# Category Archives: Machine Learning

## Precision & Recall Explained using Covid-19 Example

In this post, you will learn about the concepts of precision, recall, and accuracy when dealing with the machine learning classification model. Given that this is Covid-19 age, the idea is to explain these concepts in terms of a machine learning classification model predicting whether the patient is Corona positive or not based on the symptoms and other details. The following model performance concepts will be described with the help of examples. What is the model precision? What is the model recall? What is the model accuracy? What is the model confusion matrix? Which metrics to use – Precision or Recall? Before getting into learning the concepts, let’s look at the data (hypothetical) derived out …

## Moving Average Method for Time-series forecasting

In this post, you will learn about the concepts of the moving average method in relation to time-series forecasting. You will get to learn Python examples in relation to training a moving average machine learning model. The following are some of the topics which will get covered in this post: What is the moving average method? Why use the moving average method? Python code example for the moving average methods What is Moving Average method? The moving average is a statistical method used for forecasting long-term trends. The technique represents taking an average of a set of numbers in a given range while moving the range. For example, let’s say …

## Spend Analytics using AI & Data Science

In this post, you will learn about the high-level concepts of spend analytics in relation to procurement and how data science / machine learning & AI can be used to extract actionable insights from spend analytics. This will be useful for data analytics or business analytics professionals looking to understand the concepts of spend analytics. The following topics will get covered in this post: What is spend analytics? Why spend analytics? Spend analytics – Descriptive & Predictive Some popular spend analytics products What is Spend Analytics? Simply speaking, spend analytics is about performing systematic computational analysis to extract actionable insights from spend data. As part of spend analytics, the following are …

## Autoregressive (AR) models with Python examples

In this post, you will learn about the concepts of autoregressive (AR) models with the help of Python code examples. If you are starting on time-series forecasting, this would be useful read. Note that time-series forecasting is one of the important areas of data science / machine learning. Here are some of the topics that will be covered in the post: Autoregressive (AR) models concepts with examples Alternative methods to AR models Python code example for AR models Learning References Autoregressive (AR) Models concepts with Examples Autoregressive (AR) modeling is one of the technique used for time-series analysis. For the beginners, time series analysis represents the class of problems where the dependent variable or response variable …

## Image Classification & Machine learning

In this post, you will learn about how could image classification problems be solved using machine learning techniques. The following are some of the topics which will be covered: How does the computer learn about an image? How could machine learning be used to classify the images? How does the computer learn about an image? Unlike the human beings, the image has to be converted into numbers for computer to learn about the image. So, the question is how can an image be converted into numbers? The most fundamental element or the smallest building block of an image is a pixel. An image can be represented as a set of …

## Free Datasets for Machine Learning & Deep Learning

Here is the list of free data sets for machine learning & deep learning publicly available: Machine learning problems datasets UC Irvine Machine Learning Repository: A repository of 560 datasets suitable for traditional machine learning algorithm problems such as classification and regression Public available dataset through public APIs: A list of 650+ datasets available via public API Penn machine learning dataset: The data sets cover a broad range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. The good part if that the datasets is available in tabular form that makes it very useful for training models with traditional …

## When to use Deep Learning vs Machine Learning Models?

In this post, you will learn about when to go for training deep learning models from the perspective of model performance and volume of data. As a machine learning engineer or data scientist, it always bothers as to can we use deep learning models in place of traditional machine learning models trained using algorithms such as logistic regression, SVM, tree-based algorithms, etc. The objective of this post is to provide you with perspectives on when to go for traditional machine learning models vs deep learning models. The two key criteria based on which one can decide whether to go for deep learning vs traditional machine learning models are the following: …

## Most Common Types of Machine Learning Problems

In this post, you will learn about the most common types of machine learning (ML) problems along with a few examples. Without further ado, let’s look at these problem types and understand the details. Regression Classification Clustering Time-series forecasting Anomaly detection Ranking Recommendation Data generation Optimization Problem types Details Algorithms Regression When the need is to predict numerical values, such kinds of problems are called regression problems. For example, house price prediction Linear regression, K-NN, random forest, neural networks Classification When there is a need to classify the data in different classes, it is called a classification problem. If there are two classes, it is called a binary classification problem. …

