Advanced Machine Learning Algorithms

Advanced Machine Learning Algorithms

This course is part of Fractal Data Science Professional Certificate

Instructor: Analytics Vidhya

Access provided by Federal Bank

2,283 already enrolled

6 modules

Gain insight into a topic and learn the fundamentals.

12 reviews

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

12 reviews

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Employ regularization techniques for enhanced model performance and robustness.
Leverage ensemble methods, such as bagging and boosting, to improve predictive accuracy.
Implement hyperparameter tuning and feature engineering to refine models for real-world challenges.
Combine diverse models for superior predictions, expanding your predictive toolkit.

Skills you'll gain

Tools you'll learn

Classification Algorithms

Details to know

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Assessments

8 assignments

Taught in English

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This course is part of the Fractal Data Science Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Fractal Analytics

There are 6 modules in this course

In a world where data-driven solutions are revolutionizing industries, mastering advanced machine learning techniques is a pivotal skill that empowers innovation and strategic decision-making. This equips you with the expertise needed to harness advanced machine-learning algorithms. You will delve into the intricacies of cutting-edge machine-learning algorithms. Complex concepts will be simplified, making them accessible and actionable for you to harness the potential of advanced algorithms effectively. By the end of this course, you will learn to:

1. Employ regularization techniques for enhanced model performance and robustness. 2. Leverage ensemble methods, such as bagging and boosting, to improve predictive accuracy. 3. Implement hyperparameter tuning and feature engineering to refine models for real-world challenges. 4. Combine diverse models for superior predictions, expanding your predictive toolkit. 5. Strategically select the right machine learning models for different tasks based on factors and parameters.

In the fast-evolving field of machine learning, overfitting and underfitting are persistent challenges that can hinder the performance of models. The Regularization module delves deep into the techniques that address these challenges head-on. Over a span of 2 hours, learners will develop a profound understanding of how regularization techniques can enhance model generalization and robustness.

What's included

12 videos2 readings2 assignments1 programming assignment

12 videos Total 62 minutes

Introduction to the course 4 minutes
Introduction to Problem Statement 3 minutes
Division of the dataset 4 minutes
Overfitting and Underfitting 5 minutes
Introduction To The Apex Dataset 4 minutes
L1 Regularization 9 minutes
L2 Regularization 6 minutes
Elastic Net Regularization 7 minutes
Fine-Tuning Logistic Regression 4 minutes
L1 Regularization 9 minutes
L2 Regularization 3 minutes
Elastic Net Regularization 4 minutes

2 readings Total 20 minutes

Syllabus - Advanced Machine Learning Algorithms 10 minutes
Resources to be used in this module 10 minutes

2 assignments Total 60 minutes

Regularization in Linear Regression 30 minutes
Check your understanding 30 minutes

1 programming assignment Total 120 minutes

Regularization Programming Assessment 120 minutes

In this module, learners will explore Bagging Algorithms, which are techniques that group models together for more accurate predictions. Learners will start by learning the basics of Bagging and why it's better. They will discover how these algorithms work and why bootstrapping is a powerful idea. Next, they will dive deeper into types of Bagging Algorithms. They will explore Random Forests, Extra Trees, and how to use Bagging with classifiers.

What's included

6 videos2 readings1 assignment1 programming assignment

6 videos Total 28 minutes

Understanding Ensemble Learning 4 minutes
Introducing Bagging Algorithms 4 minutes
Hands-on to Bagging Meta Estimator 7 minutes
Introduction to Random Forest 5 minutes
Understanding Out-Of-Bag Score 5 minutes
Random Forest VS Classical Bagging VS Decision Tree 3 minutes

2 readings Total 20 minutes

Resources to be used in this module 10 minutes
Extra Trees- Reading Material 10 minutes

1 assignment Total 30 minutes

Graded Assignment 30 minutes

1 programming assignment Total 120 minutes

Bagging Programming Assessment 120 minutes

In this module, learners will grasp the essence of boosting techniques and their transformative impact on model accuracy. The focus then shifts to AdaBoost, with an exploration of its underlying algorithm and the pivotal role it plays in boosting's iterative approach. Then, they will learn about Gradient Boosting Machines (GBM). The final lesson introduces learners to advanced boosting algorithm variants: XGBoost, LightGBM, and CatBoost.

What's included

6 videos1 reading1 assignment1 programming assignment

6 videos Total 33 minutes

Introduction to Boosting 5 minutes
AdaBoost Step-by-Step Explanation 8 minutes
Hands-on - AdaBoost 5 minutes
Gradient Boosting Machines (GBM) 4 minutes
Hands-on Gradient Boost 3 minutes
Other Algo (XGBoost, LightBoost. CatBoost) 8 minutes

1 reading Total 10 minutes

Resources to be used in this module 10 minutes

1 assignment Total 30 minutes

Graded: Check Your Understanding 30 minutes

1 programming assignment Total 180 minutes

Boosting Assessment 180 minutes

What's included

10 videos1 reading2 assignments1 programming assignment

10 videos Total 54 minutes

Introduction to Feature Engineering and Hyperparameter Tuning 2 minutes
Spliting the dataset 2 minutes
Feature Transformation 7 minutes
Feature Generation 10 minutes
Feature Seletion 10 minutes
Introduction to Hyperparameter and Grid Search CV 4 minutes
Grid Search CV 7 minutes
Random Search CV 4 minutes
Bayesan Optimization 4 minutes
Bayesian Optimization in synergix dataset 4 minutes

1 reading Total 10 minutes

Resources to be used in this module 10 minutes

2 assignments Total 60 minutes

Graded Assignment 30 minutes
Check your understanding 30 minutes

1 programming assignment Total 120 minutes

Feature Engineering and Hyperparameter Tuning 120 minutes

This module, dedicated to 'Combining Models,' offers learners a concise yet insightful exploration into the realm of leveraging multiple models for superior performance. Learners will explore why mixing models is a great idea. They will delve into fundamental concepts of stacking, blending, and aggregation.

What's included

5 videos1 reading1 assignment1 programming assignment

5 videos Total 24 minutes

Introduction to the module 3 minutes
Understanding Voting 4 minutes
Leveraging the Voting 5 minutes
Understanding Stacking ensemble learning 8 minutes
Understanding Hold out Method/Blending 4 minutes

1 reading Total 10 minutes

Resources to be used in this module 10 minutes

1 assignment Total 30 minutes

Check your understanding 30 minutes

1 programming assignment Total 180 minutes

Stacking Programming Assessment 180 minutes

In this module, learners will dive into the important process of picking the right machine learning model for the job. The module begins by showing why choosing the right model matters. Learners will get to know about the factors they need to consider while choosing the model. They will get a handy guide that will help them in selecting the right model. They will learn about the essential things they need to look at while selecting a model, including performance metrics.