• E

    Topic
    1 Concept | 18 Questions | 537 Learners

    Learn how Classification works in Machine Learning.

    Instructor: Sandeep Giri
  • E

    Topic
    1 Concept | 18 Questions | 537 Learners

    Learn how Classification works in Machine Learning.

  • E

    Topic
    2 Concepts | 15 Assessments | 442 Learners

    This is an end-to-end Machine Learning project. You would start by learning how to load a dataset, visualize it, fill in the missing values, create pipelines, handle categorical variables, train models based on that data, and finally predict using that model.

    This will not only help you understand how to train a machine learning model, but will also give you a detailed idea of how to clean and prepare data for machine learning, train the model, and fine tune it in real life projects.

    Skills you will develop:

    1. scikit-learn
    2. Data visualization
    3. Treating missing values in dataset 4 …
    Instructor: Cloudxlab
  • E

    Topic
    1 Concept | 390 Learners

    Learn how Regression works in Machine Learning.

    Instructor: Sandeep Giri
  • 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: Cloudxlab
  • Topic
    13 Concepts | 12 Assessments | 372 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: Cloudxlab
  • Topic
    8 Concepts | 12 Assessments | 363 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 …

    Instructor: Cloudxlab
  • Topic
    8 Concepts | 12 Assessments | 363 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 …

    Instructor: Cloudxlab
  • Topic
    8 Concepts | 12 Assessments | 363 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 …

    Instructor: Cloudxlab
  • Welcome to the project on Building a Neural Network for Image Classification with TensorFlow. In this project, we would learn how to develop a neural network classifier from very scratch, using TensorFlow 2.

    We would build and train a dense neural network on the Fashion MNIST dataset and evaluate its performance with some test samples. This project aims to impart the knowledge of the basic steps involved in building a neural network, working with TensorFlow 2, training a neural network, and make the learner comfortable with the cutting-edge technology - TensorFlow 2.

    Skills you will develop:

    1. TensorFlow 2
    2. Matplotlib …
    Instructor: Cloudxlab
  • P

    Topic
    2 Concepts | 4 Assessments | 283 Learners

    Welcome to this project on Image Classification with Pre-trained Keras models. This project aims to impart the knowledge of how to access the pre-trained models(here we get pre-trained ResNet 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: Cloudxlab
  • Welcome to this project on the Neural Style Transfer. In this project, you will use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image.

    Neural Artistic Style Transfer finds a wide range of applications to fancily modify images. This field has so much influenced the technical world that many apps, such as Prisma, have received great craze amongst the users. In recent days, decent work has also been done in this area, which served as a holy grail to our project. The heart of this capability is the convolutional neural network …

    Instructor: Cloudxlab
  • S

    Topic
    1 Concept | 6 Assessments | 271 Learners

    Welcome to this project on Churning the Emails Inbox with Scala. In this project, you will use Scala to access the data from files and process it to achieve certain tasks. You will explore the MBox email dataset, and use Scala to count lines, headers, subject lines by emails and domains. Know your way on how to work with data in Scala.

    Skills you will develop:

    1. Scala
    2. File Handling in Scala
    3. Text Search in Scala
    Instructor: Cloudxlab
  • P

    Topic
    2 Concepts | 6 Assessments | 266 Learners

    Welcome to the project on Mask R-CNN with OpenCV for Object Detection. In this project, we will learn how to read a pre-trained TensorFlow model for object detection using OpenCV.

    The real-world scenarios have a lot of applications based on object detection. For example, object detection models are used in self-driving cars to recognize where the pedestrians are, where the are vehicles located, where the signals are, etc in the given frame of view. So, it is very important to develop an understanding of how to use a pre-trained object detection model so that we could later customize it based …

    Instructor: Cloudxlab
  • Learn how Regression works in Machine Learning.

    Instructor: Sandeep Giri