# a weekly routine to improve my python programming skills, especially for machine learning and artificial intelligence. from a senior machine learning mentor

By [shebin](https://paragraph.com/@znyder) · 2023-03-18

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Week 1: Python Basics

*   Review the basics of Python programming, including variables, data types, control structures, loops, functions, and modules.
    
*   Complete coding exercises on websites such as HackerRank, Codewars, or LeetCode to practice your Python programming skills.
    
*   Work through tutorials on the Python Standard Library to become familiar with Python's built-in modules.
    

Resources:

*   Python Crash Course by Eric Matthes
    
*   Automate the Boring Stuff with Python by Al Sweigart
    
*   Python Standard Library documentation
    

Week 2: Data Manipulation with Pandas and NumPy

*   Study Pandas and NumPy, which are essential libraries for data manipulation and analysis.
    
*   Learn to work with dataframes, series, arrays, and matrices to manipulate and analyze data.
    
*   Work through tutorials on data cleaning, merging, reshaping, and aggregating to become proficient in these tasks.
    

Resources:

*   Pandas documentation and tutorials
    
*   NumPy documentation and tutorials
    
*   Kaggle courses on Pandas and NumPy
    

Week 3: Machine Learning with Scikit-learn

*   Study Scikit-learn, which is a popular machine learning library for Python.
    
*   Learn about the different types of machine learning algorithms, such as supervised and unsupervised learning.
    
*   Practice preprocessing data, training models, and evaluating model performance.
    

Resources:

*   Scikit-learn documentation and tutorials
    
*   Coursera Machine Learning course by Andrew Ng
    
*   Kaggle courses on Scikit-learn
    

Week 4: Deep Learning with TensorFlow or PyTorch

*   Study TensorFlow or PyTorch, which are two popular deep learning libraries for Python.
    
*   Learn about artificial neural networks, including feedforward neural networks, convolutional neural networks, and recurrent neural networks.
    
*   Practice building and training neural networks, and learn about techniques such as regularization, optimization, and batch normalization.
    

Resources:

*   TensorFlow documentation and tutorials
    
*   PyTorch documentation and tutorials
    
*   Coursera Deep Learning Specialization by Andrew Ng
    
*   Fast.ai courses on deep learning
    

Underrated AI resources:

*   Papers with Code: A platform that provides code implementations of research papers in machine learning and artificial intelligence.
    
*   Distill An online research journal that provides interactive and visual explanations of research in machine learning and artificial intelligence.
    
*   GitHub: A platform that hosts open-source code repositories, including many libraries and tools for machine learning and artificial intelligence.
    
*   AI Safety: A website that focuses on the societal impacts of artificial intelligence and the potential dangers associated with the technology.
    

Uncommon advice:

*   Focus on mastering the fundamentals of programming and data manipulation before moving on to more advanced topics.
    
*   Spend time reading and studying code written by others, and try to understand how it works and why it was written that way.
    
*   Participate in online forums and communities to ask questions and learn from others in the field.
    
*   Work on small, manageable projects to build up your skills and gain experience.

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*Originally published on [shebin](https://paragraph.com/@znyder/a-weekly-routine-to-improve-my-python-programming-skills-especially-for-machine-learning-and-artificial-intelligence-from-a-senior-machine-learning-mentor)*