UCSC Silicon Valley Extension Machine Learning with Python

atomcamp AI bootcamp Weekend Update: It is said that teaching is the best way to learn. Over the weekend I had the opportunity to host sessions on the following topics: 1. Introduction to Python where we discussed the basic syntax of Python. The data structures in Vanilla Python, their benefits and limitations, and how NumPy solves the problem of numerical computation in Python. Muhammad Zuraiz Amir had some very interesting questions that made the session more exploratory and informative. 2. Prompt Engineering where we discussed the basic anatomy of a prompt, how the behavior of LLMs and Generative AI (Gen AI) can be customized with the prompt, elements of a good prompt. We also had a fruitful discussion about the advantages and disadvantages of online and offline LLMs . Thank you Fazal E Subhan for your curiosity and interest. #AI #Atomcamp #Bootcamp #Python #PromptEngineering #LLM #GenAI

I built a live AI app in my first year of university. Here are 5 things nobody told me before I started 🧵 Swipe — each slide is a lesson I had to learn the hard way. Most first-year students are still watching tutorials. I wanted to be shipping products. Which lesson hits closest to home for you? Drop it below 👇 #DataScience #MachineLearning #Python #AI #DataScienceStudent #MLInternship #ArtificialIntelligence #StudentDeveloper

If you’re still learning Python without these AI repos… you’re already behind in 2026 Everyone is learning Python. But very few people are learning how to build real AI systems. The difference? Top developers today are not coding from scratch anymore. They’re using: - Multi-agent frameworks - Typed AI pipelines - CPU-efficient models These Python AI repos are doing 80% of the heavy lifting or actually learning how to build products? Because in 2026, companies don’t hire people who “know Python”. They hire people who can ship AI systems If you want to go beyond tutorials and actually build real projects, join our 1:1 Python Mentorship Program: https://lnkd.in/dpHv3i4p #Zerotoknowing #Python #AI #coding

Certification Course on Applied Machine Learning with Python For II Semester Students. Resource Person: Name Niladri Roy Designation: Machine Learning Engineer at Analogica Day 4: Classification Models & Evaluation Focus: Build classification models and interpret their results for decision-making. Content Coverage: ● Classification problem framing ● Logistic Regression ● k-Nearest Neighbours awareness ● Decision-focused metrics awareness ● Guided classification practice #certificationcourse #day4 #classificationmodels #machinelearning #datascience #logisticregression #knn #modelevaluation #klebca #klebcakhanapur

Day 14 of my AI & Data Science Journey Today, I learned about functions in Python, with a focus on implementing nested functions. What I explored: Concept and components of functions Types and classification of functions Implementation of user-defined functions Key focus: Nested functions (function inside another function) How inner functions can access variables from the outer function Practical implementation of nested functions to organize code better Practiced writing programs using nested functions to break down problems into smaller parts. ✨ Key Insight: Nested functions help improve code structure, readability, and reusability by organizing logic within a function. They are useful when a function is needed only within another function. #Python #Programming #AI #DataScience #LearningJourney #Coding #Functions #Consistency

#FREEAICOURSE Stanford’s "Code in Place" 2026: Launched April 20, 2026, this world-renowned free introductory Python course now features a massive "AI for Educators" support track to help teachers build their own grading or lesson-prep tools. https://lnkd.in/gycuCt8d #CodeInPlace #StanfordUniversity #PythonProgramming #LearnToCode #FreeCourses #AIforEducators #EdTech #TeachersWhoCode #FutureOfEducation #TeachingWithAI #Python #ArtificialIntelligence #Programming2026 #ComputerScience #DataScience #EducationForAll #CodingCommunity #StanfordEngineering #LifelongLearning #TechForGood

✓ Advance Python Course with Machine and Deep Learning. ✓ Exercise ( Task 21 ). ✓ Statement:- 1) ----- Write a program that demonstrates a system error and then prints its output. #LearningInPublic #CodingNewBie #PythonCourse #Programming #FutureGoals #Coding

Learn the fundamentals of NumPy in Python with this beginner-friendly introduction! 🚀 In this video, I’ve covered: What is NumPy? Why NumPy is important NumPy arrays basics Difference between lists and arrays Basic operations in NumPy NumPy is one of the most powerful libraries in Python for numerical computing and is widely used in Data Science, Machine Learning, and AI. See the Details Video here : https://lnkd.in/d4ShsbXj 💡 If you are starting your journey in Python or AI, this video will help you build a strong foundation. #NumPy #Python #PythonForBeginners #LearnPython #DataScience #MachineLearning #AI #Coding #Programming #PythonTutorial #Developers #Tech #ArtificialIntelligence #DataAnalysis

The roadmap to complete machine learning in 2026 The entire ML learning roadmap is divided into 5 steps as follows: Step 1: Learn Python → Step 2: Math & Statistics → Step 3: Core ML Algorithms → Step 4: Real Projects → Step 5: Build Portfolio

The roadmap to complete machine learning in 2026 The entire ML learning roadmap is divided into 5 steps as follows: Step 1: Learn Python → Step 2: Math & Statistics → Step 3: Core ML Algorithms → Step 4: Real Projects → Step 5: Build Portfolio