About this course
Master gradient boosting and AutoML.
Real World Usecases
1 Parts
- 0:05 Hr
Global Implementations
5 Min
Prerequisites & Readiness Check
2 Parts
- 1:15 Hr
Prerequisites for Mastering Data Science: From Fundamentals to AI-Driven Insights - Advanced
45 Min
Data Science Advanced Environment Setup Guide
30 Min
Chapter 1: Advanced Machine Learning
3 Parts
- 3:15 Hr
Gradient Boosting
90 Min
AutoML and Evaluation
60 Min
Exercise: Evaluating Model Performance
45 Min
Chapter 2: Feature Engineering and Visualization
3 Parts
- 3:15 Hr
Feature Engineering
90 Min
Advanced Visualization
60 Min
Exercise: Feature Engineering for Predictive Modeling
45 Min
Chapter 3: Time-Series Analysis
3 Parts
- 3:30 Hr
Time-Series Basics
60 Min
Forecasting Models
90 Min
Exercise: Time-Series Practice
60 Min
Assignment 1
2 Parts
- 0:45 Hr
Boosting Ensemble Pipeline for Customer Churn Prediction
45 Min
Submit your Assignment here
Submit your Assignment here
Min Grade: 40
Chapter 4: Cloud and MLOps Basics
3 Parts
- 3:30 Hr
Cloud Platforms
90 Min
MLOps Introduction
60 Min
Exercise: Cloud and MLOps Practice
60 Min
Chapter 5: Introduction to NLP
3 Parts
- 3:00 Hr
Text Processing
60 Min
Neural Networks for NLP
60 Min
Exercise: NLP Practice
60 Min
Chapter 6: Deep Learning Foundations
3 Parts
- 4:00 Hr
Neural Architectures
90 Min
Advanced Architectures
90 Min
Exercise: Building a Convolutional Neural Network
60 Min
Assignment 2
2 Parts
- 0:45 Hr
Advanced Boosting with Real-World Scenarios and Hands-On Tuning
45 Min
Submit Your Assignment here
Submit Your Assignment here
Min Grade: 40
Chapter 7: Advanced NLP
3 Parts
- 4:00 Hr
Transformer Models
90 Min
Generative AI
90 Min
Exercise: Advanced NLP Practice
60 Min
Chapter 8: Distributed Computing
3 Parts
- 3:15 Hr
Big Data Frameworks
90 Min
Distributed ML
60 Min
Exercise: Streaming Data with Kafka
45 Min
Chapter 9: Advanced MLOps
3 Parts
- 3:30 Hr
Production MLOps
90 Min
Monitoring and Explainability
60 Min
Exercise: Deploying a Model with Kubernetes
60 Min
Assignment 3
2 Parts
- 0:45 Hr
MLOps-Ready Boosting System with Monitoring, Explainability, and Kubernetes Deployment
45 Min
Submit Your Assignment
Submit Your Assignment
Min Grade: 40
Capstone Project 1
2 Parts
- 50:00 Hr
Advanced Time-Series Forecasting and Visualization System
3000 Min
Submit your Project here
Submit your Project here
Min Grade: 40
Capstone Project 2
2 Parts
- 50:00 Hr
Real-Time Fraud Detection with Spark and Kafka
3000 Min
Submit your Project here
Submit your Project here
Min Grade: 40
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