Mastering Python for Machine Learning Fundamentals

Thomas Neild’s ML/AI sessions consistently strike the perfect balance between theory, practical application, and coding. He covered several supervised machine learning algorithms in this course—including linear, multivariate, and logistic regression, as well as decision trees and random forests. Notably, the code is developed in pure Python rather than relying on standard AI/ML libraries, which effectively reinforces the underlying mathematical concepts.

Here are some free AI and machine learning learning resources to enhance your skills: 1. Google Machine Learning Crash Course: https://lnkd.in/dBA76q6X 2. Kaggle Learn: https://lnkd.in/dP8SHcGk 3. Harvard CS50 AI with Python: https://lnkd.in/d3CXMc7m 4. DeepLearning.AI Short Courses: https://lnkd.in/dMFDBc5N 5. TensorFlow Official Tutorials: https://lnkd.in/dJyAdPkF Explore these resources to deepen your understanding of AI and machine learning.

Turn raw data into confident, automated decisions. In this hands-on Applied Machine Learning course, learn to ·       Build and apply regression ·       Support Vector Machines and neural network models using Python, Scikit-learn and Keras ·       Tackle real-world prediction, classification and clustering problems faster and smarter To find more, visit: https://lnkd.in/eVuEEAxf NUS Computing #machinelearning #digitaleconomy #technology

Machine Learning Demystified 🎯 In this short video, I break down how modern machine learning really works—from raw data and feature engineering to training models that power recommendations, predictions, and real-world AI products. If you’re curious about AI, data science, or getting started with Python in ML, this is a practical, no-jargon introduction. #MachineLearning #ArtificialIntelligence #DataScience #DeepLearning #Python #TechInnovation #AIApplications #MachineLearningTrends #DigitalTransformation #DataScienceInsights

I realized that progress in AI/ML doesn’t come from jumping between topics. It comes from building strong foundations and practicing consistently. That’s why I created a roadmap that keeps me focused on the skills that actually compound over time. For my next stretch, I intend to follow my 7-guardrail map: Python → Practical Math → Analysis & Visualization → Core Algorithms → Real Projects → Studying Notebooks → Sharing in Public Each step builds on the previous one, keeping the learning loop practical and grounded in real problem-solving. Do you think this roadmap fits well into an AI/ML learning journey? Also curious: What’s one small habit that significantly improved your ML learning curve? #GIT20DayChallenge #AfricaAgility #TechnovationGirls #AIForGood #MachineLearning #DeepLearning #DataVisualization #LearningInPublic #WomenInAI #BuildInPublic

If anyone is interested in developing their skills in Artificial Intelligence (AI), a quick thought based on my experience that might be helpful. 💬 Here are some tips for developing this skill: Learn the Fundamentals Focus on Python, statistics, linear algebra, and machine learning basics. A strong foundation makes advanced AI concepts easier. Hands-On Projects Build small projects like chatbots, recommendation systems, or image classifiers to apply your knowledge practically.

Hey, the PDF covers Python basics like arithmetic operators, loops, temperature conversion, etc - all essential stuff for deep learning and machine learning. If these concepts are clear, it'll be easier to pick up DL and ML 👍.

📊 Linear vs Logistic Regression Two important Machine Learning algorithms with different purposes: 🔹 Linear Regression – Used to predict continuous values (e.g., house prices). 🔹 Logistic Regression – Used for classification problems (e.g., spam detection). Choosing the right model is key to building effective ML solutions. #MachineLearning #DataScience #AI #Python

📌 Day 18/30 — Machine Learning Revision Challenge Today I went deeper into PCA (Principal Component Analysis) by applying it on a real dataset using sklearn. Working with real ML datasets always boosts intuition — and PCA on digit images is such a great way to visualize variance and compression. Loving this hands-on journey! 🚀 #LearningInPublic #MachineLearning #AI #PCA #Python #MLDaily #DataScientist #MachineLearningEngineer #PythonSoftwareDeveloper

🚀 Day 3 — 🚨 Most people fail at learning AI for one simple reason. They start with the wrong learning order. Here are the most common beginner mistakes 👇 ❌ Jumping straight into Deep Learning without learning Python ❌ Ignoring Statistics & Probability ❌ Copy-pasting code without understanding it ❌ Learning tools instead of concepts The better approach 👇 ✔ Learn Python fundamentals ✔ Understand data analysis (Pandas, NumPy) ✔ Study Machine Learning basics ✔ Move to Deep Learning later AI is not about tools. It’s about understanding how models think. #AIlearning #PythonForAI #MachineLearningJourney #AIbeginners #LearnAI #DataScienceJourney