Deep Learning has successfully created hype among students and researchers. Most of the research fields require a lot of funding and well-equipped labs. However, you will only need a computer to work with DL at the initial levels. You don't even have to worry about the computation power of your computer. Many cloud platforms are available where you can run your model.

Deep learning is a booming field at the current time, most of the projects and problem statement uses deep learning in any sort of work. If you have to pick a deep learning technique for solving any computer vision problem statement then many of you including myself will go with a conventional neural network. In this article, we will build our first image processing project using CNN and understand its power and why it has become so popular. In this article, we will walk through every step of developing our own convolutional model and build our first amazing project. Image classification is a task where the system takes an input image and classifies it with an appropriate label.

The Pima Indians of Arizona and Mexico have the highest reported prevalence of diabetes of any population in the world. A small study has been conducted to analyse their medical records to assess if it is possible to predict the onset of diabetes based on diagnostic measures. The dataset is downloaded from Kaggle, where all patients included are females at least 21 years old of Pima Indian heritage. The objective of this project is to build a predictive machine learning model to predict based on diagnostic measurements whether a patient has diabetes. This is a binary (2-class) classification project with supervised learning. Jupyter Notebook (Python) could be used to follow the process below.

The Pima Indians of Arizona and Mexico have the highest reported prevalence of diabetes of any population in the world. A small study has been conducted to analyse their medical records to assess if it is possible to predict the onset of diabetes based on diagnostic measures. The dataset is downloaded from Kaggle, where all patients included are females at least 21 years old of Pima Indian heritage. The objective of this project is to build a predictive machine learning model to predict based on diagnostic measurements whether a patient has diabetes. This is a binary (2-class) classification project with supervised learning. Jupyter Notebook could be used to follow the process below.

After modeling, the next stage is always analyzing how our model is performing and why it is doing what it's doing. However, if you've had the chance to work with ensemble methods, you probably already know that these algorithms are usually known as "black-box models". These models lack explicability and interpretability since the way they usually work implies one or several layers of a machine making decisions without human supervision, apart from a group of rules or parameters set. More often than not, not even the most expert professionals in the field can understand the function that is actually created by, for example, training a neural network. In this sense, some of the most classical machine learning models were actually better. That's why, for the sake of this post, we'll be analyzing the feature importance of our project using a classic Logistic Regression.