Machine Learning from Scratch
Building Machine Learning Algorithms from First Principles
Machine Learning from Scratch
Machine Learning from scratch refers to the process of building machine learning algorithms and models without relying on high-level libraries or pre-built frameworks like TensorFlow or scikit-learn. This approach involves understanding the foundational concepts of machine learning, such as linear algebra, probability, and statistics, and implementing algorithms directly using programming languages such as Python or R. By coding algorithms like linear regression, decision trees, or neural networks from the ground up, practitioners gain deeper insights into their mechanics, strengths, limitations, and the intricacies of the training process, such as optimization and model evaluation. This hands-on experience fosters a more profound comprehension of how these models work and enables tailored solutions for specific problems, although it requires more effort compared to using established libraries.
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1 - Introduction to Machine Learning: Understand the definition, types (supervised, unsupervised, reinforcement), and real world applications of machine learning.
2) Basic Concepts of Data: Learn about data sources, types of data (structured vs unstructured), and the importance of data quality in ML.
3) Mathematics for Machine Learning: Cover essential mathematical concepts including linear algebra, calculus, probability, and statistics vital for understanding ML algorithms.
4) Data Preprocessing: Explore techniques for cleaning, transforming, and normalizing data, including handling missing values and categorical variables.
5) Programming Foundations: Gain familiarity with Python programming, essential libraries like NumPy, Pandas, and Matplotlib, and basic coding practices.
6) Building a Simple Linear Regression: Start from scratch to implement linear regression, understand the cost function, and learn about gradient descent optimization.
7) Introduction to Classification: Learn concepts of classification problems and implement a basic algorithm like logistic regression.
8) Understanding Evaluation Metrics: Discuss various metrics to evaluate model performance such as accuracy, precision, recall, F1 score, and ROC AUC.
9) Overfitting and Underfitting: Understand these fundamental concepts, and learn techniques to prevent overfitting like regularization and cross validation.
10) Decision Trees and Random Forests: Build decision tree classifiers from the ground up and understand ensemble methods for improving accuracy.
11) Support Vector Machines (SVM): Explore SVMs for classification tasks, understand the geometric interpretations, and how kernels work to transform data.
12) Introduction to Neural Networks: Discuss the basics of neural networks including architecture, activation functions, and backpropagation.
13) Deep Learning Essentials: Brief overview of deep learning principles, convolutional networks (CNNs) for image tasks, and recurrent networks (RNNs) for sequence tasks.
14) Model Deployment Basics: Learn about how to save models, versioning, and deploying machine learning models into production environments.
15) Ethics in Machine Learning: Discuss the ethical implications of machine learning, bias in algorithms, and the importance of fairness and transparency.
16) Real World Projects: Engage in hands on projects that reinforce learning, allowing students to apply their skills to solve practical problems using machine learning.
17) Collaboration and Group Work: Promote teamwork by having students work in groups to brainstorm, share ideas, and tackle challenges together.
18) Final Assessments and Presentations: Capstone projects where students present their findings and models, simulating real world project delivery.
This structure not only covers foundational theories and algorithms but also emphasizes practical application and ethical considerations in machine learning, making for a comprehensive training program for students.
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