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 – Training, Validation & Test Data Set
In this post, you will learn about the concepts of training, validation, and test data sets used for training machine learning models. The post is most suitable for data science beginners or those who would like to get clarity and a good understanding of training, validation, and test data sets concepts. The following topics will be covered: Data split – training, validation, and test data set Different model performance based on different data splits Data Splits – Training, Validation & Test Data Sets You can split data into the following different sets and each data split configuration will have machine learning models having different performance: Training data set: When you …
Why use Random Seed in Machine Learning?
In this post, you will learn about why and when do we use random seed values while training machine learning models. This is a question most likely asked by beginners data scientist/machine learning enthusiasts. We use random seed value while creating training and test data set. The goal is to make sure we get the same training and validation data set while we use different hyperparameters or machine learning algorithms in order to assess the performance of different models. This is where the random seed value comes into the picture. Different Python libraries such as scikit-learn etc have different ways of assigning random seeds. While training machine learning models using Scikit-learn, …
Deep Learning – Top 5 Online Jupyter Notebooks Servers
In this post, you will get information regarding the online Jupyter notebooks platform (GPU-based) which you can use to get started with both, machine learning and deep learning. The list consists of both freely available and paid options of online Jupyter notebook available with GPUs. When starting with GPUs, it is recommended to use rented options available online rather than buying your own GPU servers. There are online GPU Linux servers available (free and paid options) that can be used to train deep learning & machine learning models. I will be writing about it in my next post. Here is the list of Jupyter notebook platforms that could be used …
Top Deep Learning Myths You should know
This post highlights the top deep learning myths you should know. This is important to understand in order to leverage deep learning to solve complex AI problems. Many times, beginner to intermediate level machine learning enthusiasts don’t consider deep learning based on the myths discussed in this post. Without further ado, let’s look at the topmost and most common deep learning myths: Good understanding of complex mathematical concepts: Well, that is just a myth. At times, they say that one needs to have a higher degree in Mathematics & statistics. That is not true. With tools and programming languages along with libraries available today, basic mathematical concepts should be able …
First Principles Understanding based on Physics
In this post, you will understand the concepts of first principles and first principles thinking based on physics concepts. Let’s jump in right away. In the meanwhile, you could also access one of my other posts on the first principles: First-principles thinking explained with examples. It will help you get started on what are first principles and what is first principle thinking. One of the most fundamental Physics concept to understand the first principle is this: Every physical quantity can be represented as the derived quantity or fundamental quantity. The fundamental quantities, also termed basic quantity, are most basic or fundamental and unique and there are no overlaps between them. …
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 …
Difference between Data Science & Decision Science
In this post, you will learn about the difference between data science and decision science. Those venturing out to learn data science must understand whether they want to learn data science or decision science or both. The following are some of the key questions in relation to understanding the concepts related to data science and decision science. What is data science & decision science? When do we need data and decision science as part of the analytics strategy? Are there specialized courses for decision science? What are some good websites for decision sciences? What is Data Science & Decision Science? While Data science is used to extract insights from the data …
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 …
First Principles Thinking Explained with Examples
In this post, you will learn about the concepts of First Principles thinking with the help of examples. The following topics will be covered in this post: What are first principles? What is first principles thinking? Examples of first principles thinking The first principles thinking can be used to solve any type of problem including real-life problem, product related problems, science related problems etc. What are First Principles? As per Wikipedia, the first principle is a basic proposition or assumption that can not be derived from any other proposition or assumption. From Mathematics perspective, first principle can be thought of as Axioms. Axioms are propositions which are regarded as accepted …
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 …
Actionable Insights Examples – Turning Data into Action
In this post, you will learn about how to turn data into information and then to actionable insights with the help of few examples. It will be helpful for data analysts, data scientists, and business analysts to get a good understanding of what is actionable insight? You will understand aspects related to data-driven decision making. Before getting into the details, let’s understand what is the problem at hand? The school authority is trying to assess and improve the health of students. Here is the question it is dealing with: How could we improve the overall health of the students in the school? We will look into the approach of finding the …
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. …
