# Category Archives: Machine Learning

## How to Setup / Install MLFlow & Get Started

In this post, you will learn about how to setup / install MLFlow right from your Jupyter Notebook and get started tracking your machine learning projects. This would prove to be very helpful if you are running an enterprise-wide AI practice where you have a bunch of data scientists working on different ML projects. Mlflow will help you track the score of different experiments related to different ML projects. Install MLFlow using Jupyter Notebook In order to install / set up MLFlow and do a quick POC, you could get started right from within your Jupyter notebook. Here are the commands to get set up. Mlflow could be installed with …

## Top Tutorials – Neural Network Back Propagation Algorithm

Here are the top web pages /videos for learning back propagation algorithm used to compute the gradients in neural network. I will update this page with more tutorials as I do further deep dive on back propagation algorithm. For beginners or expert level data scientists / machine learning enthusiasts, these tutorials will prove to be very helpful. Before going ahead and understanding back propagation algorithm from different pages, lets quickly understand the key components of neural network algorithm: Feed forward algorithm: Feed forward algorithm represents the aspect of how input signals travel through different neurons present in different layers in form of weighted sums and activations, and, result in output / …

## Product Manager – Machine Learning Interview Questions

In this post, you will learn about some of the interview questions which can be asked in the AI / machine learning based product manager / business analyst job. Some of the questions listed in this post can also prove to be useful for the interview for the job position of director or vice president, product management. The interview questions can be categorized based on some of the following topics: Machine learning high level concepts Identifying a problem as machine learning problems Identifying business metrics vs value generation Feature engineering Working with data science team in model development lifecycle Monitoring model performance Model performance metrics presentation to key stakeholders Setting up …

## What’s Softmax Function & Why do we need it?

In this post, you will learn about the concepts of Softmax function with Python code example and why do we need Softmax function? As a data scientist / machine learning enthusiasts, it is very important to understand the concepts of Softmax function as it helps in understanding the algorithms such as neural network, multinomial logistic regression in better manner. Note that Softmax function is used in various multiclass classification machine learning algorithms such as multinomial logistic regression (thus, also called as softmax regression), neural networks etc. What’s Softmax Function? Simply speaking, Softmax function converts raw values (as outcome of functions) into probabilities. Here is how the softmax function looks like: …

## Cross Entropy Loss Explained with Python Examples

In this post, you will learn the concepts related to cross-entropy loss function along with Python and which machine learning algorithms use cross entropy loss function as an optimization function. Cross entropy loss is used as a loss function for models which predict the probability value as output (probability distribution as output). Logistic regression is one such algorithm whose output is probability distribution. In this post, the following topics are covered: What’s cross entropy loss? Cross entropy loss explained with Python examples What’s Cross Entropy Loss? Cross entropy loss function is an optimization function which is used for training machine learning classification models which classifies the data by predicting the …

## Python Sklearn – How to Generate Random Datasets

In this post, you will learn about some useful random datasets generators provided by Python Sklearn. There are many methods provided as part of Sklearn.datasets package. In this post, we will take the most common ones such as some of the following which could be used for creating data sets for doing proof-of-concepts solution for regression, classification and clustering machine learning algorithms. As data scientists, you must get familiar with these methods in order to quickly create the datasets for training models using different machine learning algorithms. Methods for generating datasets for Classification Methods for generating datasets for Regression Methods for Generating Datasets for Classification The following is the list of …

## Neural Networks and Mathematical Models Examples

In this post, you will learn about concepts of neural networks with the help of mathematical models examples. In simple words, you will learn about how to represent the neural networks using mathematical equations. As a data scientist / machine learning researcher, it would be good to get a sense of how the neural networks can be converted into a bunch of mathematical equations for calculating different values. Having a good understanding of representing the activation function output of different computation units / nodes / neuron in different layers would help in understanding back propagation algorithm in a better and easier manner. This will be dealt in one of the …

## Adaline Explained with Python Example

In this post, you will learn the concepts of Adaline (ADAptive LInear NEuron), a machine learning algorithm, along with Python example.As like Perceptron, it is important to understand the concepts of Adaline as it forms the foundation of learning neural networks. The concept of Perceptron and Adaline could found to be useful in understanding how gradient descent can be used to learn the weights which when combined with input signals is used to make predictions based on unit step function output. Here are the topics covered in this post in relation to Adaline algorithm and its Python implementation: What’s Adaline? Adaline Python implementation Model trained using Adaline implementation What’s Adaline? …

## Stochastic Gradient Descent Python Example

In this post, you will learn the concepts of Stochastic Gradient Descent using Python example. In order to demonstrate Stochastic gradient descent concepts, Perceptron machine learning algorithm is used. Recall that Perceptron is also called as single-layer neural network. Before getting into details, lets quickly understand the concepts of Perceptron and underlying learning algorithm such SGD is used. You may want to check out the concepts of gradient descent on this page – Gradient Descent explained with examples. The following topics are covered in this post: Stochastic Gradient Descent (SGD) for Learning Perceptron Model Perceptron algorithm can be used to train binary classifier that classifies the data as either 1 or 0. …

## Yann LeCun Deep Learning Free Online Course

This post is about listing down free online course materials for deep learning (PyTorch) by none other than Yann LeCun. Here are some useful links to the deep learning course: Deep Learning course – Homepage Deep learning lecture slides Github pages having Jupyter notebooks having PyTorch code Lectures slides, notebooks and related YouTube videos can be found on the deep learning (DL) course home page. It is a 14 week course and covers different topics such as following: Introduction to deep learning (What DL can do, what are good features / representations) Gradient descent and back propagation algorithm Artificial neural networks Convolution neural networks (Convnets) and related applications Regularization / Optimization …

## Python Implementations of Machine Learning Models

This post highlights some great pages where python implementations for different machine learning models can be found. If you are a data scientist who wants to get a fair idea of whats working underneath different machine learning algorithms, you may want to check out the Ml-from-scratch page. The top highlights of this repository are python implementations for the following: Supervised learning algorithms (linear regression, logistic regression, decision tree, random forest, XGBoost, Naive bayes, neural network etc) Unsupervised learning algorithms (K-means, GAN, Gaussian mixture models etc) Reinforcement learning algorithms (Deep Q Network) Dimensionality reduction techniques such as PCA Deep learning Examples that make use of above mentioned algorithms Here is an insight into …

## 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 …