data for analysis, develop mathematical / machine …
Different types of Machine Learning: Models / Algorithms
Machine learning is a type of machine intelligence that enables computers to learn and improve without being explicitly programmed. It’s based on the idea that we can build systems which allow our data to do the talking, by finding patterns in vast quantities of information. These machine learning algorithms require different types of machine-learning models trained using different algorithms, depending on what problem they are trying to solve or how accurate an answer needs to be. In this blog post, we will discuss the following four different types of machine learning models / algorithms: Supervised learning Unsupervised learning Semi-supervised learning Reinforcement learning What is supervised learning? Supervised learning is defined …
Deep Neural Network Examples from Real-life
The deep neural network (DNN) is an artificial neural network, which has a number of hidden layers and nodes. Deep NN is composed of many interconnected and non-linear processing units that work in parallel to process information more quickly than the traditional neural networks. Deep learning algorithms are used for classification, regression analysis, prediction and other types of tasks. In this blog post, we will present deep neural network examples from the real-world/real-life. Before jumping into examples, you may want to check out some of my following posts on deep neural network: Deep Learning Explained Simply in Layman Terms Neural network explained with perceptron example Perceptron explained with Python example …
Free AI / Machine Learning Courses at Alison.com
Are you interested in learning about AI / machine learning / data sicence and looking for free online courses? As per MANUELA M. VELOSO, Herbert A. Simon University Professor at CMU,Machine Learning (ML) is a fascinating field of Artificial Intelligence (AI) research and practice where we investigate how computer agents can improve their perception, cognition, and action with experience. Machine Learning is about machines improving from data, knowledge, experience, and interaction. Machine Learning utilizes a variety of techniques to intelligently handle large and complex amounts of information build upon foundations in many disciplines, including statistics, knowledge representation, planning and control, databases, causal inference, computer systems, machine vision, and natural language …
Difference between Supervised & Unsupervised Learning
Supervised and unsupervised learning are two different common types of machine learning tasks that are used to solve many different types of business problems. Supervised learning uses training data with labels to create supervised models, which can be used to predict outcomes for future datasets. Unsupervised learning is a type of machine learning task where the training data is not labeled or categorized in any way. For beginner data scientists, it is very important to get a good understanding of the difference between supervised and unsupervised learning. In this post, we will discuss how supervised and unsupervised algorithms work and what is difference between them. You may want to check …
12 Weeks Free course on AI: Knowledge Representation & Reasoning (IIT Madras)
Are you interested in learning about exploring a variety of representation formalisms and the associated algorithms for reasoning in Artificial intelligence? IIT Madras is going to offer a free online course on AI: knowledge representation and reasoning. This course will help you understand the basics of knowledge representation and reasoning. You’ll learn how to solve problems using logic, how to build intelligent systems that can interpret natural language, reason using formal methods and more. The course is taught by Professor Deepak Khemani, who has over 20 years of experience teaching at IIT Madras. Prof. Khemani is a Professor at Department of Computer Science and Engineering. He’s also written several books …
Data Governance Framework Template / Example
Data governance is a framework that provides data management governance. It’s the process of structuring data so it can be governed, managed and used more effectively. Data governance framework forms the key aspect of data analytics strategy. This blog post will discuss key functions of a standard data governance framework and can be taken as a template or example to help you get started with setting up your data governance program. What is Data Governance Framework? The data governance framework is intended to put some structure around how data can be managed and used in an organization based on well-defined rules and processes around a variety of data related operations and decisions. Data …
Business Analytics Team Structure: Roles/ Responsibilities
Business analytics is a business function that has been around for years, but it’s only recently gained traction as one of the most important business functions. Organizations are now realizing how business analytics can help them increase revenue and improve business operations. But before you bring on a business analytics team, you need to determine if your company needs a full-time or part-time team member or both. It might seem logical to hire full-time analysts just because they’re in demand, but this isn’t always necessary. If your business operates without any external data sets and doesn’t have complex reporting needs then it may be more cost-effective to use freelancers rather …
Google Cloud Automl: Business Application Examples
Google cloud platform (GCP) automl services are a set of google cloud platform products with a focus on machine learning and automation. They help you to automate several tasks related to machine learning. In this blog post, we’ll talk about google cloud automl services and some common business problems that can be solved using these GCP automl services. What are some popular Google Cloud Automl services? Google cloud automl services include some of the following: Google Cloud Vision can be used to perform tasks related to image recognition like face detection, OCR (optical character recognition), landmark detection, etc. Google’s cloud vision can detect emotions, understand text, and more. The service …
