Machine Learning

Making Predictions Based on Data Analysis

4.6(79)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.

Learner reviews

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    Course content

    1

    Introduction to Machine Learning

    4 lectures
    1. Definition of Machine Learning and Its Applications9:33
    2. Machine Learning Classifications12:43
    3. Why Python Language?17:06
    4. Installing Anaconda2:09
    2

    Supervised Learning - Regression

    15 lectures
    1. Introduction to Supervised Learning3:41
    2. What is Regression?5:13
    3. Simple Linear Regression25:43
    4. Practical Example16:17
    5. 1FuelConsumptionCo2
    6. Model Evaluation7:06
    7. Model Performance Measurement10:12
    8. Practical Application of Simple Linear Regression33:24
    9. Multiple Linear Regression (Part One)6:19
    10. Multiple Linear Regression (Part Two)11:31
    11. Practical Application of Multiple Linear Regression27:31
    12. 1china_gdp
    13. Questions about Multiple Linear Regression9:03
    14. Non-Linear Regression (Part One)18:01
    15. Non-Linear Regression (Part Two)29:22
    3

    Supervised Learning - Classification

    17 lectures
    1. Introduction to Classification6:16
    2. KNN Nearest Neighbors Algorithm5:47
    3. Identifying and Calculating Values9:19
    4. Model Evaluation Metrics18:13
    5. Practical Application of the Nearest Neighbors Algorithm32:45
    6. 1teleCust1000t
    7. Decision Tree Algorithm7:28
    8. Building a Decision Tree18:37
    9. Practical Application of the Decision Tree25:26
    10. 1drug200
    11. Introduction to Logistic Regression9:53
    12. Understanding the Model Training Phase11:17
    13. Practical Application of the Decision Tree and Model Training20:54
    14. 1ChurnData
    15. SVM Algorithm13:50
    16. SVM Practical Application28:01
    17. 1cell_samples
    4

    Unsupervised Learning - Clustering

    10 lectures
    1. Introduction to Clustering7:07
    2. Difference Between Clustering and Classification10:41
    3. KM Algorithm12:47
    4. Measuring Algorithm Accuracy3:06
    5. Practical Application of the Algorithm19:58
    6. 1cust
    7. Introduction to Hierarchical Clustering9:09
    8. Hierarchical and K-Means Clustering4:43
    9. Agglomerative Practical Application of the Algorithm22:55
    10. 1cars_clus
    5

    Recommender System

    6 lectures
    1. Introduction to Recommendation Systems5:58
    2. Content-Based System11:46
    3. Practical Application of Content21:40
    4. Project Files
    5. Collaborative-Based System11:45
    6. Practical Application on Recommendation Systems

    Instructor

    Eng. Amr Abdel Fattah

    Eng. Amr Abdel Fattah

    Computer systems engineer specializing in mobile app and website development, with experience in creating popular platforms and web applications.
    7,145 students20 courses