AI Frameworks & APIs

Langchain Chains

OpenAI API Guide — GPT-4, DALL-E, and Whisper Integration

Learn OpenAI API integration: GPT-4 chat completions, streaming, function calling, embeddings, DALL-E image generation, Whisper speech-to-text, token counting, and rate limits.

Anthropic Claude API Guide — Prompt Engineering and Integration

Learn Anthropic Claude API integration: Messages API, system prompts, tool use, extended thinking, safety features, and comparison with OpenAI GPT-4.

LangChain Guide — Building LLM Applications with Chains and Agents

Learn LangChain: chains, agents, tools, memory, document loaders, vector stores, and RAG (retrieval augmented generation) for building production LLM applications.

Mistral AI Guide — Le Chat API and Open-Source Models

Learn Mistral AI integration: Mistral Large, Small, Nemo models, Le Chat API, embedding models, function calling, and building applications with open-source French AI.

Mastra AI Framework Guide — Building AI Agents and Workflows

Learn Mastra AI framework: building agents, workflows, tools, memory, RAG, LLM integration, deployment patterns, and production best practices.

Anthropic Claude API: Complete Developer Guide

Complete Anthropic Claude API developer guide — Messages API, system prompts, temperature/top_p/max_tokens, streaming, tool use/function calling, vision, prompt caching, and cost optimization.

DeepSeek API: Complete Integration Guide

Complete DeepSeek API integration guide — chat completions, DeepSeek-R1 reasoning model, code generation, API parameters, streaming, cost comparison with OpenAI, and self-hosting options.

Mistral AI: Models and API Guide

Complete Mistral AI guide — Mistral 7B, Mixtral 8x7B, Mistral Large models, API access via La Plateforme, self-hosting with Ollama/vLLM, quantization (GGUF/GPTQ), function calling, embeddings, and fine-tuning.

Vector Databases — Embeddings, Similarity Search, Indexing & RAG Pipelines

Learn vector databases: embeddings (OpenAI, Cohere, HuggingFace), Pinecone, Weaviate, Chroma, Qdrant, Milvus, similarity search (cosine, euclidean, dot product), hybrid search, metadata filtering, RAG pipelines, and indexing methods (HNSW, IVF, PQ).

Fine-Tuning LLMs — Full Fine-Tuning vs PEFT, LoRA, Dataset Prep, Training Frameworks & Deployment

Learn fine-tuning LLMs: full fine-tuning vs PEFT (LoRA, QLoRA, Adapters), dataset preparation (format, quality, dedup, augmentation), training frameworks (Axolotl, Unsloth, HuggingFace TRL), evaluation (perplexity, BLEU, ROUGE), deployment with vLLM/TGI/Ollama, overfitting prevention, and cost considerations.

AI Agents — Architecture, LangGraph, Multi-Agent Systems, Tool Use, Planning, Memory & Production Deployment

Learn AI agents: agent architecture (perception, reasoning, action), AutoGPT, CrewAI, LangGraph (state graphs, nodes, edges), multi-agent systems, tool use (function calling, custom tools), planning (ReAct, Plan-and-Execute), memory (short-term, long-term, entity), error recovery, production deployment, and safety.

CrewAI — Multi-Agent AI Framework Guide

Learn how to build multi-agent AI systems with CrewAI: define agents with roles and goals, assign tasks, manage workflows, and orchestrate collaborative LLM-powered teams.

LlamaIndex — RAG Framework Complete Guide

Learn how to build retrieval-augmented generation (RAG) systems with LlamaIndex: index documents, query with context, manage vector stores, and create data-powered AI applications.

Hugging Face Transformers — Complete Guide

Learn how to use Hugging Face Transformers for NLP, computer vision, and audio: loading pretrained models, fine-tuning on custom data, pipeline APIs, and deployment to production.

Google AI — Gemini API & Vertex AI Guide

Learn Google AI integration with Gemini API and Vertex AI: multimodal models, text generation, vision, embeddings, safety filters, and production deployment strategies.

