Tag Archives: machine learning

Learning Curves Python Sklearn Example

Learning curve explained with python example

In this post, you will learn about how to use learning curves using Python code (Sklearn) example to determine machine learning model bias-variance. Knowing how to use learning curves will help you assess/diagnose whether the model is suffering from high bias (underfitting) or high variance (overfitting) and whether increasing training data samples could help solve the bias or variance problem. You may want to check some of the following posts in order to get a better understanding of bias-variance and underfitting-overfitting. Bias-variance concepts and interview questions Overfitting/Underfitting concepts and interview questions What are learning curves & why they are important? Learning curve in machine learning is used to assess how models will …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Machine Learning Sklearn Pipeline – Python Example

Machine-learning-pipeline-Sklearn

In this post, you will learning about concepts about machine learning (ML) pipeline and how to build ML pipeline using Python Sklearn Pipeline (sklearn.pipeline) package. Getting to know how to use Sklearn.pipeline effectively for training/testing machine learning models will help automate various different activities such as feature scaling, feature selection / extraction and training/testing the models. It is recommended for data scientists (Python) to get a good understanding of Sklearn.pipeline.  Introduction to Machine Learning Pipeline & Sklearn.pipeline Machine Learning (ML) pipeline, theoretically, represents different steps including data transformation and prediction through which data passes. The outcome of the pipeline is the trained model which can be used for making the predictions. …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Credit Risk Modeling & Machine Learning Use Cases

credit risk modeling and machine learning use cases

Credit risk modeling is a process of estimating the probability that a borrower will default on their loan. This is done by analyzing historical data about borrowers’ credit behavior. Credit risk models are used by banks and financial institutions to make better decisions about who to lend money to, how much to extend, and when to pull back. Banks and financial institutions are under constant pressure to improve their business outcomes. One way they are doing this is by using machine learning to better predict credit risk. By understanding the factors that contribute to a borrower’s likelihood of default, banks can make more informed decisions about who to lend money …

Continue reading

Posted in Data Science, Machine Learning. Tagged with , .

Performance metrics for Time-series Forecasting models

time-series forecasting model performance metrics

Time-series forecasting is a specific type of forecasting / predictive modeling that uses historical data to predict future trends in a particular time series. There are several different metrics that can be used to measure the accuracy and efficacy of a time-series forecasting model, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and others. By understanding these performance metrics, you can better assess the effectiveness of your time-series forecasting model and make necessary adjustments as needed. In this blog, you will learn about the different time-series forecasting model performance metrics and how to use them for model evaluation. Check out a related post – Different types of time-series …

Continue reading

Posted in Data Science, Machine Learning. Tagged with .

Sample Dataset for Regression & Classification: Python

Sample-data-set-plot-for-regression

A lot of beginners in the field of data science / machine learning are intimidated by the prospect of doing data analysis and building regression (linear) & classification models in Python. But with an ability to create sample dataset using Python packages, you can practice your skills and build your confidence over a period of time. The technique demonstrated in this blog post to create and visualize / plot the sample dataset includes datasets that can be used for regression models such as linear regression and classification models such as logistic regression, random forest, SVM etc. You can use this technique to explore different methods for solving the same problem. …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .

Ranking Algorithms & Types: Concepts & Examples

Ranking algorithms are used to rank items in a dataset according to some criterion. Ranking algorithms can be divided into two categories: deterministic and probabilistic. Ranking algorithms are used in search engines to rank webpages according to their relevance to a user’s search query. In this article, we will discuss the different types of ranking algorithms and give examples of each type. What is a Ranking Algorithm? A ranking algorithm is a procedure that ranks items in a dataset according to some criterion. Ranking algorithms are used in many different applications, such as web search, recommender systems, and machine learning. A ranking algorithm is a procedure used to rank items …

Continue reading

Posted in Data Science. Tagged with .

Different types of Time-series Forecasting Models

different types of time-series forecasting

Forecasting is the process of predicting future events based on past and present data. Time-series forecasting is a type of forecasting that predicts future events based on time-stamped data points. There are many different types of time-series forecasting models, each with its own strengths and weaknesses. In this blog post, we will discuss the most common time-series forecasting machine learning models such as the following, and provide examples of how they can be used to predict future events. Autoregressive (AR) model Moving average (MA) model Autoregressive moving average (ARMA) model Autoregressive integrated moving average (ARIMA) model Seasonal autoregressive integrated moving average (SARIMA) model Vector autoregressive (VAR) model Vector error correction …

Continue reading

Posted in Data Science, Machine Learning. Tagged with , .

Knowledge Graph Concepts & Machine Learning: Examples

knowledge graph example

Knowledge graphs and machine learning are two important tools for understanding and making decisions in business. Knowledge graphs can be used to understand and model complex concepts, while machine learning is a process by which computers learn from data, without being explicitly programmed. Together, these two tools can be used to make better decisions in business by understanding the relationships between data points. In this blog, you will learn about the basics of knowledge graphs and machine learning, and how they can be used to improve decision making in business. What is a Knowledge Graph & how they can are used? A knowledge graph is a collection of data that …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , .

