Author Archives: Ajitesh Kumar

Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin.

Data Quality Assessment Frameworks – Machine Learning

data quality assessment framework for 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 …

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Posted in Data Science, Machine Learning.

Keras Neural Network for Regression Problem

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 …

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Posted in Data Science, Deep Learning. Tagged with , , .

Keras – Categorical Cross Entropy Loss Function

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 …

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Posted in Data Science, Deep Learning. Tagged with , .

Python Keras – Learning Curve for Classification Model

Training & Validation Accuracy & Loss of Keras Neural Network 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: …

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Posted in Data Science, Deep Learning, Machine Learning. Tagged with , , .

10 Key Challenges for AI / ML Projects Implementation

Challenges related to Machine Learning Projects Implementations

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 …

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Posted in AI, Machine Learning. Tagged with , .

Free MIT Course on Machine Learning for Healthcare

machine learning and healthcare MIT free course

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 – …

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Posted in Machine Learning, Online Courses. Tagged with , .

Keras Multi-class Classification using IRIS Dataset

Python keras for multi-class classification model 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 …

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Posted in Data Science, Deep Learning, Machine Learning, Python. Tagged with , , , .

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 …

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Posted in Data Science, Deep Learning. Tagged with , , .

Z-Score Explained with Ronaldo / Robert Example

In Champion’s league 2019-2020, here is the data related to their performance ( 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 / …

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Posted in statistics. Tagged with , .

Data Storytelling Explained with Examples

MS Dhoni - Former Captain of Indian Cricket Team

In this post, you will learn about some of the key concepts in relation to data storytelling and why data scientists / data analyst should acquire this skill. Data storytelling is one of the key skills which data scientists would need to acquire in order to do a great job in representing the data with story. Most of the time, it has been seen that data scientists merely present multiple plots with the sole aim of showing the logic and reasoning. However, it is equally important to represent the data as story as it results in an emotional connect with stakeholders and help them make the decisions. Thus, data scientists …

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Posted in Data Science. Tagged with .

How to Setup / Install MLFlow & Get Started

Install MLFLow and 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 …

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Posted in Machine Learning. Tagged with .

Python – How to Add Trend Line to Line Chart / Graph

Chris Gayle - Rohit Sharma - Dhoni - Virat Kohli IPL Batting Average Score Trendline

In this plot, you will learn about how to add trend line to the line chart / line graph using Python Matplotlib.As a data scientist, it proves to be helpful to learn the concepts and related Python code which can be used to draw or add the trend line to the line charts as it helps understand the trend and make decisions. In this post, we will consider an example of IPL average batting scores of Virat Kohli, Chris Gayle, MS Dhoni and Rohit Sharma of last 10 years, and, assess the trend related to their overall performance using trend lines. Let’s say that main reason why we want to …

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Posted in Python, statistics. Tagged with , , .

Top Tutorials – Neural Network Back Propagation Algorithm

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 / …

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Posted in Deep Learning, Machine Learning. Tagged with , , .

Product Manager – Machine Learning Interview Questions

product manager interview questions for machine learning

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 …

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Posted in Interview questions, Machine Learning, Product Management. Tagged with , , .

Different Types of Activation Functions using Animation

In this post, you will be seeing different types of activation functions used in neural networks in form of an animation. If you are starting on deep learning and wanted to know about different types of activation functions, you may want to bookmark this page for quicker access in future. Without further ado, let’s take a look at the animation which represents different types of activation functions: Here is the list of different types of activation functions shown in above animation: Identity function (Used in Adaline – Adaptive Linear Neuron) Sigmoid function Tanh functon ArcTan function (inverse tangent function) ReLU (Rectified Linear Unit) Leaky ReLU (Improved version of ReLU) Randomized …

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Posted in Deep Learning. Tagged with , , .

Feed Forward Neural Network Python Example

In this post, you will learn about the concepts of feed forward neural network along with Python code example. In order to get good understanding on deep learning concepts, it is of utmost importance to learn the concepts behind feed forward neural network in a clear manner. Feed forward neural network learns the weights based on back propagation algorithm which will be discussed in future posts. In this post, the following topics are covered: What’s feed forward neural network? Feed forward neural network Python example What’s Feed Forward Neural Network? Feed forward neural network represents the mechanism in which the input signals fed forward into a neural network, passes through different layers of the …

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Posted in Data Science.