{"id":29802949,"url":"https://github.com/amirmardan/ml_course","last_synced_at":"2025-07-28T10:44:37.468Z","repository":{"id":184095441,"uuid":"468519270","full_name":"AmirMardan/ml_course","owner":"AmirMardan","description":"This repository belongs to the course of machine learning with Python which is getting ready for AUT ","archived":false,"fork":false,"pushed_at":"2023-03-22T03:02:12.000Z","size":19061,"stargazers_count":16,"open_issues_count":0,"forks_count":11,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-04-28T05:11:37.290Z","etag":null,"topics":["data-analysis-python","data-science","deep-learning","keras","machine-learning","python","pytorch","scikit-learn","tensorflow"],"latest_commit_sha":null,"homepage":"https://amirmardan.github.io/ml_course/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AmirMardan.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-03-10T21:42:38.000Z","updated_at":"2023-06-26T02:09:35.000Z","dependencies_parsed_at":"2023-07-27T03:45:05.354Z","dependency_job_id":null,"html_url":"https://github.com/AmirMardan/ml_course","commit_stats":null,"previous_names":["amirmardan/ml_course"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/AmirMardan/ml_course","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AmirMardan%2Fml_course","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AmirMardan%2Fml_course/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AmirMardan%2Fml_course/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AmirMardan%2Fml_course/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AmirMardan","download_url":"https://codeload.github.com/AmirMardan/ml_course/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AmirMardan%2Fml_course/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267504024,"owners_count":24098335,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-28T02:00:09.689Z","response_time":68,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis-python","data-science","deep-learning","keras","machine-learning","python","pytorch","scikit-learn","tensorflow"],"created_at":"2025-07-28T10:44:36.255Z","updated_at":"2025-07-28T10:44:37.454Z","avatar_url":"https://github.com/AmirMardan.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Welcom to Python and machine learning course\n\nThis 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.\n\n---\n**NOTE**\n\nI will push new contents weekly\n\n---\n## [1. Introduction to Python](https://github.com/AmirMardan/ml_course/blob/main/1_intro_to_python/0_intro_to_python.ipynb)\n#### 1.1 General programming\n- An introduction\n- Required tools\n- Variables and data types\n    - Numbers in Python\n    - Strings in Python\n    - Booleans in Python\n    - List in Python\n    - Dictionary in Python\n- Operators\n    - Comparison operators\n    - Logical operators\n    - Membership operators\n    - Bitwise operators\n- Control flow\n    - `if` statements\n    - `match` statements\n    - `for` statements\n    - `while` statements\n\n#### [1.2 Modular programming](https://github.com/AmirMardan/ml_course/blob/main/1_intro_to_python/1_modular_programming.ipynb)\n- Functions\n- `Lambda` functions\n- Built-in functions\n    - `map` function\n    - `filter` function\n    - `enumerate` function\n    - `zip` function\n- Classes / objects\n\n## [2. Introduction to NumPy](https://github.com/AmirMardan/ml_course/blob/main/2_numpy/0_intro_to_numpy.ipynb)\n- Creating a NumPy array\n    - Creating arrays from lists\n    - Special arrays\n- Attributes of arrays\n- Data Selection\n    - Array indexing\n    - Array slicing\n    - Array view vs copy\n    - Conditional selection\n- Array manipulation\n    - Shape of an array\n    - Joining arrays\n    - Splitting of arrays\n- Computation on NumPy arrays\n- Aggregations\n    - Summation\n    - Minimum and maximum\n    - Variance and standard deviation\n    - Mean and median\n    - Find index\n\n## 3. Data Manipulation with Pandas\n#### [3.1 Introduction to pandas](https://github.com/AmirMardan/ml_course/blob/main/3_pandas/0_intro_to_pandas.ipynb)\n- Introducing Pandas objects\n    - The pandas `Series` object\n    - The pandas `DataFrame` object\n- Data indexing and selection\n    - Data selection in Series\n    - Data selection in DataFrame\n- Handling missing data\n    - Detecting the missing values\n    - Dealing with missing values\n- IO in pandas\n\n#### [3.2 Data manipulation in using pandas](https://github.com/AmirMardan/ml_course/blob/main/3_pandas/1_data_manipulation_using_pandas.ipynb)\n- Basic operations in pandas\n- Combining