Cohere API — NLP & Embeddings Complete Guide

Learn Cohere API integration for NLP: text generation, semantic embeddings, classification, reranking, summarization, and retrieval-augmented generation (RAG).

Replicate — Run Open-Source AI Models Guide

Learn Replicate API for running open-source AI models: Llama, Stable Diffusion, Whisper, image generation, fine-tuning, webhooks, and production deployment at scale.

Together AI — Distributed AI Inference Guide

Learn Together AI platform for distributed inference: Llama 3, Mixtral, DeepSeek, text generation, embeddings, fine-tuning, and production deployment with low latency.

Ollama — Run LLMs Locally Complete Guide

Learn Ollama for running LLMs locally: installing models like Llama 3 and Mistral, building chat and embedding applications, optimizing performance, and creating custom Modelfiles.

Anthropic Claude API Guide — Chat, Vision, Tool Use, and Streaming

Learn Anthropic Claude API integration: Messages API chat completions, streaming, tool use, system prompts, vision analysis, prompt caching, and best practices.

AI Frameworks & APIs Explained -- Complete Beginner's Guide

Learn what AI frameworks and APIs are, how they simplify machine learning development, and why TensorFlow, PyTorch, and Hugging Face lead the ecosystem.

Supervised vs Unsupervised Learning -- Key Differences

Learn the key differences between supervised and unsupervised machine learning through complete real-world use cases and practical Python code examples.

Neural Networks Basics -- Architecture Explained Step by Step

Learn how neural networks work from scratch including perceptrons, activation functions, hidden layers, and backpropagation with PyTorch code examples.

How to Choose an ML Framework -- TensorFlow vs PyTorch vs JAX

Learn how to choose the best ML framework for your next project by comparing TensorFlow, PyTorch, and JAX across performance, ecosystem, and ease of use.

Data Preprocessing for Machine Learning -- Complete Guide

Learn data preprocessing techniques including normalization, standardization, handling missing values, and encoding categorical variables for ML pipelines.

Model Training Workflow -- Step-by-Step Practical Guide

Learn a complete model training workflow from data splitting and hyperparameter tuning to cross-validation and saving trained models for production uses.

Evaluation Metrics for ML Models -- Accuracy, Precision, Recall, F1

Learn how to evaluate machine learning models using accuracy, precision, recall, F1-score, ROC-AUC curves, and confusion matrices in your Python projects.

TensorFlow Basics -- Complete Beginner's Guide with Examples

Learn TensorFlow basics including tensors, operations, graphs, and eager execution mode with hands-on Python examples building your first neural network.

Keras Sequential Model Explained -- Build Your First Neural Net

Learn how to build, compile, train, and evaluate deep neural networks using the Keras Sequential API with step-by-step examples and practical starter tips.

TensorFlow Data Pipeline with tf.data -- Performance Guide

Learn how to build high-performance data pipelines using the TensorFlow tf.data API including prefetching, caching, batching, and parallel data loading.

Keras Callbacks and Early Stopping -- Prevent Overfitting

Learn how to use Keras callbacks including early stopping, model checkpointing, learning rate scheduling, and TensorBoard logging for better model training.

TensorFlow Model Save and Load -- Complete Guide

Learn how to save and load TensorFlow models in SavedModel and HDF5 formats including weights-only saving, full model export, and TF Lite conversion methods.

Keras Transfer Learning Tutorial -- Leverage Pretrained Models

Learn transfer learning with Keras using pretrained models like VGG16, ResNet, and EfficientNet to achieve high accuracy with minimal training data samples.

TensorFlow Serving for Model Deployment -- Production Guide

Learn to deploy TensorFlow models into production using TensorFlow Serving with REST and gRPC endpoints, batching, and model version management controls.

