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Practical Machine Learning

The Practical Machine Learning Course focuses on equipping learners with the essential skills and tools needed to apply machine learning techniques in real-world scenarios. The course covers fundamental concepts, practical methodologies, and industry best practices to help you build and deploy machine learning models effectively.

Throughout the course, you will gain insights into various machine learning algorithms, data preprocessing techniques, feature engineering, model evaluation, and deployment strategies. The course includes a combination of theoretical explanations, code examples, and hands-on projects to reinforce your understanding.

Overview

Provide a brief overview of the course and its goals. What will students learn?

Table of Contents

Course Structure

1. [Introduction]

2. [30 Pandas Tricks]

3. [20 Scikit-learn Tricks]

4. [Preprocessing]

  • [Missing Values]
  • [Outlier Handling]
  • [Scaling Dataset]
  • [Imbalanced Dataset]

5. [Visualization]

6. [Useful Packages]

  • [Huggingface]
  • [SHAP]
  • [Mito]
  • [Pycaret]
  • [Lazypredict]
  • [TQDM]
  • [Vaex]
  • [Pyspark]
  • [WandB]

7. [Making WebApps - low code]

  • [Streamlit]
  • [Gradio]

8. [1st Project (House Price Prediction - Divar.ir)]

9. [2nd Project (Customer Churn Prediction)]

10. [3rd Project (Salary Prediction Stackoverflow)]

11. [4th Project (Fraud Detection - Etherium)]

12. [5th Project (Nvidia Stock & Tweet's Sentiment)]

13. [6th Project (Recommender System with Word2Vec)]

14. [7th Project (Traffic Sign Recognition - GTSRB)]

15. [8th Project (Apple Stock Prediction using LSTM)]

16. [9th Project (Speech mini Course - Urbansound 8K)]

17. [10th Project (Energy Consumption Prediction)]

Usage

To make the most out of this course, it is recommended to follow the modules in sequential order. Each module contains Jupyter Notebook files (.ipynb) that you can open and run in your preferred environment. Feel free to explore the code examples, experiment with different parameters, and apply the techniques to your own datasets. The course notebooks are designed to be self-explanatory, with comments and explanations provided at each step.

Contributing

[Contributions to the Practical Machine Learning Course are welcome! If you find any issues, errors, or have suggestions for improvements, please open an issue or submit a pull request. Your contributions can help enhance the learning experience for others.

About Me

I am a passionate and experienced professional in the field of artificial intelligence (A.I). I am currently pursuing my Ph.D. in A.I., focusing on cutting-edge research and applications in machine learning and speech technologies. With a deep understanding of the underlying principles of machine learning, I have dedicated my career to advancing the field and promoting practical applications of A.I. My expertise lies in developing innovative machine learning models and algorithms to solve complex problems and improve decision-making processes. In addition to his academic pursuits, I serve as the CEO of Saean Ertebat, a leading technology company specializing in A.I. solutions and services. with the help of Saean Ertebat, we have successfully delivered state-of-the-art A.I. products to clients across various industries, empowering them to leverage the power of machine learning and automate their processes. I am a firm believer in the democratization of knowledge and strives to make A.I. accessible to a wide audience. I have conducted numerous workshops, training programs, and online courses to educate and empower individuals in practical machine learning techniques. My teaching approach emphasizes hands-on experience and real-world applications to ensure learners acquire practical skills. As an avid researcher and practitioner, I remain actively involved in the A.I. community. He regularly contributes to conferences, journals, and open-source projects, sharing his insights and collaborating with fellow experts in the field. Connect with me on LinkedIn for updates on his latest projects and research endeavors.

Additional Resources

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