Course Overview
- Course Title: Mastering Data Science & AI with Python & Real-World Projects
- Instructor: Tamer Abdelaty Ahmed (Temotec AI Learning)
- Target Audience:
- Aspiring data scientists
- Developers transitioning to AI/ML
- AI enthusiasts with no prior coding experience
- Professionals seeking to upskill in Python, data analysis, and AI
- Prerequisites:
- None (beginner-friendly)
- Basic high school math knowledge is helpful but not required
Curriculum Highlights
- Key Topics Covered:
- Python Programming: Basics to advanced (NumPy, Pandas)
- Data Manipulation & Analysis: Cleaning, filtering, and transforming datasets
- Data Visualization: Charts, graphs, and interactive visualizations
- Statistics for Data Science: Core concepts for model development
- Machine Learning Algorithms:
- Supervised learning (regression, classification)
- Unsupervised learning (clustering, dimensionality reduction)
- Model evaluation techniques (t-SNE, PCA)
- AI & LLM Applications:
- Local LLM deployment with Ollama
- AI app development using LangChain & Streamlit
- RAG-based AI research tools
- Project Deployment: Streamlit, XGBoost, and real-world workflow automation
- Key Skills Learned:
- Python coding for data science and AI
- Data cleaning, analysis, and visualization with Pandas & Matplotlib
- Statistical modeling and hypothesis testing
- Building and evaluating ML models (regression, classification, clustering)
- Developing and deploying AI-powered applications (LLMs, chatbots, automation tools)
- Local LLM integration (Ollama, LM Studio) without cloud dependency
Course Format
- Duration:
- 21.5 hours on-demand video
- 2 practice tests
- Assignments & 2 articles
- Format:
- Self-paced online course
- Lifetime access to materials
- Mobile and TV compatibility
- Resources:
- 3 downloadable resources (datasets, code templates, project files)
- Role-play exercises for hands-on practice
- Certificate of completion
Additional Information
- Projects Included (9 End-to-End):
- Business workflow automation with Pandas
- Large dataset analysis with Google Apps
- Movie recommendation engine (Non-negative Matrix Factorization)
- Credit risk prediction app (XGBoost + Streamlit)
- LLM-powered AI apps (Ollama + LangChain)
- AI Code Assistant & RAG-based research tool
- Tools & Libraries Taught:
- Python: NumPy, Pandas, Matplotlib, Scikit-learn
- AI/ML: XGBoost, t-SNE, PCA, Streamlit
- LLMs: Ollama, LangChain, LM Studio, Web UI
- Career Outcomes:
- Portfolio-ready projects for job applications
- Skills applicable to data scientist, AI developer, and automation engineer roles