Machine Learning Training

Python for Machine Learning

Learn basics of Python programming, data types in Python and how to work with DataFrames

Machine Learning Life Cycle

Learn steps to build machine learning models and understand various visualization techniques

Data Exploration and Manipulation

Learn data exploration and manipulation using univariate and bivariate analysis

Build Your First Model

Learn to prepare a dataset and build your first model for regression and classification problem

Evaluation Metrics

Learn how to evaluate metrics for classification and regression tasks

k-NN

Learn how to build a kNN model and understand multiple distance metrics

Selecting the Right Model

Learn how to visualize overfitting and underfitting using kNN and understand various validation techniques

Linear Regression

Learn how to build and implement linear regression

Logistic Regression

Learn how to build and implement logistic regression

Decision Trees

Understand how the decision tree algorithm works and learn about the different techniques used for splitting. Build a decision tree model

Feature Engineering

Perform feature engineering for numerical, categorical, and date-time based features

Basics of Ensemble Models

Learn about what are ensemble models and implement basic ensemble techniques

Random Forest

Understand how a random forest algorithm works and learn how to build a model on a dataset

Clustering

Learn about the basics of clustering and how to evaluate clustering models. Build a clustering model using K-means