Course Overview
An intensive, end-to-end Data Science and Artificial Intelligence Computational Thinking (CT) program. Master Python data science libraries (NumPy, Pandas, Matplotlib, Scikit-Learn), machine learning models, PyTorch deep learning, and LLM GenAI integration.
What You'll Learn
- Process, clean, and visualize complex datasets using Python Pandas and Seaborn
- Train supervised and unsupervised machine learning models (Regression, Random Forest, XGBoost)
- Build Convolutional and Recurrent Neural Networks using PyTorch and TensorFlow
- Deploy GenAI applications powered by LangChain, OpenAI API, and Streamlit
Key Skills Covered
Structured Learning Roadmap
Step-by-step milestone progression for Data Science/Artificial Intelligence CT Program - Holistic Learning Approach!!!
Establish prerequisite knowledge in Data Science, setup local development environments, and master fundamental syntax and concepts.
NumPy arrays, Pandas DataFrames, exploratory data analysis (EDA), data wrangling. Linear/Logistic regression, decision trees, SVM, clustering, hyperparameter tuning.
Perceptrons, backpropagation, PyTorch CNNs, image classification, transfer learning. Transformers, LLM fine-tuning, LangChain RAG pipelines, Streamlit app deployment.
Synthesize all course modules into a production-grade portfolio project, undergo self-assessment quizzes, and earn your official Money Mitra Network certificate.
Course Curriculum
Module 1: Python Data Science & Computational Thinking
Module 2: Machine Learning Algorithms & Optimization
Module 3: Deep Learning Neural Networks & Computer Vision
Module 4: Generative AI, RAG & Cloud Deployment
Instructor Profile
Dr. Meera Nair
Ph.D. in Computer Science with 14+ years designing scalable machine learning algorithms, database engines, and analytics pipelines.
Prerequisites & Audience
Basic programming logic. High school level algebra.
Aspiring data scientists, AI engineers, software developers, and research scholars.