AI / Machine learning (ML) Model Governance Framework

ML model governance framework

AI / Machine learning (ML) based solutions / applications have become increasingly important in business and industry. However, with the power to make decisions that can impact people’s lives comes a responsibility to use those tools ethically and responsibly. The machine learning model governance framework is designed to help businesses do just that. In this blog, you will learn about the AI / Machine Learning Model Governance framework, its benefits, and how you can implement it in your organization. What is AI / Machine learning (ML) model governance and why its important? Machine learning model governance is a set of process and related tools & frameworks that the businesses need …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , .

Targeted Advertising & Machine Learning: Examples

Targeted advertising is nothing new. Businesses have been using targeted ads for years in order to try and increase sales. However, with the advent of machine learning, businesses are now able to target their ads more effectively than ever before. The importance of using machine learning for targeted advertising cannot be overstated. By using machine learning, businesses can target their ads more accurately and thus see a higher return on investment. This is because machine learning can take into account a variety of factors that humans would not be able to consider, such as browsing history and purchase history. As a business, it is important to stay ahead of the …

Continue reading

Posted in Data Science, Machine Learning. Tagged with , .

Recommender Systems in Machine Learning: Examples

collaborative filtering - recommender system

Recommender systems are used in machine learning to predict the ratings or preferences of items for a given user. They are commonly used in e-commerce applications to suggest items that a user may be interested in. One common example of a recommender system is Netflix. Netflix uses a recommender system to suggest movies and TV shows that a user may want to watch. The algorithm looks at past ratings and preferences to make suggestions. In this blog post, you will learn about recommender systems and some of the different types of recommender systems with the help of examples. Recommender systems make use of machine learning to predict the ratings or …

Continue reading

Posted in Data Science, Machine Learning. Tagged with , .

Linear Discriminant Analysis (LDA) Concepts & Examples

Linear Discriminant Analysis LDA and Fisher Criterian

You may have heard of Linear Discriminant Analysis (LDA), but you’re not sure what it is or how it works. In the world of machine learning, Linear Discriminant Analysis (LDA) is a powerful algorithm that can be used to determine the best separation between two or more classes. With LDA, you can quickly and easily identify which class a particular data point belongs to. This makes LDA a key tool for solving classification problems. In this blog post, we will discuss the key concepts behind LDA and provide some examples of how it can be used in the real world! What is Linear Discriminant Analysis (LDA) and what are its …

Continue reading

Posted in Data Science, Machine Learning. Tagged with , .

100 Interview Questions for Deep Learning

Interview questions deep learning

If you’re looking for a job in deep learning, you’ll need to be prepared to answer some tough questions. In order to help you get started, we’ve put together a list of 100 interview questions for deep learning. While many of these questions are related to deep learning concepts, we have also listed several frameworks (Tensorflow, Pytorch, etc) related questions. By being prepared for these questions, you’ll be able to demonstrate your knowledge and expertise in this area, and increase your chances of landing the job! What is deep learning? How does machine learning differ from deep learning? What are the differences between shallow and deep learning? How does deep …

Continue reading

Posted in Career Planning, Data, Data Science, Deep Learning, Interview questions, Machine Learning. Tagged with , , .

Building Data Analytics Organization: Operating Models

Data analytics organization

Most businesses these days are collecting and analyzing data to help them make better decisions. However, in order to do this effectively, they need to build a data analytics organization. This involves hiring the right people with the right skills, setting up the right infrastructure and creating the right processes. In this article, we’ll take a closer look at what it takes to set up a successful data analytics organization. We’ll start by discussing the importance of having the right team in place. Then we’ll look at some of the key infrastructure components that need to be put in place. Finally, we’ll discuss some of the key process considerations that …

Continue reading

Posted in Big Data, Data, Data analytics, data engineering, Data lake, Data Science. Tagged with , , .

Who is a Data Scientist? Test your Knowledge

Interview questions

Do you know what a data scientist is? You may think you do, but take this quiz to find out for sure! Data scientists are essential to modern business and it’s important to know who they are and what they do. This quiz is just for fun, but it’s also a great opportunity to learn more about one of the most in-demand professions today. So put your data scientist knowledge to the test and see how well you really know this profession! And, feel free to share your thoughts if you disagree with the answer of any of the questions. Here are a few related posts on this topic: What …

Continue reading

Posted in Career Planning, Data, Data analytics, Data Science, Interview questions, Machine Learning. Tagged with , .

PCA Explained Variance Concepts with Python Example

In this post, you will learn about the concepts of explained variance which is one of the key concepts related to principal component analysis (PCA). The explained variance concepts will be illustrated with Python code examples. Check out the concepts of Eigenvalues and Eigenvectors in this post – Why & when to use Eigenvalue and Eigenvectors. What is Explained Variance? Explained variance is a statistical measure of how much variation in a dataset can be attributed to each of the principal components (eigenvectors) generated by the principal component analysis (PCA) method. In very basic terms, it refers to the amount of variability in a data set that can be attributed to …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .