{"id":14958241,"url":"https://github.com/devamoghs/machine-learning-with-python","last_synced_at":"2025-05-14T19:04:32.499Z","repository":{"id":34066163,"uuid":"123592053","full_name":"devAmoghS/Machine-Learning-with-Python","owner":"devAmoghS","description":"Small scale machine learning projects to understand the core concepts . Give a Star 🌟If it helps you. BONUS: Interview Bank coming up..!","archived":false,"fork":false,"pushed_at":"2025-04-01T02:30:06.000Z","size":873,"stargazers_count":1187,"open_issues_count":0,"forks_count":183,"subscribers_count":44,"default_branch":"master","last_synced_at":"2025-04-08T11:11:54.098Z","etag":null,"topics":["beginner-friendly","data-science","deep-learning","exercises","machine-learning","practice-project","python","python-3","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Python","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/devAmoghS.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-03-02T14:54:11.000Z","updated_at":"2025-04-07T11:01:43.000Z","dependencies_parsed_at":"2023-01-15T04:20:22.898Z","dependency_job_id":"edb3b1e0-95f7-4dc8-b8ef-c0314afa7173","html_url":"https://github.com/devAmoghS/Machine-Learning-with-Python","commit_stats":{"total_commits":150,"total_committers":5,"mean_commits":30.0,"dds":"0.21333333333333337","last_synced_commit":"9381d69f380891c0d1be4614b71341484f4ca121"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devAmoghS%2FMachine-Learning-with-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devAmoghS%2FMachine-Learning-with-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devAmoghS%2FMachine-Learning-with-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/devAmoghS%2FMachine-Learning-with-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/devAmoghS","download_url":"https://codeload.github.com/devAmoghS/Machine-Learning-with-Python/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247829511,"owners_count":21002997,"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","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":["beginner-friendly","data-science","deep-learning","exercises","machine-learning","practice-project","python","python-3","scikit-learn"],"created_at":"2024-09-24T13:16:35.065Z","updated_at":"2025-04-08T11:11:59.427Z","avatar_url":"https://github.com/devAmoghS.png","language":"Python","readme":"# Machine-Learning-with-Python ![GitHub stars](https://img.shields.io/github/stars/devAmoghS/Machine-Learning-with-Python?style=for-the-badge)  ![GitHub forks](https://img.shields.io/github/forks/devAmoghS/Machine-Learning-with-Python?label=Forks\u0026style=for-the-badge)\n\n## Star History\n\n[![Star History Chart](https://api.star-history.com/svg?repos=devAmoghS/Machine-Learning-with-Python\u0026type=Date)](https://star-history.com/#devAmoghS/Machine-Learning-with-Python\u0026Date)\n\n\n![alt text](https://media.istockphoto.com/vectors/machine-learning-3-step-infographic-artificial-intelligence-machine-vector-id962219860?k=6\u0026m=962219860\u0026s=612x612\u0026w=0\u0026h=yricYyUqZbILMHp3IvtenS3xbRDhu1w1u5kk2az5tbo=)\n\n## Small scale machine learning projects to understand the core concepts (order: oldest to newest)\n* Topic Modelling using **Latent Dirichlet Allocation** with newsgroups20 dataset, implemented with Python and Scikit-Learn\n* Implemented a simple **neural network** built with Keras on MNIST dataset\n* Stock Price Forecasting on Google using **Linear Regression**\n* Implemented a simple a **social network** to learn basics of Python\n* Implemented **Naives Bayes Classifier** to filter spam messages on SpamAssasin Public Corpus\n* **Churn Prediction Model** for banking dataset using Keras and Scikit-Learn\n* Implemented **Random Forest** from scratch and built a classifier on Sonar dataset from UCI repository\n* Simple Linear Regression in Python on sample dataset\n* **Multiple Regression** in Python on sample dataset\n* **PCA and scaling** sample stock data in Python [working_with_data]\n* **Decision Trees** in Python on sample dataset\n* **Logistic Regression** in Python on sample dataset\n* Built a neural network in Python to defeat a captcha system\n* Helper methods include commom operations used in **Statistics, Probability, Linear Algebra and Data Analysis**\n* **K-means clustering** with example data; **clustering colors** with k-means; **Bottom-up Hierarchical Clustering**\n* Generating Word Clouds\n* Sentence generation using n-grams\n* Sentence generation using **Grammars and Automata Theory; Gibbs Sampling** \n* Topic Modelling using Latent Dirichlet Analysis (LDA)\n* Wrapper for using Scikit-Learn's **GridSearchCV** for a **Keras Neural Network**\n* **Recommender system** using **cosine similarity**, recommending new interests to users as well as matching users as per common interests\n* Implementing different methods for **network analysis** such as **PageRank, Betweeness Centrality, Closeness Centrality, EigenVector Centrality**\n* Implementing methods used for **Hypothesis Inference** such as **P-hacking, A/B Testing, Bayesian Inference**\n* Implemented **K-nearest neigbors** for next presedential election and prediciting voting behavior based on nearest neigbors.