## Historical Dates & Timeline for Deep Learning

This post is a quick check on the timeline including historical dates in relation to the evolution of deep learning. Without further ado, let’s get to the important dates and what happened on those dates in relation to deep learning: Year Details/Paper Information Who’s who 1943 An artificial neuron was proposed as a computational model of the “nerve net” in the brain. Paper: “A logical calculus of the ideas immanent in nervous activity,” Bulletin of Mathematical Biophysics, volume 5, 1943 Warren McCulloch, Walter Pitts Late 1950s A neural network application by reducing noise in phone lines was developed Paper: Andrew Goldstein, “Bernard Widrow oral history,” IEEE Global History Network, 1997 Bernard …

## Machine Learning – Why use Confidence Intervals?

In this post, you will learn about the concepts of confidence intervals in relation to machine learning models and related concepts with the help of an example and Python code examples. When you get a hypothesis function by training a machine learning classification model, you evaluate the hypothesis/model by calculating the classification error. The classification error is calculated on the sample of the data used for training the model. However, does this classification error for the sample (sample error) also represent (same as) the classification error of the hypothesis/model for the entire population (true error)? How can the true error be represented as a function of the sample error? This is …

## Great Mind Maps for Learning Machine Learning

In this post, you will get to look at some of the great mind-maps for learning different machine learning topics. I have gathered these mind maps from different web pages on the Internet. The idea is to reinforce our understanding of different machine learning topics using pictures. You may have heard the proverb – A picture is worth a thousand words. Keeping this in mind, I thought to pull some of the great mind maps posted on different web pages. I would be updating this blog post from time-to-time. If you are a beginner data scientist or an experienced one, you may want to bookmark this page for refreshing your …

## Different Types of Distance Measures in Machine Learning

In this post, you will learn different types of distance measures used in different machine learning algorithms such as K-nearest neighbours, K-means etc. Distance measures are used to measure the similarity between two or more vectors in multi-dimensional space. The following represents different forms of distance metrics / measures: Geometric distances Computational distances Statistical distances Geometric Distance Measures Geometric distance metrics, primarily, tends to measure the similarity between two or more vectors solely based on the distance between two points in multi-dimensional space. The examples of such type of geometric distance measures are Minkowski distance, Euclidean distance and Manhattan distance. One other different form of geometric distance is cosine similarity which will discuss …

## Hold-out Method for Training Machine Learning Models

In this post, you will learn about the hold out method used during the process of training machine learning model. When evaluating machine learning (ML) models, the question that arises is whether the model is the best model available from the algorithm hypothesis space in terms of generalization error on the unseen / future data set. Whether the model is trained and tested using the most appropriate method. Out of available models, which model to select? These questions are taken care using what is called as hold out method. Instead of using entire dataset for training, different sets called as validation set and test set is separated or set aside …

## Machine Learning Terminologies for Beginners

When starting on the journey of learning machine learning and data science, we come across several different terminologies when going through different articles/posts, books & video lectures. Getting a good understanding of these terminologies and related concepts will help us understand these concepts in a nice manner. At a senior level, it gets tricky at times when the team of data scientists / ML engineers explain their projects and related outcomes. With this in context, this post lists down a set of commonly used machine learning terminologies that will help us get a good understanding of ML concepts and also engage with the DS / AI / ML team in …

## Machine Learning Free Course at Univ Wisconsin Madison

In this post, you will learn about the free course on machine learning (STAT 451) recently taught at University of Wisconsin-Madison by Dr. Sebastian Raschka. Dr. Sebastian Raschka in currently working as an assistant Professor of Statistics at the University of Wisconsin-Madison while focusing on deep learning and machine learning research. The course is titled as “Introduction to Machine Learning”. The recording of the course lectures can be found on the page – Introduction to machine learning. The course covers some of the following topics: What is machine learning? Nearest neighbour methods Computational foundation Python Programming (concepts) Machine learning in Scikit-learn Tree-based methods Decision trees Ensemble methods Model evaluation techniques Concepts of …

## Reinforcement Learning Real-world examples

In this post, you will learn about some real-world / real-life examples of Reinforcement learning, one of the different approaches to machine learning where other approaches are supervised and unsupervised learning. Before looking into the real-world examples of Reinforcement learning, let’s quickly understand what is reinforcement learning. Introduction to Reinforcement Learning (RL) Reinforcement learning is an approach to machine learning in which the agents are trained to make a sequence of decisions. The agent, also called as an AI agent gets trained in the following manner: The agent interacts with the environment and make decisions or choices. For training purpose, the agent is provided with the contextual information about the environment and …