https://github.com/amirmardan/ml_course

This repository belongs to the course of machine learning with Python which is getting ready for AUT
https://github.com/amirmardan/ml_course

data-analysis-python data-science deep-learning keras machine-learning python pytorch scikit-learn tensorflow

Last synced: 5 months ago
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This repository belongs to the course of machine learning with Python which is getting ready for AUT

• Host: GitHub
• URL: https://github.com/amirmardan/ml_course
• Owner: AmirMardan
• License: mit
• Created: 2022-03-10T21:42:38.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2023-03-22T03:02:12.000Z (over 2 years ago)
• Last Synced: 2024-04-28T05:11:37.290Z (over 1 year ago)
• Topics: data-analysis-python, data-science, deep-learning, keras, machine-learning, python, pytorch, scikit-learn, tensorflow
• Language: Jupyter Notebook
• Homepage: https://amirmardan.github.io/ml_course/
• Size: 18.2 MB
• Stars: 16
• Watchers: 1
• Forks: 11
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
## Welcom to Python and machine learning course

This repository is created by [Amir Mardan](https://amirmardan.github.io/) to maintain and preview the contents for a Python and machine learning course prepared for Amirkabir University of Technology, Tehran, Iran. Please contact me via my email (mardan.amir.h@gmail.com) for your lovely feedback and suggestions.

---

**NOTE**

I will push new contents weekly

---

## [1. Introduction to Python](https://github.com/AmirMardan/ml_course/blob/main/1_intro_to_python/0_intro_to_python.ipynb)

#### 1.1 General programming

- An introduction

- Required tools

- Variables and data types

    - Numbers in Python

    - Strings in Python

    - Booleans in Python

    - List in Python

    - Dictionary in Python

- Operators

    - Comparison operators

    - Logical operators

    - Membership operators

    - Bitwise operators

- Control flow

    - `if` statements

    - `match` statements

    - `for` statements

    - `while` statements

#### [1.2 Modular programming](https://github.com/AmirMardan/ml_course/blob/main/1_intro_to_python/1_modular_programming.ipynb)

- Functions

- `Lambda` functions

- Built-in functions

    - `map` function

    - `filter` function

    - `enumerate` function

    - `zip` function

- Classes / objects

## [2. Introduction to NumPy](https://github.com/AmirMardan/ml_course/blob/main/2_numpy/0_intro_to_numpy.ipynb)

- Creating a NumPy array

    - Creating arrays from lists

    - Special arrays

- Attributes of arrays

- Data Selection

    - Array indexing

    - Array slicing

    - Array view vs copy

    - Conditional selection

- Array manipulation

    - Shape of an array

    - Joining arrays

    - Splitting of arrays

- Computation on NumPy arrays

- Aggregations

    - Summation

    - Minimum and maximum

    - Variance and standard deviation

    - Mean and median

    - Find index

## 3. Data Manipulation with Pandas

#### [3.1 Introduction to pandas](https://github.com/AmirMardan/ml_course/blob/main/3_pandas/0_intro_to_pandas.ipynb)

- Introducing Pandas objects

    - The pandas `Series` object

    - The pandas `DataFrame` object

- Data indexing and selection

    - Data selection in Series

    - Data selection in DataFrame

- Handling missing data

    - Detecting the missing values

    - Dealing with missing values

- IO in pandas

#### [3.2 Data manipulation in using pandas](https://github.com/AmirMardan/ml_course/blob/main/3_pandas/1_data_manipulation_using_pandas.ipynb)

- Basic operations in pandas

- Combining datasets

    - Concat

    - Merge

    - Join

- Aggregation

- `Groupby`

- Vectorized string

## 4 Visualization

#### [4.1 Matplotlib](https://github.com/AmirMardan/ml_course/blob/main/4_visualization/0_matplotlib.ipynb)

- Basic matplotlib

    - Simple matplotlib

    - Subplots

    - Object-oriented method

- Different types of plot

    - Scatter plot

    - Bar plot

    - Histogram

    - Pie chart

    - Box Plot

    - Violin plot

- Images with matplotlib

- Animation using matplotlib

    - Live graph with matplotlib

    