PyTorch Basics -- Tensor Operations Complete Guide

Learn PyTorch tensor operations including creation, indexing, reshaping, broadcasting, and GPU acceleration with hands-on Python code examples and demos.

PyTorch Autograd -- Automatic Differentiation Explained

Learn how PyTorch Autograd computes gradients automatically for backpropagation with computational graphs, gradient tracking, and custom gradient functions.

PyTorch nn.Module -- Building Custom Neural Network Models

Learn to build custom neural network architectures in PyTorch using nn.Module including sequential containers, custom layers, and parameter management.

PyTorch DataLoader and Dataset -- Data Loading Guide

Learn PyTorch DataLoader and Dataset classes for efficient data loading, batching, shuffling, and parallel preprocessing in your model training pipelines.

PyTorch Training Loop Explained -- From Scratch Implementation

Learn to write a complete PyTorch training loop from scratch including forward pass, loss computation, backpropagation, gradient zeroing, and batch iteration.

PyTorch CNN for Image Classification -- Step-by-Step Tutorial

Learn to build and train a convolutional neural network in PyTorch for CIFAR-10 image classification with data augmentation and GPU acceleration techniques.

PyTorch Model Export with TorchScript -- Deployment Guide

Learn to export PyTorch models using TorchScript for production deployment including tracing, scripting, quantization, and mobile-optimized model export.

OpenAI API Basics -- Complete Developer's Guide

Learn OpenAI API fundamentals including authentication, model selection, request formatting, streaming responses, and effective rate limit handling in Python.

OpenAI Chat Completions API Tutorial -- Build Chat Apps

Learn to use the OpenAI Chat Completions API with system prompts, message history, temperature control, and token limits for building conversational AI.

LangChain Basics -- LLM Application Development Framework

Learn LangChain fundamentals including prompt templates, chains, memory management, output parsers, and LLM orchestration for building complex AI applications.

RAG Pipeline with LangChain -- Retrieval-Augmented Generation

Learn to build a retrieval-augmented generation pipeline using LangChain with vector stores, document loaders, embeddings, and LLM-based answer generation.

OpenAI Embeddings API Guide -- Text to Vector Conversion

Learn to use OpenAI text-embedding models for semantic search, text clustering, recommendation systems, and similarity comparisons with Python code examples.

Prompt Engineering Techniques -- Advanced LLM Prompting Guide

Learn prompt engineering techniques including few-shot prompting, chain-of-thought, role prompting, and structured outputs for optimized LLM responses.

OpenAI Function Calling Tutorial -- Structured Data Extraction

Learn OpenAI function calling to extract structured data from text, call external APIs, and build tool-using AI agents with JSON schema definitions properly.

OpenCV Basics -- Image Processing in Python Guide

Learn OpenCV fundamentals for image processing including reading, writing, resizing, filtering, edge detection, and feature extraction techniques in Python.

Image Classification APIs -- Pretrained Models Compared

Learn to use pretrained image classification models from Hugging Face, TensorFlow Hub, and PyTorch Hub with practical code examples and performance benchmarks.

Object Detection with YOLO -- Real-Time Detection API Guide

Learn real-time object detection using the YOLO API including inference, bounding box extraction, confidence thresholds, and custom model fine-tuning steps.

Hugging Face Image Pipeline -- Classification and Segmentation

Learn Hugging Face image pipelines for classification, object detection, segmentation, and depth estimation using transformer-based vision model APIs.

Google Cloud Vision API Tutorial -- Image Analysis Guide

Learn Google Cloud Vision API for label detection, OCR text extraction, face detection, landmark recognition, and safe search content filtering in Python.

AWS Rekognition API Guide -- Image and Video Analysis

Learn AWS Rekognition for image and video analysis including facial recognition, content moderation, celebrity detection, and custom label model training.

Image Generation APIs -- DALL-E and Stable Diffusion Guide

Learn text-to-image generation using DALL-E 3 and Stable Diffusion APIs including prompt crafting, parameter tuning, and advanced image editing techniques.

