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🎓 15 Free MIT Courses to Kickstart Your Data Science Career 🔥 Published by MIT Open Learning, this curated list brings you the foundational building blocks of Data Science—math, stats, Python, ML, and more. All 100% free! 📚 Course List: 1️⃣ Linear Algebra Explore linear algebra and matrix theory through multidisciplinary topics. 2️⃣ Single Variable Calculus Master derivatives, integrals, coordinate systems, and infinite series. 3️⃣ Multivariable Calculus Learn differential, integral, and vector calculus for multivariable functions. 4️⃣ Introduction to Probability and Statistics Foundations of probability, Bayesian inference, and linear regression. 5️⃣ Probability: The Science of Uncertainty and Data Part of MITx MicroMasters in Statistics & DS—random processes, statistical inference. 6️⃣ Fundamentals of Statistics Estimation, hypothesis testing, prediction. Also part of MITx MicroMasters. 7️⃣ Understanding the World Through Data Use basic data forms, tools & ML algorithms to make sense of the world. 8️⃣ Introduction to Computer Science and Programming Using Python Solve real-world analytical problems with Python 3.5. 9️⃣ Introduction to Computational Thinking and Data Science Learn to solve problems computationally & write small, effective programs. 🔟 Data Analysis: Statistical Modeling and Computation in Applications Analyze real-world data using stats & computation (also MicroMasters course). 1️⃣1️⃣ Introduction to Algorithms Model computational problems and solve them using powerful algorithms. 1️⃣2️⃣ Introduction to Machine Learning Explore ML principles, modeling, and predictive applications. 1️⃣3️⃣ Matrix Methods in Data Analysis, Signal Processing, and ML Linear algebra meets neural networks, probability, and optimization. 1️⃣4️⃣ Mathematics of Big Data and Machine Learning Understand D4M (Dynamic Distributed Dimensional Data Model) using graph theory and databases. 1️⃣5️⃣ Machine Learning with Python: from Linear Models to Deep Learning Hands-on ML with linear models, deep learning, and reinforcement learning in Python. --- 🌐 Source: MIT Open Learning 🔗 https://openlearning.mit.edu/news/15-free-mit-data-science-courses 💸 100% Free | 📍 Self-Paced | 🧠 Taught by Top MIT Professors #datascience ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

Machine Learning Free Courses Machine Learning with Python – FreeCodeCamp 🎬 1 video lesson (full course) Duration ⏰: 4 hours 🏃‍♂️ Self paced Resource: freecodecamp 🔗 Course Link Machine Learning for Beginners – 26-Lesson ML Video Series 🆓 Free Online Course 🎬 Video lectures ⏰ 10 hours 🏃‍♂️ Self paced Teacher 👨‍🏫 : Microsoft Cloud Advocates Team Source: Microsoft Learn 🔗 Course Link STAT 451: Introduction to Machine Learning – Sebastian Raschka (UW‑Madison) 🆓 Free Online Course 🎬 Video lectures ⏰ ~20 hours 🏃‍♂️ Self paced Teacher 👨‍🏫 : Sebastian Raschka (U‑Wisconsin‑Madison) Source: UW‑Madison STAT 451 YouTube & course materials 🔗 Course Link Intro to Machine Learning with Python (Kaggle) Rating ⭐️: 4.5 out of 5 Students 👨‍🎓: 125,000+ Duration ⏰: 3hrs 30min Created by: Kaggle (Dan Becker) 🔗 Course Link Google’s Machine Learning Crash Course ⏳Modules: 25+ Duration ⏰: 15 hours 🏃‍♂️ Self paced Resource: Google AI 🔗 Course Link Machine Learning Specialization – DeepLearning.AI (Audit Free) Rating ⭐️: 4.8 out of 5 Students 👨‍🎓: 900,000+ Duration ⏰: ~30 hours (3 courses) Created by: Andrew Ng (DeepLearning.AI) 🔗 Course Link Introduction to Machine Learning – CMU (10-301/601) 🆓 Free Online Course 🎬 Video lectures 📝 Lecture notes (PDF) ⏰ ~30 hours 🏃‍♂️ Self-paced Teacher 👨‍🏫: CMU Faculty (varies by year) Source: Carnegie Mellon University 🔗 Lecture Notes Machine Learning Full Course – Edureka (YouTube) 🎬 1 video lesson (full course) Duration⏰: 10 hours 🏃‍♂️ Self paced Resource: YouTube 🔗 Course Link #machinelearning #ml ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

Hands‑On Intro to Data Science with Python (2025, Univ. of Applied Sciences Düsseldorf) 📍 Topic: Python‑based data science workflow from scratch 📑 Format: Jupyter notebooks + datasets + PDF book 📆 Release: 2025 👨‍🏫 Created by Huber et al. at Düsseldorf UAS & ZDD ⏰ Duration: Self‑paced (~40 hrs) 🔗 Link: https://florian-huber.github.io/data_science_course/book/cover.html 📝 Description: A modern, project-oriented course teaching Pandas, Matplotlib, scikit‑learn via real datasets. Perfect for early-stage data scientists. #datascience #python ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