datasets\n    - Concat\n    - Merge\n    - Join\n- Aggregation\n- `Groupby`\n- Vectorized string\n\n## 4 Visualization\n\n#### [4.1 Matplotlib](https://github.com/AmirMardan/ml_course/blob/main/4_visualization/0_matplotlib.ipynb)\n- Basic matplotlib\n    - Simple matplotlib\n    - Subplots\n    - Object-oriented method\n- Different types of plot\n    - Scatter plot\n    - Bar plot\n    - Histogram\n    - Pie chart\n    - Box Plot\n    - Violin plot\n- Images with matplotlib\n- Animation using matplotlib\n    - Live graph with matplotlib\n    \n#### [4.2 Seaborn](https://github.com/AmirMardan/ml_course/blob/main/4_visualization/1_seaborn.ipynb)\n- Relational plots\n- Distribution plots\n    - `displot`\n    - `jointplot`\n    - `pairplot`\n- Categorical plots\n    - Categorical scatter plots\n    - Categorical distribution plots\n    - Categorical estimate plots\n- Regression plots\n- FacetGrid\n- Customization\n    - Style and theme\n    - Colors\n\n## 5 Data Analysis and Processing\n\n#### [5.1 Exploratory data analysis (EDA)](https://github.com/AmirMardan/ml_course/blob/main/5_data_analysis_processing/0_introduction_to_EDA.ipynb)\n- Initial general assessment\n- Basic analysis\n- Missing data\n- Outliers\n- Correlation\n\n#### [5.2 Data preparation](https://github.com/AmirMardan/ml_course/blob/main/5_data_analysis_processing/1_intro_to_data_preparation.ipynb)\n\n#### [5.3 Data Cleaning](https://github.com/AmirMardan/ml_course/blob/main/5_data_analysis_processing/2_data_cleaning.ipynb)\n- Initial general assessment\n    - Rows with duplicated data\n    - Columns with a single value\n- Outliers\n    - Standard deviation method\n    - Interquartile range method\n- Missing data\n    - Remove rows with missing values\n    - Filling missing values\n\n#### [5.4 Data Transforms](https://github.com/AmirMardan/ml_course/blob/main/5_data_analysis_processing/3_data_transform.ipynb)\n- Scaling numerical data\n    - Data normalization\n    - Data standardization\n    - Robust scaling\n- Encode categorical data\n    - Ordinal Encoding\n    - One Hot Encoding\n    - Dummy Encoding\n- How to make distribution more Gaussian\n    - Box-Cox transform\n    - Yeo-Johnson transform\n    - Quantile transform\n\n## 6 Classical Machine Learning\n\n#### [6.1 Introduction to Machine Learning](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/0_Intro_to_ML.md)\n\n#### [6.2 Introduction to Scikit-Learn](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/1_intro_to_sklearn.ipynb)\n- Data presentation\n- Models in Scikit-learn\n    - Simple linear regression example\n    - Simple classification example\n    - Simple dimensionality reduction example\n    - Simple clustering example\n- Hyperparameters and model validation\n    - Cross validation\n    - Finding the best model\n    - Grid Search\n\n#### [6.3 Regression 1](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/2_regression_1.ipynb)\n- Ordinary Linear Regression\n- Linear Regression With Regularization\n    - Ridge Regularization\n    - Lasso Regularization\n    - Combined Regularization\n- A Linear Regression Project\n    - Exploratory Data Analysis\n    - Data Cleaning\n    - Data Processing Pipeline\n- Training and Evaluation\n    - Training Curve\n\n#### [6.4 Classification 1](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/3_classification_1.ipynb)\n- Logistic Regression\n- Support Vector Machine\n- Random Forest Classifier\n\n#### [6.5 Clustering 1](https://github.com/AmirMardan/ml_course/blob/main/6_classical_machine_learning/4_clustering_1.ipynb)\n- k-Means Clustering\n- Gaussian Mixture Models\n- Evaluation Clustering Models\n\n## 7. Fully Connected Neural Networks (FCNNs)\n\n#### [7.1 Introduction to TensorFlow](https://github.com/AmirMardan/ml_course/blob/main/7_fully_connected_nn/0_intro_to_tensorflow.ipynb)\n- Graph and Session\n    - Build and Perform a Graph\n    - Gradient in TensorFlow\n- Tensor types in TensorFlow\n    - Constant\n    - Variable\n- Tensor Manipulation\n    - Creating A Tensors\n    - Creating Special Tensors\n    - Shape Manipulation\n    - Slicing\n- Operators\n    - Basic Arithmetic Operators\n    - Comparison Operators\n    - Logical And Bitwise Operators\n\n#### [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)\n- Neural Network From Scratch\n- Neural Network With TensorFlow\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirmardan%2Fml_course","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famirmardan%2Fml_course","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirmardan%2Fml_course/lists"}