# Author Archives: Ajitesh Kumar

## NLTK – How to Read & Process Text File

In this post, you will learn about the how to read one or more text files using NLTK and process words contained in the text file. As data scientists starting to work on NLP, the Python code sample for reading multiple text files from local storage will be very helpful. Python Code Sample for Reading Text File using NLTK Here is the Python code sample for reading one or more text files. Pay attention to some of the following aspects: Class nltk.corpus.PlaintextCorpusReader reader is used for reading the text file. The constructor takes input parameter such as corpus root and the regular expression representing the files. List of files that are read could be found using method such as fileids List …

## Poisson Distribution Explained with Python Examples

In this post, you will learn about the concepts of Poisson probability distribution with Python examples. As a data scientist, you must get a good understanding of the concepts of probability distributions including normal, binomial, Poisson etc. Poisson distribution is the discrete probability distribution which represents the probability of occurrence of an event r number of times in a given interval of time or space if these events occur with a known constant mean rate and independent of each other. The following is the key criteria that the random variable follows the Poisson distribution. Individual events occur at random and independently in a given interval. This can be an interval of time or …

## Geometric Distribution Explained with Python Examples

In this post, you will learn about the concepts of Geometric probability distribution with the help of real-world examples and Python code examples. It is of utmost importance for data scientists to understand and get an intuition of different kinds of probability distribution including geometric distribution. You may want to check out some of my following posts on other probability distribution. Normal distribution explained with Python examples Binomial distribution explained with 10+ examples Hypergeometric distribution explained with 10+ examples In this post, the following topics have been covered: Geometric probability distribution concepts Geometric distribution python examples Geometric distribution real-world examples Geometric Probability Distribution Concepts Geometric probability distribution is a discrete …

## Top 10 Analytics Strategies for Great Data Products

In this post, you will learn about the top 10 data analytics strategies which will help you create successful data products. These strategies will be helpful in case you are setting up a data analytics practice or center of excellence (COE). As an AI / Machine Learning / Data Science stakeholders, it will be important to understand these strategies in order to deliver analytics solution which creates business value having positive business impact. Here are the top 10 data analytics strategies: Identify top 2-3 business problems Identify related business / engineering organizations Create measurement plan by identifying right KPIs Identify analytics deliverables such as analytics reports, predictions etc Gather data …

## Keras CNN Image Classification Example

In this post, you will learn about how to train a Keras Convolution Neural Network (CNN) for image classification. Before going ahead and looking at the Python / Keras code examples and related concepts, you may want to check my post on Convolution Neural Network – Simply Explained in order to get a good understanding of CNN concepts. Keras CNN Image Classification Code Example First and foremost, we will need to get the image data for training the model. In this post, Keras CNN used for image classification uses the Kaggle Fashion MNIST dataset. Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a …

## Data Quality Challenges for Machine Learning Models

In this post, you will learn about some of the key data quality challenges which need to be dealt with in a consistent and sustained manner to ensure high quality machine learning models. Note that high quality models can be termed as models which generalizes better (lower true error with predictions) with unseen data or data derived from larger population. As a data science architect or quality assurance (QA) professional dealing with quality of machine learning models, you must learn some of these challenges and plan appropriate development processes to deal with these challenges. Here are some of the key data quality challenges which need to be tackled appropriately in …

## Convolutional Neural Network (CNN) – Simply Explained

In this post, you will learn about the basic concepts of convolutional neural network (CNN) explained with examples. As data scientists / machine learning / deep learning enthusiasts, you must get a good understanding of convolution neural network as there are many applications of CNN. Before getting into the details on CNN, let’s understand the meaning of Convolution in convolutional neural network. What’s Convolution? What’s intuition behind Convolution? Convolution represents a mathematical operation on two functions. As there can be applied different mathematical operations such as addition or multiplication on two different functions, in the similar manner, convolution operation can be applied on two different functions. Mathematically, the convolution of two different …

## Data Quality Assessment Frameworks – Machine Learning

In this post, you will learn about data quality assessment frameworks / techniques in relation to machine learning and why one needs to assess data quality for building high-performance machine learning models? As a data science architect or development manager, you must get a sense of the importance of data quality in relation to building high-performance machine learning models. The idea is to understand what is the value of data set. The goal is to determine whether the value of data can be quantised. This is because it is important to understand whether the data contains rich information which could be valuable for building models and inform stakeholders on data …

