Efficient Machine Learning

Efficient Machine Learning, available at $19.99, has an average rating of 3.85, with 31 lectures, based on 26 reviews, and has 5068 subscribers.

You will learn about Master Machine Learning Performing Ideal Preprocessing Understand Feature Engineering Understand Feature Selection Know the Best Way to Evaluate Models Analyse Models and Overcome its Challenges Hyperparameters Tuning Make Accurate Predictions Work with Real-World Data This course is ideal for individuals who are Anyone interested in Machine Learning or Any data analysts who want to level up in Machine Learning or Anyone who want to master Machine Learning It is particularly useful for Anyone interested in Machine Learning or Any data analysts who want to level up in Machine Learning or Anyone who want to master Machine Learning.

Enroll now: Efficient Machine Learning

Summary

Title: Efficient Machine Learning

Price: $19.99

Average Rating: 3.85

Number of Lectures: 31

Number of Published Lectures: 31

Number of Curriculum Items: 31

Number of Published Curriculum Objects: 31

Original Price: 19.99

Quality Status: approved

Status: Live

What You Will Learn

Master Machine Learning

Performing Ideal Preprocessing

Understand Feature Engineering

Understand Feature Selection

Know the Best Way to Evaluate Models

Analyse Models and Overcome its Challenges

Hyperparameters Tuning

Make Accurate Predictions

Work with Real-World Data

Who Should Attend

Anyone interested in Machine Learning

Any data analysts who want to level up in Machine Learning

Anyone who want to master Machine Learning

Target Audiences

Anyone interested in Machine Learning

Any data analysts who want to level up in Machine Learning

Anyone who want to master Machine Learning

If you’re a machine learning specialist looking to transition into real-world AI applications, this comprehensive course will be your ultimate guide. By teaching you how to scale up your machine learning model to achieve the best performance, you’ll learn everything you need to advance your model to the next stage.

This course is designed for both beginners with some programming experience and experienced developers who want to make the leap to Data Science. Throughout this course, you’ll gain valuable knowledge and practical skills that will empower you to excel in your AI career.

Key features of the course include:

1. A strong foundation in machine learning concepts and algorithms, providing you with the necessary theoretical background to build and optimize your models.

2. Practical, hands-on experience with popular machine learning frameworks, such as TensorFlow, PyTorch, and Scikit-learn, enabling you to implement and fine-tune your models effectively.

3. Insights into deploying your machine learning models in real-world applications, from web services to mobile applications, ensuring that your models are ready to be utilized and make a meaningful impact.

4. Strategies for dealing with common challenges in the field, such as handling imbalanced datasets, addressing overfitting, and optimizing hyperparameters, equipping you with the tools needed to tackle any obstacles that may arise.

5. Comprehensive support from expert instructors and a thriving online community, providing you with the resources and connections necessary for your continued growth and success in the field.

By the end of this course, you’ll have a thorough understanding of machine learning principles, practical experience with state-of-the-art tools and techniques, and the confidence to apply your newfound knowledge to real-world AI applications. Whether you’re a beginner looking to launch a rewarding career in Data Science or an experienced developer eager to expand your skill set, this course will provide you with the resources and guidance you need to excel in the rapidly evolving world of AI.

You’ll learn the machine learning, AI, and data mining techniques real employers are looking for, including:

Handling Missing Values

Label Encoder

One-Hot Encoder

Normalization

Standardization

Binarization

Principal Analysis Component (PCA)

Manual Feature Engineering

Automatic Feature Engineering

Feature Selection

Model Evaluation

Confusion Matrix

Precision and Recall

F1-score and Fbeta-score

Area Under Curve (AUC)

Overfitting vs Underfitting

Cross-Validation

Analyzing Learning Curves

Hyperparameters Tuning

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Jupyter Notebook Files

Chapter 2: Preprocessing and Feature Scaling

Lecture 1: Preprocessing Intro

Lecture 2: The Dataset

Lecture 3: Missing Values

Lecture 4: Label Encoder

Lecture 5: One-Hot Encoder

Lecture 6: Normalization

Lecture 7: Standardization

Chapter 3: Feature Engineering

Lecture 1: Feature Engineering Intro

Lecture 2: Binarization

Lecture 3: Principal Component Analysis (PCA)

Lecture 4: Installing Featuretools

Lecture 5: Manual Feature Engineering

Lecture 6: Automatic Feature Engineering

Lecture 7: Feature Selection 1 (Intro)

Lecture 8: Feature Selection 2 (Univariate Selection)

Lecture 9: Feature Selection 3 (Feature Importance)

Lecture 10: Feature Selection 4 (Model-Based Feature Selection)

Lecture 11: Feature Selection 5 (Recursive Feature Elimination)

Chapter 4: Model Evaluation and Selection

Lecture 1: Model Evaluation and Selection Intro

Lecture 2: Regression Evaluation

Lecture 3: Classification Accuracy

Lecture 4: Confusion Matrix – Precision and Recall

Lecture 5: F1-score and Fbeta-score

Lecture 6: Area Under Curve (AUC)

Lecture 7: Evaluation Measures for Multi-Class Classification

Lecture 8: Overfitting vs Underfitting

Lecture 9: Cross-Validation

Lecture 10: Analyzing Learning Curves

Lecture 11: Grid Search vs Random Search

Instructors

Usama Albaghdady
Artificial Intelligence Engineer

Rating Distribution

1 stars: 0 votes

2 stars: 4 votes

3 stars: 4 votes

4 stars: 14 votes

5 stars: 4 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!