Introduction: After completion of data preprocessing, we need to do the following steps to build the regression model. 1. Divide the Data into the Training and Test Sets. 2. Creating and Training a Neural Network with TensorFlow 2.0 3. Evaluating Neural Networks Performance * Divide the Data into the Training

Introduction This is a series of 4 articles where we will explore how to build a horse power prediction model using Deep Learning Regression from scratch. We will cover data pre-processing, model building, model tracking and model deployment in this series of blogs. In this blog we will cover data

"Structured data classification with deep learning offers groundbreaking potential for heart disease prediction. By harnessing the capabilities of neural networks, this approach can process medical data efficiently and deliver precise diagnoses, leading to improved patient care and better health outcomes." This series of five blogs will guide you through a

Introduction After data collection and data preprocessing, we have to build the model, train it, validate it and test it. In this article, we will delve into Model creation, its architecture, training, validation and testing.  The input all_features = layers.concatenate(    [         sex_encoded,         cp_encoded,         fbs_encoded,         restecg_encoded,         exang_

Introduction Model deployment is crucial as it bridges the gap between theoretical models and real-world applications. It enables practical utilisation of machine learning and AI solutions, delivering valuable outcomes to businesses and users. Efficient deployment ensures scalability, reliability, and accessibility, maximising the impact of AI technology in various domains. We

Introduction MLflow is an open-source platform designed to manage the machine learning models. It enables data scientists and engineers to track experiments. With components like tracking, projects, and models, MLflow allows users to organise and reproduce experiments efficiently. It supports various machine learning frameworks and cloud platforms, promoting collaboration and

Evaluation Metrics are quantitative measures that are used to assess the performance of a statistical or machine-learning model. These metrics provide insights as to how well the model is performing and help in comparing different models and algorithms.  There are two types of models, regression model (continuous output) and classification

In the previous article, we explored the different data preprocessing steps using the Auto MPG dataset. In this article, we will be looking at exploratory data analysis and the different methods. Exploratory Data Analysis is used to analyze and investigate datasets and summarize their main characteristics, often employing data visualization

Real-world data is often incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data, Noisy: containing errors or outliers. Inconsistent: containing discrepancies in codes or names. Data preprocessing is a technique that converts raw data into understandable data. It transforms the data into a format that

AI is used to gather data to create natural sounding voices that can read digital text. Hence audio deep fakes are closely related to text to speech - TTS technology. Text to speech (TTS) is a technology that converts text input into spoken audio. It can read aloud PDFs, websites,

Audio deepfake, also called voice cloning is a type of AI technology which is used to create audio files that sound like a particular person. The term ‘deepfake’ comes from the underlying technology ‘deep learning’ which is a form of AI.  In recent times, audio deepfake also known as audio

In this article we are going perform the sentiment analysis on restaurant reviews dataset. We are going to build an explainable model using of logistic regression from the glass box library and show the explainability of the model. DATA PREPROCESSING The first step in the context of sentiment analysis is

In this captivating blog, we embark on an exciting journey into the world of Convolutional Neural Networks (CNNs) — a revolutionary class of artificial intelligence models that have transformed the fields of computer vision, image recognition, and beyond. Introduction: As we said earlier Convolutional Neural Networks (CNNs) have revolutionized the field

"Education is not the learning of facts, but the training of the mind to think."                              - Albert Einstein This quote by Albert Einstein emphasizes that education goes beyond the mere accumulation of facts and highlights the importance of developing a mindset focused on learning and critical thinking. It aligns with

In the field of artificial intelligence, an exciting breakthrough has opened doors to endless possibilities. It's called Generative Adversarial Networks (GANs) that has a remarkable ability to generate captivating images, fake data and many more. You may have already experienced the wonders of AI tools that produce new images and

In the domain of natural language processing, we have seen that Recurrent Neural Network (RNN) play a crucial role. We have seen how RNN bridge the gap between previous information and the current tasks. The potential applications are from analyzing the video frames to predicting the next words in a

Interpretability in AI refers to the ability to explain how artificial intelligence models arrive at their predictions or decisions in a way that humans can easily comprehend. It allows us to understand the inner workings of AI models, make fair judgments, ensure their reliability, and hold them accountable. What is

Introduction In the previous blogs we explored data pre-processing and model building, in this article we will explore model tracking using MLFlow. MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle. It was developed by Databricks and has become widely used in the machine learning community.

Introduction In the initial period When Machine Learning was introduced, there were very few ways to make the machine learn and what we wanted them to learn. There we started to get an idea of machines and what a machine can do for us, in this process, we started generating