Course curriculum
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01 - intro of, course instructor and AI sciences
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04 - course overview
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03 - Past, present and future of ML
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02 - Motivation for course
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06 - Outliars
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03 - Dataset
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09 - Accuracy, error
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14 - Recap,flow of ML project
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13 - Clustring
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11 - types of ML
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01 - intro to ML
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02 - Kids vs computer learning
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04 - Labels, Features
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05 - Train test split
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07 - Model, training
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10 - Formates of data
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12 - classification, regression
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08 - over fitting, under fitting
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01 - Hello World
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02 - Intro to data types
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05 - Tuples
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03 - Numbers
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08 - Sets
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09 - Comparison operators
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10 - Logical oprator,user input, game
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11 - Decision making(if,elif,else)
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12 - Decision making(nested if)
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13 - Better coding practice, completing the game
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07 - dictionaries
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06 - Lists
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15 - while loop
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14 - For loop
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04 - Strings
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16 - Simple functions
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17 - Boolian and value returning Function
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18 - Calculator project
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06 - pandas(2)
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09 - Matplotlib(2)
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01 - Intro to LR and motivation
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15 - Decision Boundary
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04 - numpy
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03 - Intro to final project
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10 - Dealing with missing values
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05 - pandas(1)
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02 - pros cons
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14 - Sigmoid function
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07 - Reading and manipulating dataset
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18 - LR from scratch(1)
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11 - outlier removal
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12 - Categorical to numric
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08 - Matplotlib(1)
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21 - LR from scratch(4)
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13 - Quick implementation of logistic regression
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16 - Cost function
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24 - binary to multiclass
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17 - Gradient decent
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25 - Concluding remarks
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20 - LR from scratch(3)
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19 - LR from scratch(2)
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23 - LR from scratch(6)
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22 - LR from scratch(5)
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About this course
- $199.99
- 61 lessons
- 8.5 hours of video content