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 this exercise, you will get a step-wise hands-on guide on how to build a basic deep neural network using Keras, and how to use it to predict the class of the test samples.
Skills you will develop:
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, the intermittent MCQs would help you concretize your understanding.
Skills you will develop:
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, dimensionality reduction using the corresponding scikit-learn classes. You will also evaluate the performance of your final ensembling model using various performance metrics.
Skills you will develop:
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 data in various forms, including 2-D and 3-D visualizations.
Skills you will develop:
Welcome to this project on Getting Started with Git. In this project, you will understand how to use Git, one of the most famous distributed version-control systems to track changes in files and source code in your projects during software development.
In this exercise, you will learn about creating git repository, commits, branches, and merge branches. Additionally, you will learn how to create and clone a GitHub repository and SSH keys to push the changes to the remote repository.
Skills you will develop:
