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🤖 AI — 2025 & Beyond

AI with Python Course

From machine learning fundamentals to building production AI apps — covering scikit-learn, TensorFlow, PyTorch, NLP, OpenAI API, LangChain, RAG pipelines, and deploying AI models to cloud.

50 Hours
12 Modules
4+ Projects
✓ Certificate

Tools & Technologies Covered

🐍 Python 3.x 📊 NumPy & Pandas 🧪 scikit-learn 🧠 TensorFlow / Keras 🔥 PyTorch 💬 OpenAI API / ChatGPT 🔗 LangChain 📦 RAG & Vector Databases 🖼️ OpenCV / Computer Vision 📝 Hugging Face Transformers 📊 Matplotlib / Seaborn ☁ Azure ML / AWS SageMaker

Course Summary

Course Name AI with Python
Duration 50 Hours (Online / Classroom)
Prerequisites Basic Python, NumPy/Pandas knowledge helpful but not required
Target Audience Python developers, data analysts, engineers entering AI/ML field
Certificate Yes — Industry-recognised certificate upon completion
Mode Online & Classroom — with recorded sessions for lifetime access

📋 Detailed Course Syllabus

🟥 Data & ML Foundations

1 Python for Data Science (NumPy & Pandas)
  • NumPy arrays — creation, indexing, slicing, broadcasting, vectorised operations
  • Pandas Series and DataFrame — CRUD, filtering, grouping, merging
  • Data loading — CSV, JSON, Excel, SQL with Pandas
  • Data cleaning — handling missing values, duplicates, type conversion
  • Exploratory Data Analysis (EDA) — describe, info, value_counts
  • Data visualisation — Matplotlib and Seaborn (bar, line, scatter, heatmap)
2 Machine Learning with scikit-learn
  • ML pipeline — data preprocessing, feature engineering, model training, evaluation
  • Supervised learning — Linear Regression, Logistic Regression, Decision Trees, Random Forest
  • Unsupervised learning — K-Means clustering, PCA
  • Model evaluation — accuracy, precision, recall, F1, ROC-AUC, confusion matrix
  • Cross-validation, hyperparameter tuning with GridSearchCV
  • Feature importance and model interpretation
  • Saving and loading models with joblib / pickle

🟣 Deep Learning

3 Neural Networks with TensorFlow / Keras
  • Artificial Neural Networks (ANNs) — neurons, layers, activation functions
  • Building models with Keras Sequential and Functional API
  • Training — loss functions, optimizers (Adam, SGD), batch size, epochs
  • Callbacks — EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
  • Convolutional Neural Networks (CNNs) for image classification
  • Recurrent Neural Networks (RNNs) / LSTMs for sequence data
  • Transfer learning with pre-trained models (VGG16, ResNet, MobileNet)
4 Deep Learning with PyTorch
  • PyTorch tensors — creation, operations, autograd
  • Building neural networks with nn.Module
  • Custom training loop — forward pass, loss, backward pass, optimizer step
  • DataLoader and Dataset for efficient batch training
  • GPU training with CUDA — .to(device)
  • Saving and loading PyTorch models
  • Fine-tuning pretrained models from torchvision

🟡 NLP & Generative AI

5 Natural Language Processing (NLP)
  • Text preprocessing — tokenisation, stopword removal, stemming, lemmatisation
  • Bag-of-Words and TF-IDF vectorisation
  • Sentiment analysis with Naive Bayes and Logistic Regression
  • Word embeddings — Word2Vec, GloVe, FastText
  • Transformer architecture overview — attention, encoder, decoder
  • Hugging Face Transformers — using pre-trained BERT, GPT-2, T5
  • Fine-tuning a pre-trained model for text classification
6 OpenAI API & Prompt Engineering
  • OpenAI API setup — API keys, rate limits, pricing
  • Chat Completions API — system, user, assistant roles
  • Prompt engineering techniques — few-shot, chain-of-thought, role prompting
  • Function calling / tool use with GPT-4o
  • Streaming responses with Server-Sent Events
  • Embeddings API — generating and comparing text embeddings
  • Vision API — analysing images with GPT-4 Vision
7 LangChain & RAG Pipelines
  • LangChain fundamentals — chains, prompts, output parsers
  • Document loaders — PDF, web pages, CSV, databases
  • Text splitters and chunking strategies
  • Vector stores — FAISS, Chroma, Pinecone
  • Retrieval-Augmented Generation (RAG) — embed → store → retrieve → answer
  • Conversational memory — ConversationBufferMemory, window memory
  • LangChain agents — tools, ReAct pattern, custom tools

🟠 Computer Vision & Deployment

8 Computer Vision with OpenCV & YOLO
  • OpenCV basics — reading, writing, resizing, colour space conversions
  • Image filtering — blur, edge detection (Canny), thresholding
  • Object detection with YOLO v8 — setup, inference, custom training
  • Face detection and recognition with OpenCV and DeepFace
  • Real-time video processing from webcam
  • Integrating CV models into a FastAPI endpoint
9 MLOps & Model Deployment
  • Model packaging — ONNX, TorchScript, TF SavedModel
  • Serving models with FastAPI — prediction endpoint, batch inference
  • Containerising ML models with Docker
  • Azure ML — workspaces, experiments, model registry, endpoints
  • AWS SageMaker — training jobs, model hosting, batch transform
  • Monitoring models in production — drift detection, logging predictions
  • CI/CD for ML — GitHub Actions, DVC, model versioning
10 Responsible AI & Ethics
  • AI bias — sources of bias, measuring fairness, mitigation strategies
  • Model explainability — SHAP values, LIME, feature attribution
  • Data privacy — GDPR implications for AI systems, anonymisation
  • AI safety principles — red-teaming, hallucinations, prompt injection
  • Microsoft Responsible AI framework — fairness, reliability, privacy
11 Capstone AI Projects
📦 Project Options:
  • AI Chatbot — OpenAI + LangChain + RAG on custom docs
  • Image Classifier API — CNN + FastAPI + Docker
  • Sentiment Dashboard — NLP + Pandas + Streamlit
  • Product Recommender — collaborative filtering + FastAPI
✅ Must-Include:
  • Trained and evaluated ML / DL model
  • FastAPI inference endpoint
  • Deployed to cloud (Azure / AWS)
  • Model card with performance metrics
  • Responsible AI considerations documented

🤖 Build Real AI Applications with Python!

50 hours — ML, Deep Learning, LangChain, RAG, OpenAI API & cloud deployment.

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