• E

    Topic
    1 Concept | 18 Questions | 962 Learners

    Learn how Classification works in Machine Learning.

    Instructor: Sandeep Giri
  • E

    Topic
    1 Concept | 18 Questions | 962 Learners

    Learn how Classification works in Machine Learning.

  • A

    Topic
    2 Concepts | 21 Questions | 955 Learners

    Learn more about Analytics and Data Science, probability, normal distribution, variance, data cleaning, feature scaling, standardization from industry experts.

    Instructor: Sandeep Giri
  • I

    Topic
    2 Concepts | 14 Questions | 945 Learners

    An introduction to AI and Machine Learning for Managers.

  • A

    Topic
    3 Concepts | 21 Questions | 856 Learners

    Learn more about Analytics and Data Science, probability, normal distribution, variance, data cleaning, feature scaling, standardization from industry experts.

  • 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: Cloudxlab
  • Topic
    13 Concepts | 2 Questions | 12 Assessments | 722 Learners

    Welcome to this project on the Spam Classifier Project with Logistic Regression Classifier using scikit-learn. In this project, you will use Python and scikit-learn to build a Logistic Regression Classifier, and apply it to predict whether an email is Spam or Ham.

    The world is full of textual data being generated at a very rapid pace each second. The most important data preprocessing steps include accessing and cleansing the real-time data, transforming it to get a refined form, and making it in an ML-algorithm compatible way by representing the textual data into numerical form. You will learn to achieve all …

    Instructor: Cloudxlab
  • Topic
    8 Concepts | 12 Assessments | 650 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 | 650 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 | 650 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
  • E

    Topic
    2 Concepts | 15 Assessments | 625 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
  • 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: Cloudxlab
  • Topic
    13 Concepts | 12 Assessments | 580 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
  • 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
  • E

    Topic
    1 Concept | 545 Learners

    Learn how Regression works in Machine Learning.

    Instructor: Sandeep Giri