ChatGPT・生成AI時代の今だからこそ学びたい!人工知能・機械学習入門講座(教師あり学習編)

Master AI & Machine Learning: Build Predictive Models with Python & Scikit-learn

Unlock the power of Artificial Intelligence (AI) and Machine Learning (ML) with this comprehensive, hands-on course! Learn to build robust predictive models using Python and the powerful Scikit-learn library, all within the accessible Google Colab environment. This course bridges the gap between theory and practical application, equipping you with the skills to excel in the rapidly evolving field of AI.

What You'll Learn:

This course is structured into two parts: a foundational overview and a practical implementation phase. We'll cover the core concepts of AI and ML, focusing on supervised learning and model creation. Then, you'll dive into hands-on projects, building and refining regression and classification models.

Key Topics Covered:

• AI & Machine Learning Fundamentals:
  • Understanding Artificial Intelligence (AI): Explore specialized vs. general AI (AGI).
  • Demystifying Machine Learning (ML): Learn about Large Language Models (LLMs), OpenAI, Generative AI, and ChatGPT.
  • Explore various ML types: Supervised Learning, Unsupervised Learning, Deep Learning, Reinforcement Learning, and Ensemble Methods.
  • The Machine Learning Process: A step-by-step breakdown of model development.
  • Essential ML Skills: A look at the tools and knowledge you need to succeed.
• Google Colab & Python Essentials:
  • Getting started with Google Colab: A powerful cloud-based environment for coding.
  • Python for Data Science: Master the fundamentals of Python programming.
  • Scikit-learn: A deep dive into the core ML library for model building.
  • Data manipulation with Pandas and visualization with Matplotlib and NumPy.
• Supervised Learning in Detail:
  • Regression Models:
    • Understanding Linear Regression: Learn about Ordinary Least Squares (OLS) and Gradient Descent.
    • Exploring Decision Trees and Random Forests.
    • Introduction to Neural Networks.
  • Classification Models:
    • Logistic Regression: Understand Maximum Likelihood Estimation (MLE).
    • Support Vector Machines (SVM): Master linear and non-linear classification with kernel functions and the kernel trick.
    • Decision Trees and Gain Calculation.
    • Gini Coefficient.
  • Data Preparation:
    • Data Preprocessing: Essential techniques for cleaning and transforming data.
    • Data Splitting: Understand train-test split (hold-out method) and cross-validation (k-fold cross-validation).
  • Model Evaluation:
    • Regression Evaluation Metrics: R-squared, Mean Squared Error (MSE), and Mean Absolute Error (MAE).
    • Classification Evaluation Metrics: Accuracy, Precision, Recall, F1-score, and Confusion Matrix.
  • Model Improvement:
    • Feature Scaling: Standardization and handling Multicollinearity (using Correlation Coefficient and VIF).
    • Dimensionality Reduction: PCA (Principal Component Analysis) and Lasso Regression.
    • Addressing Overfitting: Using cross-validation, hyperparameter tuning, and algorithm selection.
• Hands-on Projects:
  • Regression Project: Build a predictive model for real-world scenarios.
  • Classification Project: Develop a model to classify data into distinct categories.
• Bonus Lecture: Gain insights into the ethical considerations and societal impact of AI.

Why Choose This Course?

• Self-Contained Learning: No need to search for external resources – everything you need is within this course.
• Detailed Explanations: We explain why we use specific techniques and code, fostering a deeper understanding.
• Downloadable Resources: Access all datasets and completed code for hands-on practice and troubleshooting.
• Practical Skills: Develop the skills to build and deploy AI models, and enhance your career prospects.

Gain a Competitive Edge:

This course isn't just about technical skills. It also equips you with the mindset and strategic thinking needed to succeed as an AI professional. You'll gain insights into:

• AI Team Management: Learn to lead and manage effective