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Guided Projects

Free Guided Projects

  • 13 Concepts | 12 Assessments | 528 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 application of various Pandas functions, with a clear picture of how to over-sample the dataset with imbalanced classes using the SMOTE technique and how to use the thus obtained data to train a classifier.

    Skills you will develop:

    1. Pandas
    2. Python Programming
    3. SMOTE
    4. Scikit-Learn
  • 12 Concepts | 11 Assessments | 534 Learners

    Welcome to this project on Image Stitching using OpenCV. In this project, you 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
  • 14 Concepts | 18 Assessments | 553 Learners

    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 on data cleaning and data modeling to build a predictive model for stock market closing prices.

    Skills you will develop:

    • Keras
    • Pandas
    • Data Processing
    • Python
    • Deep Learning
  • 14 Concepts | 2 Questions | 10 Assessments | 515 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 hardware resources. In addition, Transfer Learning has made it possible to yield impressive results within a short amount of time, even with fewer data. Upon completing this project, you will understand the pragmatic application of the transfer learning technique using Keras, and you will be able to appreciate its salient features.

    Skills you will develop:

    • Transfer Learning
    • Keras
    • Python Programming
    • Tensorflow
  • 11 Concepts | 2 Questions | 19 Assessments | 687 Learners

    Welcome to this project on the Numpy - Cat vs Non-cat Classifier with Logistic Regression using Numpy. In this project, you will use Python and Numpy to build a Logistic Regression Classifier from scratch, and apply it to predict the class of an input image - whether it is a cat or a non-cat.

    Though we have a lot of ready-made APIs like scikit-learn and Keras to build Machine Learning and Deep Learning models, it is very essential for a Machine Learning enthusiast to clearly understand the hidden mechanism behind the working of ML models. Upon completing this project, you will understand the pragmatic application of various Numpy functions with a clear picture of workflow in an end-to-end Machine Learning project.

    Skills you will develop:

    1. Numpy
    2. Python Programming
    3. H5py files
    4. Machine Learning