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Statistical Learning with Python

Statistical Learning with Python

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Module 1: Introduction to Statistical Learning

1

What is Statistical Learning?

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2

Differences between Statistics, Machine Learning, and Data Science

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3

Supervised vs Unsupervised Learning

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4

Applications in Real-World Problems

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5

Overview of Python for Statistical Learning (Anaconda, Jupyter Notebook)

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Module 2: Python for Data Analysis

6

Python Basics: Variables, Data Types, Loops, Functions

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7

NumPy: Arrays, Vectorized Operations, Linear Algebra Basics

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8

Pandas: DataFrames, Series, Indexing, Filtering, Grouping

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9

Data Cleaning & Preprocessing: Handling Missing Values, Duplicates

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10

Exploratory Data Analysis (EDA) with Pandas

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Module 3: Data Visualization

11

Matplotlib: Line Plots, Scatter Plots, Histograms, Subplots

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12

Seaborn: Heatmaps, Pairplots, Boxplots, Violin Plots

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13

Plotly / Interactive Visualizations

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14

Customizing Plots, Colors, Labels, and Legends

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Module 4: Probability & Statistical Foundations

15

Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation

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16

Probability Basics: Events, Conditional Probability, Bayes Theorem

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17

Probability Distributions: Normal, Binomial, Poisson, Exponential

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18

Sampling Techniques & Central Limit Theorem

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19

Correlation vs Covariance

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Module 5: Inferential Statistics

20

Hypothesis Testing: Null & Alternative Hypotheses

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21

t-Test, z-Test, Chi-Square Test

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22

ANOVA (Analysis of Variance)

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23

Confidence Intervals

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Module 6: Regression Analysis

24

Simple Linear Regression

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25

Multiple Linear Regression

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26

Assumptions of Regression Models

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27

Model Evaluation Metrics: R², Adjusted R², RMSE, MAE

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Module 7: Classification Techniques

28

Logistic Regression

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29

K-Nearest Neighbors (KNN)

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30

Support Vector Machines (SVM)

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31

Decision Trees & Random Forests

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Course details

Lectures 31

Level Intermediate

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