## Keras Neural Network for Regression Problem

In this post, you will learn about how to train neural network for regression machine learning problems using Python Keras. Regression problems are those which are related to predicting numerical continuous value based on input parameters / features. You may want to check out some of the following posts in relation to how to use Keras to train neural network for classification problems: Keras – How to train neural network to solve multi-class classification Keras – How to use learning curve to select most optimal neural network configuration for training classification model In this post, the following topics are covered: Design Keras neural network architecture for regression Keras neural network …

## Keras – Categorical Cross Entropy Loss Function

In this post, you will learn about when to use categorical cross entropy loss function when training neural network using Python Keras. Generally speaking, the loss function is used to compute the quantity that the the model should seek to minimize during training. For regression models, the commonly used loss function used is mean squared error function while for classification models predicting the probability, the loss function most commonly used is cross entropy. In this post, you will learn about different types of cross entropy loss function which is used to train the Keras neural network model. Cross entropy loss function is an optimization function which is used in case …

## Python Keras – Learning Curve for Classification Model

In this post, you will learn about how to train an optimal neural network using Learning Curves and Python Keras. As a data scientist, it is good to understand the concepts of learning curve vis-a-vis neural network classification model to select the most optimal configuration of neural network for training high-performance neural network. In this post, the following topics have been covered: Concepts related to training a classification model using a neural network Python Keras code for creating the most optimal neural network using a learning curve Training a Classification Neural Network Model using Keras Here are some of the key aspects of training a neural network classification model using Keras: …

## 10 Key Challenges for AI / ML Projects Implementation

In this post, you will learn about some of the key challenges in relation to achieving successful AI / ML projects implementation in a consistent and sustained manner. As AI / ML project stakeholders including senior management stakeholders, data science architects, product managers etc, you must get a good understanding of what would it take to successfully execute AI / ML projects and create value for the customers and the business. Either you are building AI / ML products or enabling unique models for your clients in SaaS setup, you will come across most of these challenges. Here are some of the key challenges: Whether a machine learning solution is …

## Free MIT Course on Machine Learning for Healthcare

In this post, you will get a quick overview on free MIT course on machine learning for healthcare. This is going to be really helpful for machine learning / data science enthusiasts as building machine learning solutions to serve healthcare requirements comes with its own set of risks. It will be good to learn about different machine learning techniques, applications related disease progression modeling, cardiac imaging, pathology etc, risks and risk mitigation techniques. Here is the link to the course – Machine Learning for Healthcare Here are the links to some of the important course content: Video lectures Lecture notes (PDF) The entire course material can be downloaded from this page – …

## Keras Multi-class Classification using IRIS Dataset

In this post, you will learn about how to train a neural network for multi-class classification using Python Keras libraries and Sklearn IRIS dataset. As a deep learning enthusiasts, it will be good to learn about how to use Keras for training a multi-class classification neural network. The following topics are covered in this post: Keras neural network concepts for training multi-class classification model Python Keras code for fitting neural network using IRIS dataset Keras Neural Network Concepts for training Multi-class Classification Model Training a neural network for multi-class classification using Keras will require the following seven steps to be taken: Loading Sklearn IRIS dataset Prepare the dataset for training and testing …

## Neural Network Back-Propagation Python Examples

In this post, you will learn about the concepts of neural network back propagation algorithm along with Python examples. As a data scientist, it is very important to learn the concepts of back propagation algorithm if you want to get good at deep learning models. This is because back propagation algorithm is key to learning weights at different layers in the deep neural network. What’s Back Propagation Algorithm? The backpropagation algorithm represents the propagation of the gradients of outputs from each node (in each layer) on the final output, in the backward direction right up to the input layer nodes. All that is achieved using the backpropagation algorithm is to …

## Z-Score Explained with Ronaldo / Robert Example

In Champion’s league 2019-2020, here is the data related to their performance (ESPN.in). Player No. of Matches Played No. of Goals Scored Avg Goals / Matches Christiano Ronaldo 8 4 0.5 Robert Lewandowski 10 15 1.5 Table 1. Ronaldo / Robert performance in 2019-2020 Champion’s League . Well, the average goals / match indicates that Robert Lewandowski played much better than Christiano Ronaldo. However, can we conclude the same using statistical measures? How could we find out if they performed better than their own performance over last 7-8 years? This is where Z-Score comes into picture. In above evaluation, what is used to compare the performance is average goals / …