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  • This Data Science Certification Program is a self-paced online course. This gives you complete freedom about your schedule and convenience. This course has over 220+ hours of learning. This consists of 5 courses (Big Data with Hadoop, Big Data with Spark, Python, Machine Learning, and Deep Learning).

  • Project - Churn Emails Inbox with Python

    Topic
    4 Concepts | 6 Assessments | 3,642 Learners

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

    Skills you will develop:

    1. Python
    2. File Handling in Python
    Instructor: Abhinav Singh
  • Project - Forecast Bike Rentals

    Topic
    9 Concepts | 14 Questions | 15 Assessments | 1,532 Learners

    Welcome to this project on the Forecasting Bike Rentals with DecisionTreeRegressor, LinearRegression, RandomForestRegressor using scikit-learn. In this project, you will use Python and scikit-learn to build models using the above-mentioned algorithms, and apply them to forecast the bike rentals.

    Forecasting is a regression problem, which is a highly demanded skill in the real world. This exercise enables you to understand the basic workflow to solve a regression problem, which includes data preprocessing and data modeling steps. You will understand how Pandas and scikit-learn, in association with Python, could be used to solve a machine learning problem end-to-end project. In addition …

    Instructor: Sandeep Giri
  • Project - Classify Clothes from Fashion MNIST Dataset using Machine Learning Techniques

    Topic
    12 Concepts | 7 Questions | 11 Assessments | 1,137 Learners

    Welcome to this project on Classify Clothes from Fashion MNIST Dataset with a couple of Machine Learning algorithms like SGD Classifier, XGBClassifier, Softmax Regression (multi-class LogisticRegression), DecisionTreeClassifier, RandomForestClassifier, Ensemble (with soft voting) using scikit-learn. In this project, you will use Python and scikit-learn to build Machine Learning models, and apply them to predict the class of clothes from Fashion MNIST Dataset.

    In this end-to-end Machine Learning project, you will get a hands-on overview of how to methodologically solve a machine learning classification problem. As a part of it, you will understand various methods of improvising the models using hyperparameter tuning …

    Instructor: Sandeep Giri
  • Project - Predicting Titanic Passenger Survival using Machine Learning and Python

    Topic
    7 Concepts | 2 Questions | 12 Assessments | 539 Learners

    Welcome to this project on the Titanic Machine Learning Project with Support Vector Machine Classifier and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to build SVC and random forest, and apply them to predict the survival rate of Titanic passengers.

    Data preprocessing is one of the most prominent steps to make an effective prediction model in Machine Learning, and it is often a best practice to use data preprocessing pipelines. In this exercise, you will also learn how to build your custom data transformers and chain all these data pre-processing steps using scikit-learn pipelines …

  • Project - Building Cat vs Non-Cat Image Classifier using NumPy

    Topic
    11 Concepts | 2 Questions | 19 Assessments | 429 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 …

    Instructor: Vagdevi K
  • Project - Building Spam Classifier

    Topic
    13 Concepts | 2 Questions | 12 Assessments | 398 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 …

  • Project - Credit Card Fraud Detection using Machine Learning

    Topic
    13 Concepts | 12 Assessments | 191 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
  • 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 | 157 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 …