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Udemy - Key Concepts in Analytics, ML and AI Made Easy

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Added 1 day ago by freecoursewb in Other
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Udemy - Key Concepts in Analytics, ML and AI Made Easy (Size: 589.6 MB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  1 - How Handle Data Imbalance
  1 - What is Undersampling.mp4 32 MB
  2 - Underfitting & Overfitting
  3 - Data Engineering Basics
  4 - Training and Testing of a Model
  10 - Cross Validation.mp4 24.5 MB
  11 - Why not test on Training Data.mp4 27.8 MB
  5 - Some Key Statistical concepts useful everywhere
  12 - Correlation & Causation.mp4 35.9 MB
  13 - What is A B Testing.mp4 30.4 MB
  14 - What is 95% Confidence Interval.mp4 36.8 MB
  15 - What does P-Value really tell us.mp4 36 MB
  16 - Magic of Central Limit Theorem.mp4 24.4 MB
  17 - Regression v s Classification.mp4 26.3 MB
  6 - Let's discuss Accuracy of the Model
  18 - What is Confusion Matrix.mp4 35.7 MB
  19 - Precision or Recall, What is more important.mp4 25.4 MB
  7 - Churn Prediction
  20 - How do Companies know that a customer is about to leave them.mp4 27.4 MB
  5 - How to Handle Missing Data.mp4 28.9 MB
  6 - What is Data Scaling.mp4 29.7 MB
  7 - What is Standardization.mp4 30.2 MB
  8 - What is Normalization.mp4 25.2 MB
  9 - What is Feature Engineering.mp4 23 MB
  4 - What is Underfitting & Overfitting.mp4 24.7 MB
  2 - What is Oversampling.mp4 26.8 MB
  3 - What is SMOTE.mp4 38.5 MB

Description


Key Concepts in Analytics, ML & AI Made Easy
https://WebToolTip.com
Published 7/2026

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 1h 0m | Size: 589.63 MB
Master interview-ready concepts, business applications and model evaluation without coding or complex mathematics.
What you'll learn

Build a strong foundation in data preprocessing, model training, evaluation, and handling common machine learning challenges.

Master essential statistical concepts such as Correlation, A/B Testing, P-value, Confidence Intervals, and the Central Limit Theorem.

Interpret model performance using Accuracy, Confusion Matrix, Precision, Recall, and choose the right evaluation metric for different situations.

Prepare confidently for Analytics, Data Science, and Machine Learning interviews by understanding the reasoning behind commonly asked concepts.
Requirements

No programming knowledge required.

No prior experience in Machine Learning is needed.

No advanced mathematics or statistics background required.

Just curiosity and a willingness to understand how Analytics and AI concepts work.

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