Machine Learning Course for Beginners: 4.0 Machine Learning Roadmap and Theory

Overview

Instructor: Todd, an experienced data science professional
Target Audience: Beginners in 2024
Focus: Demystify machine learning and bridge gaps in educational resources
Structure: Theory, hands-on applications, full end-to-end project using Python
Platform: Lun Tech, includes free resources (website/Youtube channel)

Section Breakdown

Introduction to Machine Learning

Definition and Real-world Applications
- Healthcare: Diagnosing diseases, drug discovery, personalized medicine, hospital operations
- Finance: Fraud detection, trading (quantitative finance)
- Retail: Demand estimation, optimized operations, recommendation systems (e.g., Amazon)
- Marketing: Targeting tactics to reduce marketing costs
- Autonomous Vehicles: Deep learning applications in self-driving technology
- Natural Language Processing (NLP): Chatbots, virtual assistants (e.g., ChatGPT)
- Agriculture: Weather condition estimation, crop yield optimization
- Entertainment: Recommendation systems in platforms like Netflix

Machine Learning Roadmap

Skills Required: Mathematics (Linear Algebra, Calculus, Discrete Mathematics)
- Linear Algebra: Matrix operations, transformations, identity matrix, etc.
- Calculus: Differentiation (chain rule, sum rule, product rule), integration theory
- Discrete Mathematics: Graph theory, combinations, complexity
- Additional: Basic statistics and high school math concepts
Statistics: Descriptive, inferential, probability distributions, statistical thinking
- Descriptive: Mean, median, standard deviation, etc.
- Inferential: Central limit theorem, hypothesis testing, confidence intervals
- Probability Distributions: Binomial, normal, uniform distributions, etc.
- Statistical Thinking: Baysian statistics, theorem, probabilities
Machine Learning Fundamentals: Theory and popular algorithms
- Types: Supervised vs unsupervised, semi-supervised
- Algorithms: Linear regression, logistic regression, LDA, KNN, decision trees, random forest, boosting models, etc.
- Training: Model training, hyperparameter tuning, resampling techniques
Python Programming: Libraries for data science and ML (Pandas, NumPy, Sci-kit learn, TensorFlow, PyTorch)
- Data Structures: Arrays, matrices, lists, indexing, sets, etc.
- Data Processing: Handling missing data, duplication, feature engineering, etc.
- Visualization: Matplotlib, Seaborn libraries
NLP Basics: Working with text data, cleaning, embeddings, TF-IDF, word embeddings
- Text Data Manipulation: Tokenization, stemming, lemmatization, stop words
- Advanced Applications: Transformers, attention mechanisms, LSTM, RNN, etc.

Projects and Practical Applications

Recommended System: Job/movie recommendation system
Regression Models: Predicting job salaries using regression algorithms
Classification Models: Classify emails as spam or not
Unsupervised Learning: Customer segmentation based on transaction history
Advanced Projects: Large language models, Baby GPT implementation

Career Paths and Industry Insights

Common Career Paths:
- Machine Learning Researcher
- Machine Learning Engineer
- AI Researcher/Engineer
- NLP Researcher/Engineer
- Data Scientist
- Data Science Engineer
Industries: Various from healthcare to entertainment, finance to agriculture
Salaries: Discusses average salaries across different roles and industries
Resources: Preparation for interviews, courses, and on-hand resources

Key Steps to Learning

Theory: Start with basic theory and concepts
Math & Statistics: Strengthen foundational knowledge
Python & Libraries: Learn and practice library usage
Practice: Engage in practical projects
Advance Further: Explore advanced models and techniques
Career Prep: Arm with interview prep courses and real-world project practices

Tools & Resources

Detailed step-by-step guides and courses at Lun Tech
Free and paid resources to further learning
GitHub and LinkedIn accounts showcasing practical examples
Regular updates and newsletters from progressions in tech