Machine Learning Course for Beginners - Lecture Notes

Overview

• Course created for beginners in 2024
• Starts with Machine Learning roadmap for 2024
  • Emphasis on career paths and beginner-friendly theory
• Progresses to hands-on practical applications
• Concludes with a comprehensive end-to-end project using Python
• Instructor: Todd, experienced data science professional
  • Aim: Demystify machine learning concepts and bridge educational gaps

Course Structure

Introduction to Machine Learning

• Basics of machine learning
• Implementation in real-world case studies
• Free resources available on Lun Tech's website and YouTube channel

Machine Learning Roadmap for 2024

• Required skill sets to get into machine learning
• Topics include machine learning definitions, career paths, and resources

Theory and Fundamentals

• Basics and fundamentals of machine learning
• Introduction to statistics and mathematics necessary for machine learning
  • Linear algebra, calculus, discrete mathematics
  • Key concepts: Matrix multiplication, derivatives, probabilities
• Importance of statistics
  • Descriptive, inferential statistics, and probability distributions

Practical Applications

• Use of data science libraries in Python (Pandas, NumPy, sci-kit learn, matplotlib, Seaborn)
• Building a machine learning model for Californian house prices
  • Linear regression for causal and predictive analysis
  • Data preprocessing: cleaning, visualization, handling missing data and outliers
• Evaluation of machine learning models
  • Performance metrics like MSE, RMSE, MAE, F1 score, Precision, Recall, etc.

Advanced Topics in Machine Learning

• Introduction to NLP, Deep Learning, and generative AI
  • RNNs, GANs, LSTMs, CNNs
  • Transformers, BERT, GPT-3
• Practical projects to build a portfolio
  • Recommender systems, regression models, classification models, clustering

Career Paths and Industry Applications

• Machine learning in various industries: healthcare, finance, retail, entertainment, etc.
• Average salaries for machine learning roles
• Types of roles: Data Scientist, Machine Learning Engineer, AI Specialist

Resources and Next Steps

• Recommended resources for further learning
  • Statistics courses, machine learning handbooks
• Preparing for machine learning and deep learning interviews
  • Focus on important concepts and hands-on projects

Course Case Study: Californian House Prices

• Step-by-step approach
  • Data loading and preprocessing
  • Exploratory data analysis
  • Linear regression implementation
  • Results interpretation and evaluation
• Key insights
  • Features influencing housing prices
  • Visualization and handling of key trends

Final Remarks

• Encouragement to practice and explore further
• Importance of starting simple and building strong foundations

Next Steps in Learning

• Consider learning more advanced models and techniques
  • Decision trees, random forests, boosting, clustering
• Dive deeper into topics like deep learning and generative AI

Tools and Libraries to Know

• Pandas, NumPy, Scikit-learn, Seaborn, Matplotlib
• Advanced: TensorFlow, PyTorch, NLTK