\n\n## Installation notes\nMLwP is built using Python 3.5.  The easiest way to set up a compatible\nenvironment is to use [Conda](https://conda.io/).  This will set up a virtual\nenvironment with the exact version of Python used for development along with all the\ndependencies needed to run MLwP.\n\n1.  [Download and install Conda](https://conda.io/docs/download.html).\n2.  Create a Conda environment with Python 3. \n\n(**Note**: enter ```cd ~``` to go on **$HOME** , then perform these commands)\n\n    ```\n    conda create --name *your env name* python=3.5\n    ```\n   \n   You will get the following, mlwp-test is the env name used in this example\n   \n   ```\n   Solving environment: done\n   \n## Package Plan ##\n\n  environment location: /home/user/anaconda3/envs/mlwp-test\n\n  added / updated specs: \n    - python=3.5\n\n\nThe following NEW packages will be INSTALLED:\n\n    ca-certificates: 2018.12.5-0            \n    certifi:         2018.8.24-py35_1       \n    libedit:         3.1.20181209-hc058e9b_0\n    libffi:          3.2.1-hd88cf55_4       \n    libgcc-ng:       8.2.0-hdf63c60_1       \n    libstdcxx-ng:    8.2.0-hdf63c60_1       \n    ncurses:         6.1-he6710b0_1         \n    openssl:         1.0.2p-h14c3975_0      \n    pip:             10.0.1-py35_0          \n    python:          3.5.6-hc3d631a_0       \n    readline:        7.0-h7b6447c_5         \n    setuptools:      40.2.0-py35_0          \n    sqlite:          3.26.0-h7b6447c_0      \n    tk:              8.6.8-hbc83047_0       \n    wheel:           0.31.1-py35_0          \n    xz:              5.2.4-h14c3975_4       \n    zlib:            1.2.11-h7b6447c_3      \n\nProceed ([y]/n)?  *Press y*\n\nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n#\n# To activate this environment, use:\n# \u003e source activate mlwp-test\n#\n# To deactivate an active environment, use:\n# \u003e source deactivate\n#\n\n   ```\n   The environment is successfully created.\n\n3.  Now activate the Conda environment.\n\n    ```\n    source activate *your env name*\n    ```\n    You will get the following\n    \n    ```\n    (mlwp-test) amogh@hp15X34:~$ \n    ```\n    Enter `conda list` to get the list of available packages\n    \n    ```\n        (mlwp-test) amogh@hp15X34:~$ conda list\n    # packages in environment at /home/amogh/anaconda3/envs/mlwp-test:\n    #\n    # Name                    Version                   Build  Channel\n    ca-certificates           2018.12.5                     0  \n    certifi                   2018.8.24                py35_1  \n    libedit                   3.1.20181209         hc058e9b_0  \n    libffi                    3.2.1                hd88cf55_4  \n    libgcc-ng                 8.2.0                hdf63c60_1  \n    libstdcxx-ng              8.2.0                hdf63c60_1  \n    ncurses                   6.1                  he6710b0_1  \n    openssl                   1.0.2p               h14c3975_0  \n    pip                       10.0.1                   py35_0  \n    python                    3.5.6                hc3d631a_0  \n    readline                  7.0                  h7b6447c_5  \n    setuptools                40.2.0                   py35_0  \n    sqlite                    3.26.0               h7b6447c_0  \n    tk                        8.6.8                hbc83047_0  \n    wheel                     0.31.1                   py35_0  \n    xz                        5.2.4                h14c3975_4  \n    zlib                      1.2.11               h7b6447c_3 \n    ```\n\n4.  Install the required dependencies.\n\n    ```\n    (mlwp-test) amogh@hp15X34:~$ conda install --yes --file *path to requirements.txt*\n    ```\n    \n5. In case you are not able to install the packages or getting `PackagesNotFoundError`\nUse the following command ` conda install -c conda-forge *list of packages separated by space*`. For more info, refer issue [#3](https://github.com/devAmoghS/Machine-Learning-with-Python/issues/3) **Unable to install requirements**\n\n\n## How good is the code ?\n* It is well tested\n* It passes style checks (PEP8 compliant)\n* It can compile in its current state (and there are relatively no issues)\n\n## How much support is available?\n* FAQs (coming soon)\n* Documentation (coming soon)\n\n## Issues\nFeel free to submit issues and enhancement requests.\n\n## Contributing\nPlease refer to each project's style guidelines and guidelines for submitting patches and additions. In general, we follow the \"fork-and-pull\" Git workflow.\n\n 1. **Fork** the repo on GitHub\n 2. **Clone** the project to your own machine\n 3. **Commit** changes to your own branch\n 4. **Push** your work back up to your fork\n 5. Submit a **Pull request** so that we can review your changes\n\nNOTE: Be sure to merge the latest from \"upstream\" before making a pull request!\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevamoghs%2Fmachine-learning-with-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdevamoghs%2Fmachine-learning-with-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevamoghs%2Fmachine-learning-with-python/lists"}