Python for AI with Scikit-Learn

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Mentor Based

Program Syllabus

Module 1

About This Course

The Advanced Python for AI with Scikit-Learn program is designed for high-level academics and professionals in data science and artificial intelligence. It offers specialized training in Python programming and machine learning, focusing on the powerful capabilities of Scikit-learn for predictive modeling and AI-driven data analysis.

Module 2

Aim

This program aims to equip PhD scholars and academicians with an in-depth understanding of Python and Scikit-learn as tools for developing advanced AI applications. Participants will master data manipulation, machine learning model development, and algorithm optimization to drive innovation and research in AI.

Module 3

Program Objectives

Develop expert-level Python programming skills tailored to AI applications.
Master the use of Scikit-learn for building and optimizing machine learning models.
Apply machine learning and AI principles to solve real-world data challenges.
Understand and implement advanced techniques in natural language processing.
Lead AI projects and collaborations in academic or industry settings.

Module 4

Program Structure

Python Programming for AI
- Advanced Python features and libraries for AI.
- Efficient data structures and algorithms for AI applications.
Data Handling and Analysis with Pandas and NumPy
- Manipulating and processing data using Pandas.
- Numerical operations for AI with NumPy.
Machine Learning Foundations with Scikit-Learn
- Supervised learning techniques: Regression and Classification.
- Unsupervised learning techniques: Clustering and Dimensionality Reduction.
Model Optimization and Evaluation
- Hyperparameter tuning using GridSearchCV and RandomizedSearchCV.
- Performance metrics and model evaluation strategies.
Ensemble Methods and Advanced Modeling
- Implementing ensemble methods for robust predictions.
- Advanced machine learning models for complex problem-solving.
Natural Language Processing with Scikit-Learn and NLTK
- Text data preprocessing and feature extraction.
- Building and tuning NLP models for text classification and sentiment analysis.
Capstone Project
- Applying learned techniques to a real-world AI challenge.
- Collaboration with industry experts and academic peers.

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.