Course Content
1. Introduction to Python, Installation and Setup
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2. Numbers and Strings
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3. List Dictionaries, Tuple sets and Booleans
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4. Statements in Python (if else, for and while loop)
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5. Useful Operators and Functions
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6. More About Function, Modules and Packages
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7. Errors and Exception Handling
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8. What is Machine Learning
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9. Types of ML Algorithm
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10. Classification vs Regression
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11. Preprocessing in Theory
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12. Numpy
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13. Pandas
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14. Matplotlib (Visualization)
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15. Exploratory Data Analysis and Preprocessing
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16. Linear Regression From Scratch
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17. Polynomial Regression and Multiple Linear Regression
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18. Intro to classification and KNN Model
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19. Logistic Regression and Naive Byes
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20. Decision Tree, SVM and Random Forest(Adult Data)
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