All Machine Learning Concepts Explained in 22 Minutes

All Basic Machine Learning Terms Explained in 22 Minutes

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To get you out of your confusion with all the Machine Learning Vocabulary, here an overview of all basic terms you will encounter as you start your Journey in Machine Learning and Data Science.

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================== Timestamps ================
00:04 - Artificial Intelligence (AI)
00:37 - Machine Learning
01:30 - Algorithm
02:06 - Data
02:48 - Model
03:30 - Model fitting
03:44 - Training Data
04:17 - Test Data
04:54 - Supervised Learning
05:24 - Unsupervised Learning
06:01 - Reinforcement Learning
07:05 - Feature (Input, Independent Variable, Predictor)
07:45 - Feature engineering
08:15 - Feature Scaling (Normalization, Standardization)
08:48 - Dimensionality
09:34 - Target (Output, Label, Dependent Variable)
09:59 - Instance (Example, Observation, Sample)
10:32 - Label (class, target value)
11:16 - Model complexity
12:15 - Bias & Variance
13:23 - Bias Variance Tradeoff
14:11 - Noise
14:30 - Overfitting & Underfitting
15:20 - Validation & Cross Validation
16:20 - Regularization
16:40 - Batch, Epoch, Iteration
17:40 - Parameter
18:22 - Hyperparameter
18:50 - Cost Function (Loss Function, Objective Function)
19:39 - Gradient Descent
20:49 - Learning Rate
21:28 - Evaluation