MOONDOOG Launches Its 3rd Cohort, Python + AI (2026)
Artificial Intelligence is no longer a future skill. it’s a production skill.
From chatbots and automation to data intelligence and deployed AI systems, companies today are hiring people who can build, deploy, and maintain real AI products, not just run tutorials.
That’s why MOONDOOG Technologies is launching its 3rd Cohort: Python + AI Full Program. A hands-on, production-focused training designed to take learners from beginner to deployment-ready in just 8 weeks.
What Makes This Cohort Different?
This is not a theory-heavy course.
This is a build-first, deploy-early, production-ready AI program.
By the end of 8 weeks, participants will have:
- Built real AI applications
- Deployed working APIs and AI services
- Created a portfolio of production-grade projects
- Gained the confidence to work on real-world AI problems
This cohort is designed for:
- University students (Legon, KNUST, GCTU & beyond)
- Career switchers entering tech
- Junior developers leveling up
- Founders who want to build with AI
- Professionals preparing for AI-driven roles
Program Overview
Program: Python + AI Full Curriculum (3rd Cohort)
Duration: 8 Weeks
Mode: Remote (Live sessions + guided practice)
Level: Beginner → Advanced
Start Date: 31st January, 2026
Price: GHS 1,999
Slots: Limited
Full 8-Week Curriculum Breakdown
WEEK 1 — Python Foundations
You’ll build a solid programming base.
Topics
- Variables, data types, operators
- Strings, lists, tuples, sets, dictionaries
- Conditional statements & loops
- Functions, lambda expressions, scopes
- File handling (TXT, CSV, JSON)
- Exceptions & basic logging
- Modules, packages, pip
- Virtual environments & PEP8 standards
Mini-Projects
- CLI calculator
- CSV/JSON parser
- Simple automation script
WEEK 2 — Intermediate Python + APIs
Learn how real systems communicate.
Topics
- List & dictionary comprehensions
- Generators & iterators
- Closures & decorators
- OOP (classes, inheritance, dunder methods)
- Async programming with
asyncio - Threads vs processes
- REST APIs using
requests/httpx
Mini-Projects
- Weather API app
- Async web fetcher
- OOP-based expense tracker
WEEK 3 — Data Handling & Analysis
Turn raw data into insight.
Topics
- NumPy fundamentals
- Pandas (DataFrames, merges, groupby)
- Data cleaning & preprocessing
- Matplotlib & data visualization
- Exploratory Data Analysis (EDA)
- SQL basics (SQLite / MySQL)
Mini-Projects
- Sales dashboard
- Data cleaning pipeline
- EDA project
WEEK 4 — Machine Learning with scikit-learn
Build predictive systems.
Topics
- Supervised vs unsupervised learning
- Train/test split & cross-validation
- Model evaluation metrics
- Algorithms:
- Linear & Logistic Regression
- KNN
- Decision Trees & Random Forest
- Gradient Boosting (XGBoost / LightGBM)
- SVM
- K-Means
- PCA
- Feature engineering
- ML pipelines & hyperparameter tuning
- Model serialization (joblib / pickle)
Mini-Projects
- Classification model
- Clustering system
- End-to-end ML pipeline
WEEK 5 — Deep Learning (TensorFlow / PyTorch)
Go beyond traditional ML.
Topics
- Neural network fundamentals
- Training models with Keras
- CNNs for image recognition
- RNN / LSTM for time-series data
- Optimizers, regularization, dropout
- Data augmentation
- Training callbacks & monitoring
Mini-Projects
- Image classifier
- LSTM time-series predictor
WEEK 6 — Generative AI & Large Language Models
Build modern AI systems.
Topics
- How LLMs work
- Tokenization & embeddings
- Semantic similarity search
- Vector databases (FAISS, Pinecone – concepts)
- RAG (Retrieval-Augmented Generation)
- Prompt engineering
- LangChain & LlamaIndex
- LoRA fine-tuning concepts
- OpenAI, Mistral, Anthropic & Hugging Face APIs
Mini-Projects
- PDF Q&A chatbot (RAG)
- Embedding search engine
WEEK 7 — AI Backend Integration & Deployment
Turn models into real products.
Topics
- FastAPI for AI backends
- REST & streaming endpoints
- Packaging AI models for production
- Docker & containerization
- Deployment on Render, Railway & AWS
- CI/CD fundamentals (GitHub Actions)
- Authentication, rate limiting & monitoring
Mini-Projects
- Deployed AI microservice
- Model-serving API
WEEK 8 — Final Projects & Production AI Concepts
Think like a real AI engineer.
Topics
- RLHF & alignment basics
- Multimodal AI (vision + text)
- Speech pipelines (Whisper → Intent → TTS)
- Explainable AI (SHAP, LIME)
- Edge deployment & optimization
- Model drift & AI observability
Final Deliverables
- Capstone Project 1 (ML or Vision)
- Capstone Project 2 (GenAI / RAG system)
- Fully deployed GitHub portfolio
Capstone Project Examples
Participants may build:
- AI customer support agent
- PDF document Q&A system
- E-commerce recommender
- Flight booking AI assistant
- Real-time vision recognition app
- Voice-enabled AI assistant
Tools & Stack You’ll Use
- Python 3.10+, VS Code, Jupyter
- NumPy, Pandas, Matplotlib
- Scikit-learn, TensorFlow / PyTorch
- FastAPI, Docker
- LangChain, Transformers
- FAISS / Pinecone (concepts)
- Render, Railway, AWS
Assessment & Certification
- Weekly quizzes
- Weekly mini-projects
- Two final capstone projects
- Certificate of completion
Mentorship & Instruction
This cohort includes direct guidance and live sessions led by experienced engineers, including Vidhan Sharma, with a strong focus on:
- Clean code
- Real-world architecture
- Production deployment
- Industry-ready best practices
What’s Next?
MOONDOOG has already trained hundreds of learners across previous cohorts and free workshops — including our Free HTML, CSS & Tailwind Crash Course held on 10th January 2026.
This Python + AI Cohort is the next step for anyone ready to move beyond tutorials and into real AI engineering.
Enrollment is now open. Slots are limited.
Start Date: 31st January, 2026
Contact: contact@moondoog.com
Call/WhatsApp: +233 24 00 11 260
Website: www.moondoog.com