Introduction to Machine Learning

Description: This course is an overview and introduction to the brilliant field of machine learning and data science. This course focuses on the background and overview of machine learning, the data science workflow, the paradigms of machine learning, data preprocessing, model training and evaluation, visualization, and introduction to more advanced fields in machine learning.

Important Course Resources

1. The Hundred-Page Machine Learning Book by Andriy Burkov
2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron Aurelien
3. Machine Learning for Hackers by Drew Conway and John Myles White
4. 2024 Python Data Analysis & Visualization Masterclass [Source]
5. Stanford CS229: Machine Learning Full Course taught by Andrew Ng [Link]
6. Relevant researches and blogs in the field

How This Works

While getting education in established institutions is crucial more than anything else, it may lack some other crucial topics that are vital for real-world applications. This course aims to bridge the gap by providing supplementary lessons at the comfort of your PC or laptop or whatever device you’re browsing this on.

You can browse this free online resource that includes in-depth lessons, case studies, practical examples, and some exercises you can work on. This approach not only fosters a deeper understanding of the module, but also encourages you to try things yourself and get your hands dirty with machine learning.

We encourage participants to actively engage, ask questions, and utilize the resources provided to maximize their learning experience.

Course Organization

Section 1: Introduction to Machine Learning and AI

1.1. What is Machine Learning?

1.2. Difference Between AI, ML, and Deep Learning

1.3. Importance of ML in Science and Research

1.4. History and Evolution of Machine Learning

1.5. Key Applications of ML in Scientific Fields

Section 2: Core Concepts in Machine Learning

2.1. Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)

2.2. Components of an ML System (Data, Model, Training, Evaluation)

2.3. General Workflow of an ML Project

2.4. The Role of Data in Machine Learning

2.5. Features and Labels: Understanding Data Structure

2.6. Bias, Variance, and the Fundamental Trade-off

Section 3: Mathematical Foundations of ML

3.1. Overview of Key Mathematical Concepts in ML

3.2. Linear Algebra for Machine Learning (Vectors, Matrices, Tensors)

3.3. Probability and Statistics for Machine Learning

3.4. Optimization Basics (Gradient Descent, Cost Functions)

3.5. Understanding Functions, Models, and Hypothesis Spaces

Section 4: Essential Machine Learning Algorithms

4.1. Overview of Common ML Algorithms

4.2. Linear Models (Linear Regression, Logistic Regression)

4.3. Decision Trees and Random Forests

4.4. Introduction to Neural Networks and Deep Learning

4.5. Clustering and Dimensionality Reduction

Section 5: Model Evaluation and Performance Metrics

5.1. Why Model Evaluation is Critical in ML

5.2. Training vs. Validation vs. Test Sets

5.3. Overfitting and Underfitting

5.4. Common Performance Metrics (Accuracy, Precision, Recall, F1-Score, MSE, RMSE)

5.5. Cross-Validation and Hyperparameter Tuning Basics

Section 6: Data Preparation and Feature Engineering

6.1. The Importance of Data Preprocessing

6.2. Handling Missing Data and Outliers

6.3. Feature Scaling and Normalization

6.4. Feature Selection and Dimensionality Reduction

6.5. Encoding Categorical Data

Section 7: Introduction to ML Tools and Frameworks

7.1. Overview of ML Libraries (Scikit-Learn, TensorFlow, PyTorch)

7.2. Introduction to Python for ML

7.3. Working with Jupyter Notebooks

7.4. Basics of ML Workflow in Python

7.5. Using Pretrained Models and Open-Source Datasets

Section 8: Ethical Considerations and Limitations of ML

8.1. The Role of Ethics in Machine Learning

8.2. Bias in Machine Learning Models

8.3. Interpretability and Explainability in ML

8.4. Limitations and Challenges in Machine Learning

8.5. The Future of ML in Science and Research

Section 9: Next Steps and Learning Pathways

9.1. Building a Strong ML Foundation for Scientific Research

9.2. Recommended Resources (Books, Courses, Papers)

9.3. Practical Next Steps: Small Projects to Get Started

9.4. Transitioning to More Advanced ML Topics

9.5. How to Stay Updated with ML Trends and Research

Contact Me

You may encounter some roadblocks or challenges while navigating through this course, and questions may arise along the way. Don’t worry. If you need help, clarification, or support, please don’t hesitate to reach out to me. I’m here to help in any way I can to ensure you have a successful and enjoyable experience.

• Email: martin@chemolytics.com
• Socials: Twitter | Linkedin

I’ll do my best to respond to all inquiries within 24 to 48 hours during the weekdays. I’ll welcome your concerns, whether you have specific questions, need assistance with projects, or just simply want to discuss your progress, I’m here 😊

Additionally, if you encounter technical issues or have feedback regarding the course, please let me know. Your input is valuable and helps improve the learning experience for everyone.

Remember, seeking help is an important part of the learning process, and I am here to support you every step of the way!