Embarking on a new learning adventure is always an exciting prospect. I am thrilled to share with you the insights and knowledge I will be gaining through this post, with gratitude to Perplexity for the inspiration and guidance to create this path. While I have previously explored many of the topics covered in this learning journey, I see this as a remarkable opportunity to challenge myself further and deepen my understanding.

Throughout this post, I will be sharing my progress, celebrating key milestones, and being candid about any hurdles I may encounter along the way. Your feedback and suggestions are invaluable to me, so please feel free to share any topics or resources that you believe should be included in this learning path. Your input is greatly appreciated!

Without further ado, let’s delve into the learning journey together. I hope you find inspiration and value in this path that I am about to embark on!

Here is the structured learning path that I will be following, and I invite you to join me in utilizing it for your own educational pursuits!

Phase 1: Foundation (Days 1–20)

Days 1–5: Python Programming Basics

• Master fundamental Python concepts like variables, data types, loops, conditionals, and functions.
• Put your skills into practice by creating simple Python scripts and sharing them on GitHub.

Days 6–10: Linear Algebra and Statistics

• Dive into the realm of linear algebra by exploring matrices and their operations.
• Gain insights into basic statistical concepts such as mean, median, variance, and standard deviation.

Days 11–15: Data Structures and Algorithms

• Explore essential data structures like lists, dictionaries, sets, and tuples in Python.
• Learn about fundamental algorithms such as sorting and searching techniques.

Days 16–20: Introduction to Machine Learning

• Grasp the fundamental concepts of machine learning, including supervised, unsupervised, and reinforcement learning models.
• Set up your development environment with key libraries, such as NumPy and Pandas, essential for implementing machine learning algorithms.

…