Machine Learning Training

Internshala Summer Trainings

Learned Machine Learning from scratch and take the first step towards AI with Six-week certified training powered by Analytics Vidhya

1. Introduction to Machine Learning

Understand the basics and applications of Machine Learning.

2. Python for Machine Learning

Learn basics of Python programming

Data types in Python and how to work with DataFrames, Introduction to Python, Setting Up System, Operators in Python, Data Types in Python, Conditional Statements, Looping Constructs,Functions in Python, Data Structures in Python, Standard Libraries Reading CSV Files in Python, Working with Dataframes

3. Machine Learning Life Cycle

Learn steps to build Machine Learning models and understand various visualization techniques

Introduction to Predictive Modeling, Understanding Hypothesis Generation, Data Extraction, Understanding Data Exploration, Reading Data in Python, Variable Identification, Univariate Analysis Bivariate Analysis, Missing Values and Outlier Treatment, Variable Transformation, Basics of Model Building

4. Data Exploration and Manipulation

Learn data exploration and manipulation using univariate and bivariate analysis

Problem Statement and Univariate Analysis, Data Manipulation and Bivariate Analysis

5. Build Your First Model

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

Introduction and Overview, Preparing the Dataset, Building a Regression Model, Building a Classification Model

6. Evaluation Metrics

Learn how to evaluate metrics for classification and regression task.

Introduction to Evaluation Metrics, Evaluation Metrics for Classification Task, Evaluation Metrics for Regression Task

7. k-NN

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

Building a kNN Model, Introduction to sklearn, Implementing kNN Algorithm

8. Selecting The Right Model

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

Overfitting and Underfitting, Different Validation Techniques, Bias Variance Tradeoff

9. Linear Regression

Learn how to build and implement linear regression

Introduction to Linear Model, Cost Function and Gradient Descent, Building a Linear Regression

10. Logistic Regression

Learn how to build and implement logistic regression.

Building a Logistic Regression Model, Multiclass Using Logistic Regression

11. Decision Trees

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

Basics of Decision Tree, Selecting the Best Split Point, Building a Decision Tree Model

12. Feature Engineering

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

Introduction to Feature Engineering, Feature Preprocessing, Feature Generation, Feature Engineering with Date-Time Variables, Automated Feature Engineering

13. Basics of Ensemble Models

Learn about what are ensemble models and implement basic ensemble techniques

Getting Started with Ensemble Models

14. Random Forest

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

Basics of Random Forest, Building a Random Forest

15. Clustering

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

Introduction to Clustering, Evaluation Metrics for Clustering, K-means Clustering

Final Project

Project Problem Statement

Customer Churn Prediction:

A Bank wants to take care of customer retention for its product: savings accounts. The bank wants you to identify customers likely to churn balances below the minimum balance. You have the customers information such as age, gender, demographics along with their transactions with the bank. Your task as a data scientist would be to predict the propensity to churn for each customer.