Data Science Tutorials

Learn how gradient descent optimizes models for machine learning. Discover its applications in linear regression, logistic regression, neural networks, and the key types including batch, stochastic, and mini-batch gradient descent.

Learn about the most popular deep learning model RNN and get hands-on experience by building a MasterCard stock price predictor.

Learn basic MLOps and end-to-end development and deployment of ML pipelines.

Learn about how learning curves can help you evaluate your data and identify optimal solutions.

Prepare for your first Kaggle Competition with this step-by-step tutorial.

Learn about Hadoop ecosystem, the architectures and how to start with Cloudera.

Learn about Kaggle datasets and notebooks and get a head start on creating your Kaggle profile.

A tutorial to understand what correlation is and why it is important for every aspiring data scientist to know it.

A detailed guide to time series forecasting. Learn to use python and supporting frameworks. Learn about the statistical modelling involved.

The tutorial answers the most frequently asked questions about deep learning and explores various aspects of deep learning with real-life examples.

The data scientist's comprehensive guide to mastering PyCharm

Data cleaning is a very basic building block of data science. Learn the importance of data cleaning and how to use Python and carry out the process.