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Categories Machine Learning

CDS Video Series | Section 04: Supervised and Unsupervised Learning

  • Familiarity with Python programming language.
  • If you're new to the field of machine learning, this course provides a structured path to understanding supervised and unsupervised learning.
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3,760.00

What I will learn?

  • Gain a solid grasp of the underlying concepts and methodologies behind both supervised and unsupervised learning approaches.
  • Learn to apply supervised learning methods to solve real-world problems.
  • Explore the realms of unsupervised learning through clustering methods, anomaly detection, and feature learning, uncovering patterns and insights within unstructured data.

Course Curriculum

Supervised and Unsupervised Learning: Video Lesson

  • Supervised and Unsupervised Learning: Intuition
    34:15
  • Supervised and Unsupervised Learning: Hands-on
    10:50
  • Simple Linear Regression: Intuition
    16:52
  • Simple Linear Regression: Hands-on
    13:04
  • Random Forest Regression: Intuition
    07:37
  • Random Forest Regression: Hands-on
    10:37
  • Decision Tree Regression: Intuition
    05:01
  • Decision Tree Regression: Hands-on
    07:03
  • AdaBoost: Intuition
    06:59
  • AdaBoost: Hands-on
    04:51
  • ARIMA: Intuition
    09:48
  • ARIMA: Hands-on
    07:57
  • Decision Tree Classification: Intuition
    07:40
  • Decision Tree Classification: Hands-on
    07:18
  • Exponential Smoothing: Intuition
    11:45
  • Exponential Smoothing: Hands-on
    12:02
  • Gradient Boosting: Intuition
    05:56
  • Gradient Boosting: Hands-on
    08:18
  • Hierarchical Clustering: Intuition
    05:39
  • Hierarchical Clustering: Hands-on
    05:14
  • KNN Regression: Intuition
    05:27
  • KNN Regression: Hands-on
    07:49
  • KNN Classification: Intuition
    07:29
  • KNN Classification: Hands-on
    06:28
  • KMeans: Intuition
    13:00
  • KMeans: Hands-on
    05:21
  • Lasso Ridge ElasticNet: Intuition
    10:55
  • Lasso Ridge ElasticNet: Hands-on
    14:01
  • Logistic Regression: Intuition
    08:56
  • Logistic Regression: Hands-on
    10:09
  • Multiple Linear Regression: Intuition
    08:43
  • Multiple Linear Regression: Hands-on
    10:54
  • Moving Average: Intuition
    08:17
  • Moving Average: Hands-on
    07:02
  • Naive Bayes Classification: Intuition
    07:52
  • Naive Bayes Classification: Hands-on
    05:43
  • Principal Component Analysis: Intuition
    04:53
  • Principal Component Analysis: Hands-on
    08:08
  • Polynomial Regression: Intuition
    04:03
  • Polynomial Regression: Hands-on
    05:20
  • Random Forest Classification: Intuition
    07:26
  • Random Forest Classification: Hands-on
    06:54
  • SARIMA: Intuition
    04:07
  • SARIMA: Hands-on
    05:03
  • Support Vector Machine Regression: Intuition
    04:28
  • Support Vector Machine Regression: Hands-on
    08:20
  • Support Vector Machine Classification: Intuition
    05:39
  • Support Vector Machine Classification: Hands-on
    07:58
  • XGBoost: Intuition
    05:40
  • XGBoost: Hands-on
    05:38

Supervised and Unsupervised Learning: Handout Notes

Links to the Python notebooks

Assessment

Testimonials

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Material Includes

  • Video Lessons
  • Handout Notes
  • Python Codes
  • MCQs

Requirements

  • Familiarity with Python programming language.
  • If you're new to the field of machine learning, this course provides a structured path to understanding supervised and unsupervised learning.

Who Should Take this course?

  • Beginners in Machine Learning

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