Each project comes with 2-5 hours of micro-videos explaining the solution.

Get access to 102+ solved projects with iPython notebooks and datasets.

Add project experience to your Linkedin/Github profiles.

Importing and understanding the dataset

Understanding unbalanced data and converting class into factors

Dividing the dataset into equal parts with equal distribution of both classes

Imputing for the null values

Defining cross-validation, metrics, and preprocessing techniques

Understanding and implementing LOGISTIC REGRESSION and selecting important features

Applying Bayesian model along with recursive partitioning

Improve Logistic Results using Random Forest

Implementing boosting like AdaBoost and GradientBoostingClassifier.

Defining AUC-ROC score and getting in-depth knowledge of how it works

Using Gini, AUC, and KS for evaluating model

Understanding Recall, Precision and F1score

Model Improvement with Gaussian RBF kernel

Display Performance Reports and interpreting the same

Visualizing the result of each model via plot

In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.

Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.

In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.

In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.

Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.

In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.

In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Introduction

07m

Import Data Sets

01m

Rename Columns

10m

Next Steps

00m

Import Libraries

06m

Distribution of Columns

04m

Different Approaches

02m

Weight of Evidence (WOE)

12m

Information Value (IV)

04m

Compute WOE and IV

06m

Univariate, Bivariate and Multivariate

06m

Logistic Regression Introduction

07m

Recap

01m

Library Overview

01m

Data Set Overview

02m

Next Steps - Overview

02m

Custom Functions

05m

Function to Compute WOE and IV

02m

Compute WOE and IV for each Variable

21m

Variable Clustering

05m

Training and Testing Sample

02m

Logistic Regression

11m

Logistic Regression - QnA

08m

Decision Tree

08m

Random Forest

04m

Logistic Regression using important variables

03m

Conditional Tree

01m

Bayesian Learn Model

03m

KSVM - Kernel Support Vector Machines

05m

Neural Network

04m

Compare all Models

05m