AI

CS-Introduction to Artificial Intelligence

• Lesson_01_Decoding_Artificial_Intelligence

  24:13Preview

  • 01 Course Introduction 1

    03:04

  • 01 Decoding Artificial Intelligence 1

    00:19

  • 02 Meaning Scope and Stages Of Artificia

    00:46

  • 03 Three Stages of AI 1

    02:19

  • 04 Applications of Artificial Intelligence 1

    01:06

  • 05 Image Recognition 1

    01:26

  • 06 Applications of AI Examples 1

    01:06

  • 07 Effects of Artificial Intelligence on Socie

    03:54

  • 08 Supervises Learning for Telemedicine 1

    03:10

  • 09 Solves Complex Social Problems 1

    03:17

  • 10 Benefits Multiple Insustries 1

    02:39

  • 11 Key Takeaways 1

    01:07

• Lesson_02_Fundamentals_of_Machine_Learning_and_Deep_Learning

  31:02Preview

  • 01 Fundamental of Machi

    00:50

  • 02 Meaning of Machine Learning.mp4 1

    02:22

  • 03 Relationship between ML and SA.mp4

    01:31

  • 04 Process of Machine Learning.mp4 1

    01:32

  • 05 Types of Machine Learning.mp4 1

    01:34

  • 06 Meaning of Unsupervised Learning.mp4

    01:24

  • 07 Meaning of Semi-supervised Learning.mp4

    01:59

  • 08 Algorithms of Machine Learning.mp4

    01:00

  • 09 Regression.mp4

    03:12

  • 10 Naive Bayes.mp4

    01:09

  • 11 Naive Bayes Classification.mp4

    02:45

  • 12 Machine Learning Algorithms.mp4

    02:22

  • 13 Deep Learning.mp4

    02:45

  • 14 Artificial Neural Network Definition.mp4 1

    01:48

  • 15 Definition of Perceptron.mp4

    01:18

  • 16 Online and Batch Learning.mp4

    02:17

  • 17 Key Takeaways.mp4

    01:14

• Lesson_03_Machine_Learning_Workflow

  14:52Preview

  • 01 Learning Objective 1

    00:28

  • 02 Machine Learning Workflow

    01:06

  • 03 Step-1.Get more data

    00:41

  • 04 Step-2.Ask a Sharp Question

    02:07

  • 05 step-3 Add Data to the Table.mp4

    03:20

  • 06 Step-4.Check for Quality

    01:15

  • 07 Step-5.Transform Features

    02:41

  • 11 Key takeaways

    00:54

  • Step 6

    01:49

  • Step 7

    00:31

• Lesson_04_Performance_Metrics

  19:39Preview

  • 01 Performance Metrics

    00:31

  • 02 Need For Performance Metrics

    01:10

  • 03 Key Method Of Performance Metrics

    00:53

  • 04 Confusion Matrix Example

    01:15

  • 05 Terms Of Confusion Metrics

    01:53

  • 06 Minimize False Cases

    01:37

  • 07 Minimize False Positive Example

    01:07

  • 08 Accuracy

    01:42

  • 09 Precision

    01:13

  • 10 Recall Or Sensitivity

    02:32

  • 11 Specificity

    01:36

  • 12 F1 Score

    03:01

  • 13 Key takeaways

    01:09