Hugging Face Transformers Basics -- Complete Beginner's Guide

Learn the Hugging Face Transformers library including model loading, tokenization, pipeline inference, and working with pretrained transformer models in Python.

NLP Text Classification with Transformers -- Fine-Tuning Guide

Learn text classification using transformer models including fine-tuning BERT and DistilBERT for sentiment analysis, spam detection, and topic labeling.

Named Entity Recognition with NLP APIs -- Extraction Guide

Learn named entity recognition using spaCy, Hugging Face, and cloud NLP APIs to extract people, organizations, locations, and custom entities from text.

Text Summarization APIs -- Extractive and Abstractive Methods

Learn text summarization using Hugging Face pipelines, OpenAI GPT models, and BART transformers for extractive and abstractive summary generation methods.

Sentiment Analysis API Tutorial -- From Text to Insights

Learn sentiment analysis using APIs from Hugging Face, Google Cloud, and AWS to classify text as positive, negative, or neutral with confidence scores.

Sentence Embeddings and Semantic Search -- Practical Guide

Learn to generate sentence embeddings using Sentence Transformers and OpenAI for semantic search, text clustering, and similarity measurement in Python.

Hugging Face Fine-Tuning Guide -- Custom Model Training

Learn to fine-tune Hugging Face transformer models on custom datasets using the Trainer API including tokenization, training arguments, and evaluation.

Google Cloud AI Platform -- End-to-End ML Workflow Guide

Learn Google Cloud AI Platform for training, deploying, and managing machine learning models at scale using Vertex AI pipelines and custom containers.

AWS SageMaker Basics -- Build Train and Deploy ML Models

Learn AWS SageMaker for building, training, and deploying machine learning models including managed notebooks, automatic tuning, and production model hosting.

Azure AI Services Overview -- Vision Speech and Language APIs

Learn Microsoft Azure AI services including Computer Vision, Speech-to-Text, Language Understanding, and Translator APIs with Python code integration.

Cloud Model Deployment Strategies -- Serving ML at Scale

Learn cloud model deployment strategies including serverless inference, auto-scaling endpoints, A/B testing, canary deployments, and multi-region serving.

Serverless ML Inference -- Deploy Models Without Servers

Learn serverless ML inference using AWS Lambda, Google Cloud Functions, and Azure Functions with cold start optimization and container-based deployments.

Vector Databases for AI Applications -- Pinecone Weaviate Qdrant

Learn vector databases including Pinecone, Weaviate, and Qdrant for storing and searching embeddings in AI-powered semantic search and RAG applications.

MLOps Basics -- CI/CD for Machine Learning Pipelines

Learn MLOps fundamentals including model versioning, experiment tracking with MLflow, automated retraining pipelines, and monitoring models in production.

ML Project Best Practices -- Structure and Workflow Guide

Learn ML best practices including project structure, reproducibility, data versioning, experiment tracking, and code organization for team collaboration.

Model Monitoring and Maintenance -- Production ML Guide

Learn how to monitor ML models in production including drift detection, performance degradation alerts, data quality checks, and automated retraining.

Ethical AI Practices for Developers -- Fairness and Transparency

Learn ethical AI practices including bias detection, fairness metrics, model interpretability with SHAP and LIME, and responsible AI deployment guidelines.

AI Framework Comparison -- TensorFlow vs PyTorch vs JAX

Learn a detailed comparison of TensorFlow, PyTorch, and JAX across performance benchmarks, community ecosystem, deployment options, and learning curves.

AI Engineer Career Roadmap 2026 -- Skills and Certifications

Learn the AI engineer career roadmap for 2026 including essential skills, learning paths, portfolio projects, and certification recommendations for readiness.

AI Certification Guide for Developers -- Top Certifications 2026

Learn about top AI certifications including TensorFlow Developer, AWS Machine Learning, Google Cloud ML Engineer, and Azure AI certifications in 2026.