# Tag Archives: machine learning

## 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 …

## 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 …

## Adaptive Linear Neuron (Adaline) 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 Adaptive …

## 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 …

## 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 …

## Gradient Descent Explained Simply with Examples

In this post, you will learn about gradient descent algorithm with simple examples. It is attempted to make the explanation in layman terms. For a data scientist, it is of utmost importance to get a good grasp on the concepts of gradient descent algorithm as it is widely used for optimising the objective function / loss function related to various machine learning algorithms such as regression, neural network etc in order to learn weights / parameters. The related topics such as the following are covered in this post: Introduction to Gradient Descent algorithm Different types of gradient descent List of top 5 Youtube videos on Gradient descent algorithm Introduction to …

## Deep Learning Explained Simply in Layman Terms

In this post, you will get to learn deep learning through simple explanation (layman terms) and examples. Deep learning is part or subset of machine learning and not something which is different than machine learning. Many of us when starting to learn machine learning try and look for the answers to the question “what is the difference between machine learning & deep learning?”. Well, both machine learning and deep learning is about learning from past experience (data) and make predictions on future data. Deep learning can be termed as an approach to machine learning where learning from past data happens based on artificial neural network (a mathematical model mimicking human brain). …

## KMeans Silhouette Score Explained with Python Example

In this post, you will learn about concepts of KMeans Silhouette Score in relation to assessing the quality of K-Means clusters fit on the data. As a data scientist, it is of utmost important to understand the concepts of Silhouette score as it would help in evaluating the quality of clustering done using K-Means algorithm. In this post, the following topics will be covered: Introduction to Silhouette Score concepts Silhouette score explained using Python example You may want to check some of the following posts in relation to clustering: K-Means clustering explained with Python examples K-Means clustering elbow method and SSE Plot K-Means interview questions and answers Introduction to Silhouette Score Concepts …

## K-means Clustering Elbow Method & SSE Plot – Python

In this plot, you will quickly learn about how to find elbow point using SSE or Inertia plot with Python code and You may want to check out my blog on K-means clustering explained with Python example. The following topics get covered in this post: What is Elbow Method? How to create SSE / Inertia plot? How to find Elbow point using SSE Plot What is Elbow Method? Elbow method is one of the most popular method used to select the optimal number of clusters by fitting the model with a range of values for K in K-means algorithm. Elbow method requires drawing a line plot between SSE (Sum of Squared errors) …

## K-Means Clustering Explained with Python Example

In this post, you will learn about K-Means clustering concepts with the help of fitting a K-Means model using Python Sklearn KMeans clustering implementation. Before getting into details, let’s briefly understand the concept of clustering. Clustering represents a set of unsupervised machine learning algorithms belonging to different categories such as prototype-based clustering, hierarchical clustering, density-based clustering etc. K-means is one of the most popular clustering algorithm belong to prototype-based clustering category. The idea is to create K clusters of data where data in each of the K clusters have greater similarity with other data in the same cluster. The different clustering algorithms sets out rules based on how the data …

## Adaboost Algorithm Explained with Python Example

In this post, you will learn about boosting technique and adaboost algorithm with the help of Python example. You will also learn about the concept of boosting in general. Boosting classifiers are a class of ensemble-based machine learning algorithms which helps in variance reduction. It is very important for you as data scientist to learn both bagging and boosting techniques for solving classification problems. Check my post on bagging – Bagging Classifier explained with Python example for learning more about bagging technique. The following represents some of the topics covered in this post: What is Boosting and Adaboost Algorithm? Adaboost algorithm Python example What is Boosting and Adaboost Algorithm? As …

## Hard vs Soft Voting Classifier Python Example

In this post, you will learn about one of the popular and powerful ensemble classifier called as Voting Classifier using Python Sklearn example. Voting classifier comes with multiple voting options such as hard and soft voting options. Hard vs Soft Voting classifier is illustrated with code examples. The following topic has been covered in this post: Voting classifier – Hard vs Soft voting options Voting classifier Python example Voting Classifier – Hard vs Soft Voting Options Voting Classifier is an estimator that combines models representing different classification algorithms associated with individual weights for confidence. The Voting classifier estimator built by combining different classification models turns out to be stronger meta-classifier that balances out the individual …

## Keras Hello World Example

In this post, you will learn about how to set up Keras and get started with Keras, one of the most popular deep learning frameworks in current times which is built on top of TensorFlow 2.0 and can scale to large clusters of GPUs. You will also learn about getting started with hello world program with Keras code example. Here are some of the topics which will be covered in this post: Set up Keras with Anaconda Keras Hello World Program Set up Keras with Anaconda In this section, you will learn about how to set up Keras with Anaconda. Here are the steps: Go to Environments page in Anaconda App. …