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  • Project - Credit Card Fraud Detection using Machine Learning

    Topic
    13 Concepts | 12 Assessments | 193 Learners

    Welcome to this project on Credit Card Fraud Detection. In this project, you will use Python, SMOTE Technique(to over-sample data), build a Logistic Regression Classifier, and apply it to detect if a transaction is fraudulent or not.

    The real world datasets often might be with data of imbalanced classes. It is very important to feed a decent number of data samples of each class in a classification problem so that the classifier would detect the underlying hidden patterns for each class and prepare itself to reasonably classify the test data. Upon completing this project, you will understand the pragmatic …

    Instructor: Vagdevi K
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    Project - Image Classification with Pre-trained InceptionV3 Network

    Topic
    1 Concept | 4 Assessments | 188 Learners

    Welcome to this project on Image Classification with Pre-trained InceptionV3 Network. This project aims to impart the knowledge of how to access the pre-trained models(here we get pre-trained Inception model) from Keras of TensorFlow 2, and appreciate its powerful classification capacity by making the model predict the class of an input image.

    Understanding the pre-trained models is very important because this forms the basis of transfer learning. one of the most appreciated techniques to perform the classification of a different task thus reducing the training time, the number of iterations, and resource consumption. Learning about the pre-trained models and …

    Instructor: Vagdevi K
  • Welcome to this project on NYSE Closing Price Prediction. In this project, you will use Pandas, Keras, and Python in order to build a predictive model and apply it to predict the closing prices.

    Time-series modeling has a huge demand in today's numbers-filled world. It has a wide variety of applications in sales s forecasting, prediction of meteorological elements like rainfall, economic forecasting in the financial worlds, and many more.

    In this exercise, we shall understand how to predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data. We shall focus …

    Instructor: Vagdevi K
  • Iris Flowers Classification using Deep Learning & Keras

    Topic
    8 Concepts | 1 Question | 8 Assessments | 160 Learners

    Welcome to this project on Classifying Flowers in Iris dataset with Deep Neural Network using Keras. In this project, you will use Python and Keras to build a Deep Neural Network, and apply it to predict the classes of Flowers in the Iris dataset.

    Keras is one of the most extensively used APIs in the world of Deep Learning. It provides an amazing developer-friendly deep learning framework to build deep learning models with wide-ranging features to support high scalability, because of which it is not only widely used in academics but also in organizations to build state-of-the-art research models. In …

  • Getting Started with Matplotlib

    Topic
    8 Concepts | 12 Assessments | 125 Learners

    Welcome to this project on Getting Started with Matplotlib. In this project, you will understand how to use Matplotlib, one of the most famous visualizing libraries in Python.

    Data visualization is one of the most prominent ways of analyzing the data. It presents visually appealing ways to detect the patterns, noise, outliers, and many other insights, which would assist the data scientists to understand, transform, and refine the data to build better comprehensive models. This project would help you build data visualization skills on top of your existing Python programming skills. You will understand how to use Matplotlib to depict …

  • Welcome to this project on Building a CNN Classifier using TensorFlow 2 for MNIST Fashion Dataset. In this project, we will understand how to use the TensorFlow 2 platform to build a simple classifier using Convolutional Neural Networks(CNNs).

    CNNs have been one of the state-of-art tools in the current era of Computer Vision. The present-day deep learning and computer vision communities find numerous applications of CNNs in classification tasks and object detection use cases. These use cases have formed the basis of many real-time applications like self-driving cars, face recognition apps, satellite photo analysis and classification, and many more …

    Instructor: Vagdevi K
  • Image Stitching using OpenCV and Python (Creating Panorama Project)

    Topic
    12 Concepts | 11 Assessments | 87 Learners

    Welcome to this project on Image Stitching using OpenCV. In this project, we will use OpenCV with Python and Matplotlib in order to merge two images and form a panorama.

    As you know, the Google photos app has stunning automatic features like video making, panorama stitching, collage making, and many more. In this exercise, we will understand how to make a panorama stitching using OpenCV with Python.

    Skills you will develop:

    • OpenCV
    • Python
    • Matplotlib
    Instructor: Vagdevi K
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    Project - How to Deploy an Image Classification Model using Flask

    Topic
    2 Concepts | 13 Assessments | 70 Learners

    Welcome to this project on Deploy Image Classification Pre-trained Keras model using Flask. In this project, we will have a comprehensive understanding of how to deploy a deep learning model as a web application using the Flask framework.

    Developing a machine learning or deep learning model is very important to solve problems using AI. On the other hand, it is equally important to have a knowledge of how to deploy those amazing problem-solving models into such an interface which enables the users to make use of these solutions. Even many apps we use today, like YouTube, Amazon/Flipkart shopping, FaceApps …

    Instructor: Vagdevi K
  • Welcome to this project on Sentiment Analysis using TensorFlow 2. This project aims to impart an understanding of how to process English sentences, apply NLP techniques, make the deep learning model understand the context of the sentence, and classify the sentiment the sentence implies.

    Our real-world is being flooded with a lot of reviews all around us. Be it an online shopping mart, movie reviews, offline market, or anything else. It has become very common for us to rely on these reviews. Hence it would be really helpful for a Machine Learning aspirant to understand various techniques related to processing …

    Instructor: Vagdevi K
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    Project - Introduction to Transfer Learning (Cat vs Non-cats Project)

    Topic
    14 Concepts | 2 Questions | 10 Assessments | 65 Learners

    Welcome to this project on Cat vs Non-cat Classifier using Transfer Learning. In this project, you will use Python and Keras to apply the Transfer Learning technique in order to build an image classifier, and apply it to predict the class of an input image - whether it is a cat or a non-cat.

    Deep Learning is computationally intensive, often demanding powerful computational resources to yield reasonable accuracies in the real world. The idea of Transfer Learning has become a boon for the computer vision and deep learning community as it has reduced the hunger of Deep Learning algorithms for powerful …

    Instructor: Vagdevi K