#### [4.2 Seaborn](https://github.com/AmirMardan/ml_course/blob/main/4_visualization/1_seaborn.ipynb)

- Relational plots

- Distribution plots

    - `displot`

    - `jointplot`

    - `pairplot`

- Categorical plots

    - Categorical scatter plots

    - Categorical distribution plots

    - Categorical estimate plots

- Regression plots

- FacetGrid

- Customization

    - Style and theme

    - Colors

## 5 Data Analysis and Processing

#### [5.1 Exploratory data analysis (EDA)](https://github.com/AmirMardan/ml_course/blob/main/5_data_analysis_processing/0_introduction_to_EDA.ipynb)

- Initial general assessment

- Basic analysis

- Missing data

- Outliers

- Correlation

#### [5.2 Data preparation](https://github.com/AmirMardan/ml_course/blob/main/5_data_analysis_processing/1_intro_to_data_preparation.ipynb)

#### [5.3 Data Cleaning](https://github.com/AmirMardan/ml_course/blob/main/5_data_analysis_processing/2_data_cleaning.ipynb)

- Initial general assessment

    - Rows with duplicated data

    - Columns with a single value

- Outliers

    - Standard deviation method

    - Interquartile range method

- Missing data

    - Remove rows with missing values

    - Filling missing values

#### [5.4 Data Transforms](https://github.com/AmirMardan/ml_course/blob/main/5_data_analysis_processing/3_data_transform.ipynb)

- Scaling numerical data

    - Data normalization

    - Data standardization

    - Robust scaling

- Encode categorical data

    - Ordinal Encoding

    - One Hot Encoding

    - Dummy Encoding

- How to make distribution more Gaussian

    - Box-Cox transform

    - Yeo-Johnson transform

    - Quantile transform

## 6 Classical Machine Learning

#### [6.1 Introduction to Machine Learning](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/0_Intro_to_ML.md)

#### [6.2 Introduction to Scikit-Learn](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/1_intro_to_sklearn.ipynb)

- Data presentation

- Models in Scikit-learn

    - Simple linear regression example

    - Simple classification example

    - Simple dimensionality reduction example

    - Simple clustering example

- Hyperparameters and model validation

    - Cross validation

    - Finding the best model

    - Grid Search

#### [6.3 Regression 1](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/2_regression_1.ipynb)

- Ordinary Linear Regression

- Linear Regression With Regularization

    - Ridge Regularization

    - Lasso Regularization

    - Combined Regularization

- A Linear Regression Project

    - Exploratory Data Analysis

    - Data Cleaning

    - Data Processing Pipeline

- Training and Evaluation

    - Training Curve

#### [6.4 Classification 1](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/3_classification_1.ipynb)

- Logistic Regression

- Support Vector Machine

- Random Forest Classifier

#### [6.5 Clustering 1](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/4_clustering_1.ipynb)

- k-Means Clustering

- Gaussian Mixture Models

- Evaluation Clustering Models

## 7. Fully Connected Neural Networks (FCNNs)

#### [7.1 Introduction to TensorFlow](https://github.com/AmirMardan/ml_course/blob/main/7_fully_connected_nn/0_intro_to_tensorflow.ipynb)

- Graph and Session

    - Build and Perform a Graph

    - Gradient in TensorFlow

- Tensor types in TensorFlow

    - Constant

    - Variable

- Tensor Manipulation

    - Creating A Tensors

    - Creating Special Tensors

    - Shape Manipulation

    - Slicing

- Operators

    - Basic Arithmetic Operators

    - Comparison Operators

    - Logical And Bitwise Operators

#### [7.2 Introduction To Fully Connected Neural Networks](https://github.com/AmirMardan/ml_course/blob/main/7_fully_connected_nn/1_intro_to_NN.ipynb)

- Neural Network From Scratch

- Neural Network With TensorFlow