Agentic AI Coding

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📂 Full description Many of the worlds most exciting and innovative new tech projects leverage the power of machine learning. But if you want to set yourself apart as a data scientist or machine learning engineer, you need to stay up to date with the current tools and best practices for creating effective, predictable models.In this course, instructor Helen Sun shows you how to get up and running with PyTorch, the open-source machine learning framework known for its simplicity, performance, and APIs. Explore the basic concepts of PyTorch, including tensors, operators, and conversion to and from NumPy, as well as how to utilize autograd, which tracks the history of every computation recorded by the framework. By the end of this course, youll also be equipped with a new set of skills to get the most out of Torchvision, Torchaudio, and Torchtext.

🔅 Hands-On PyTorch Machine Learning 🌐 Author: Helen Sun 🔰 Level: Intermediate ⏰ Duration: 56m

🌀 Discover the fundamentals of creating machine learning models with PyTorch, the open-source machine learning framework.

📗 Topics: PyTorch, Machine Learning 📤 Join Artificial intelligence for more courses

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🔗 AI Agents for Beginners - A Course

This course has 10 lessons covering the fundamentals of building AI Agents.

🔰Each lesson covers its own topic so start wherever you like! 🔰There is multi-language support for this course. 🔗 Links: https://github.com/microsoft/ai-agents-for-beginners

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🔅 08 - FineTuning LLMs

🔅 07 - LLM Context Window

🔅 06 - LLM Parameters

🔅 05 - Inside an LLM Under the Hood

🔅 04 - How LLMs generate text

🔅 03 - Who invented LLMs

🔅 02 - Ingredients of an LLM

🔅 01 - Introduction to LLMs

LLMs Intuition 01 - Introduction to LLMs 02 - Ingredients of an LLM 03 - Who invented LLMs 04 - How LLMs generate text 05 - Inside an LLM Under the Hood 06 - LLM Parameters 07 - LLM Context Window 08 - FineTuning LLMs

🔗 The Only roadmap you need to become an ML Engineer 🥳 Phase 1: Foundations (1-2 Months) 🔹 Math & Stats Basics – Linear Algebra, Probability, Statistics 🔹 Python Programming – NumPy, Pandas, Matplotlib, Scikit-Learn 🔹 Data Handling – Cleaning, Feature Engineering, Exploratory Data Analysis Phase 2: Core Machine Learning (2-3 Months) 🔹 Supervised & Unsupervised Learning – Regression, Classification, Clustering 🔹 Model Evaluation – Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC) 🔹 Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization 🔹 Basic ML Projects – Predict house prices, customer segmentation Phase 3: Deep Learning & Advanced ML (2-3 Months) 🔹 Neural Networks – TensorFlow & PyTorch Basics 🔹 CNNs & Image Processing – Object Detection, Image Classification 🔹 NLP & Transformers – Sentiment Analysis, BERT, LLMs (GPT, Gemini) 🔹 Reinforcement Learning Basics – Q-learning, Policy Gradient Phase 4: ML System Design & MLOps (2-3 Months) 🔹 ML in Production – Model Deployment (Flask, FastAPI, Docker) 🔹 MLOps – CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow) 🔹 Cloud & Big Data – AWS/GCP/Azure, Spark, Kafka 🔹 End-to-End ML Projects – Fraud detection, Recommendation systems Phase 5: Specialization & Job Readiness (Ongoing) 🔹 Specialize – Computer Vision, NLP, Generative AI, Edge AI 🔹 Interview Prep – Leetcode for ML, System Design, ML Case Studies 🔹 Portfolio Building – GitHub, Kaggle Competitions, Writing Blogs 🔹 Networking – Contribute to open-source, Attend ML meetups, LinkedIn presence The data field is vast, offering endless opportunities so start preparing now.

Tech Stack Roadmaps by Career Path 🛣️ What to learn depending on the job you’re aiming for 👇 1. Frontend Developer ❯ HTML, CSS, JavaScript ❯ Git & GitHub ❯ React / Vue / Angular ❯ Responsive Design ❯ Tailwind / Bootstrap ❯ REST APIs ❯ TypeScript (Bonus) ❯ Testing (Jest, Cypress) ❯ Deployment (Netlify, Vercel) 2. Backend Developer ❯ Any language (Node.js, Python, Java, Go) ❯ Git & GitHub ❯ REST APIs & JSON ❯ Databases (SQL & NoSQL) ❯ Authentication & Security ❯ Docker & CI/CD Basics ❯ Unit Testing ❯ Frameworks (Express, Django, Spring Boot) ❯ Deployment (Render, Railway, AWS) 3. Full-Stack Developer ❯ Everything from Frontend + Backend ❯ MVC Architecture ❯ API Integration ❯ State Management (Redux, Context API) ❯ Deployment Pipelines ❯ Git Workflows (PRs, Branching) 4. Data Analyst ❯ Excel, SQL ❯ Python (Pandas, NumPy) ❯ Data Visualization (Matplotlib, Seaborn) ❯ Power BI / Tableau ❯ Statistics & EDA ❯ Jupyter Notebooks ❯ Business Acumen 5. DevOps Engineer ❯ Linux & Shell Scripting ❯ Git & GitHub ❯ Docker & Kubernetes ❯ CI/CD Tools (Jenkins, GitHub Actions) ❯ Cloud (AWS, GCP, Azure) ❯ Monitoring (Prometheus, Grafana) ❯ IaC (Terraform, Ansible) 6. Machine Learning Engineer ❯ Python + Math (Linear Algebra, Stats) ❯ Scikit-learn, Pandas, NumPy ❯ Deep Learning (TensorFlow/PyTorch) ❯ ML Lifecycle (Train, Tune, Deploy) ❯ Model Evaluation ❯ MLOps (MLflow, Docker, FastAPI) React with ❤️ if you found this helpful — content like this is rare to find on the internet! Credits: https://t.me/lesformations ENJOY LEARNING 👍👍