A Beginner-to-Upper Intermediate Data Science Roadmap for 2025 #9: Mastering Machine Learning
A Step-by-Step Roadmap to Start a Data Science Career In 2025
In the ninth article of the series A Beginner-to-Upper Intermediate Data Science Roadmap for 2025, you will learn the fundamentals of machine learning for data science.
In this step of the roadmap, you’ll explore key concepts such as supervised and unsupervised learning, model evaluation, and feature engineering. This guide will introduce essential algorithms, including linear regression, decision trees, and neural networks, along with practical applications using Python libraries like Scikit-Learn.
By the end of this learning step of the roadmap, you’ll have a solid foundation for building and fine-tuning machine learning models, preparing you for more advanced topics in artificial intelligence and deep learning.
This article is the Ninth article in the ongoing series of A Beginner-to-Upper Intermediate Data Science Roadmap for 2025:
Introduction to Data Science & Data Methodology (Published!)
Mastering Machine Learning (You are here!)
Deep Learning Fundamentals (Coming Soon!)
Generative AI & Large Language Models (LLMs) Fundamentals (Coming Soon!)
Machine Learning Operations (MLOps) (Coming Soon!)
Building Your Data Science Portfolio (Coming Soon!)
Getting Ready for the Market (Coming Soon!)
Whether you’re a recent graduate or a professional looking to make a career change, Data Science and AI offer a wide range of exciting and lucrative opportunities.
In this series of articles, I will provide a comprehensive guide with a clear and actionable plan for building the skills and knowledge you need to succeed in this growing field. By following the steps outlined in this roadmap, you’ll be well on your way to a successful and rewarding Data Science and AI career.
This roadmap will take you to an upper intermediate level, and you can land a job and start your career after finishing it. However, to go to an advanced level, you must take more in-depth courses, books, and research papers.
For each learning step, there will be compulsory material, optional material, and action points to ensure that you implement what you have learned Also, each of the learning resources will be estimated in hours, so you can calculate the time needed to finish this roadmap based on your pace.
Table of Contents:
Machine Learning Specialization
Additional Resources
Putting it into Action
