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

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.