Machine Learning
Making Predictions Based on Data Analysis
9h 54m52 lectures5 sections
What you'll learn
- Learn the basics of machine learning
- Understand regression techniques
- Design recommendation systems
- Analyze data to extract patterns
- Apply practical projects
- Use Python for predictions
About this course
Start your journey in machine learning with this comprehensive course covering all essential and advanced aspects. You will learn how to build machine learning models, apply regression, classification, and clustering techniques, and create recommendation systems. The course is designed to equip you with practical skills through projects and step-by-step explained code.
Expected outcomes
- Ability to build machine learning models using modern tools and techniques.
- Understanding how to apply supervised and unsupervised learning algorithms.
- Ability to analyze data and use it to improve performance and predictions.
- Creating customized recommendation systems for various practical applications.
- Developing practical projects that make machine learning a part of your professional life.
Course content
1Introduction to Machine Learning
4 lectures
2Supervised Learning - Regression
15 lectures
- Introduction to Supervised Learning3:41
- What is Regression?5:13
- Simple Linear Regression25:43
- Practical Example16:17
- 1FuelConsumptionCo2
- Model Evaluation7:06
- Model Performance Measurement10:12
- Practical Application of Simple Linear Regression33:24
- Multiple Linear Regression (Part One)6:19
- Multiple Linear Regression (Part Two)11:31
- Practical Application of Multiple Linear Regression27:31
- 1china_gdp
- Questions about Multiple Linear Regression9:03
- Non-Linear Regression (Part One)18:01
- Non-Linear Regression (Part Two)29:22
3Supervised Learning - Classification
17 lectures
- Introduction to Classification6:16
- KNN Nearest Neighbors Algorithm5:47
- Identifying and Calculating Values9:19
- Model Evaluation Metrics18:13
- Practical Application of the Nearest Neighbors Algorithm32:45
- 1teleCust1000t
- Decision Tree Algorithm7:28
- Building a Decision Tree18:37
- Practical Application of the Decision Tree25:26
- 1drug200
- Introduction to Logistic Regression9:53
- Understanding the Model Training Phase11:17
- Practical Application of the Decision Tree and Model Training20:54
- 1ChurnData
- SVM Algorithm13:50
- SVM Practical Application28:01
- 1cell_samples
4Unsupervised Learning - Clustering
10 lectures
- Introduction to Clustering7:07
- Difference Between Clustering and Classification10:41
- KM Algorithm12:47
- Measuring Algorithm Accuracy3:06
- Practical Application of the Algorithm19:58
- 1cust
- Introduction to Hierarchical Clustering9:09
- Hierarchical and K-Means Clustering4:43
- Agglomerative Practical Application of the Algorithm22:55
- 1cars_clus
5Recommender System
6 lectures
- Introduction to Recommendation Systems5:58
- Content-Based System11:46
- Practical Application of Content21:40
- Project Files
- Collaborative-Based System11:45
- Practical Application on Recommendation Systems
Instructor
Eng. Amr Abdel Fattah
6,563 students20 courses
This course is part of the diploma
