MLS-PYTHON.AW1
Building Machine Learning Systems using Python
Learn how to build smart systems, boost your skills in this interactive Python machine learning course, and set the foundation for a promising career.
- 12 Lecciones interactivas y 64 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
12Lecciones interactivas
64Topics
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
Enroll in our Python machine learning course to build smart, real-world models that actually work and leverage data.
In this machine learning with Python course, dive into regression, classification, clustering, and neural networks. Learn the ins and outs of key algorithms like Random Forest, SVM, and PCA…also, how to avoid common pitfalls like overfitting and bias.
From basic concepts to advanced techniques, you’ll get hands-on with Python and scikit-learn.
- Building & Deploying ML Models: Develop and fine-tune predictive models using Regression, Classification, and Clustering techniques.
- Hands-on Python for ML: Master scikit-learn, data preprocessing, and model evaluation with real-world datasets.
- Algorithm Expertise: Implement key ML algorithms like Decision Trees, SVM, Random Forest, and Neural Networks.
- Data Optimization: Prevent overfitting, apply regularization, and improve model accuracy using best practices.
- Unsupervised Learning: Work with clustering (K-means, Hierarchical) and dimensionality reduction (PCA).
- Bias Detection & Fairness: Identify and mitigate biases in ML models for ethical AI development.
Course Highlights
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12 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
12 Lecciones interactivas · 64 topics01 Preface +
02 Introduction 6 topics +
- History of machine learning
- Classification of machine learning
- Challenges faced in adopting machine learning
- Applications
- Conclusion
- Questions
03 Linear Regression 6 topics +
- Linear regression in one variable
- Linear regression in multiple variables
- Gradient descent
- Polynomial regression
- Conclusion
- Questions
04 Classification Using Logistic Regression 6 topics +
- Introduction
- Binary classification
- Logistic regression
- Multiclass classification
- Conclusion
- Questions
05 Overfitting and Regularization 4 topics +
- Overfitting and regularization in linear regression
- Overfitting and regularization in logistic regression
- Conclusion
- Questions
06 Feasibility of Learning 5 topics +
- Introduction
- Feasibility of learning an unknown target function
- In-sample error and out-of-sample error
- Conclusion
- Questions
07 Support Vector Machine 5 topics +
- Introduction
- Margin and Large Margin methods
- Kernel methods
- Conclusion
- Questions
08 Neural Network 7 topics +
- Introduction
- Early models
- Perceptron learning
- Back propagation
- Stochastic Gradient Descent
- Conclusion
- Questions
09 Decision Trees 8 topics +
- Introduction
- Decision trees
- Regression trees
- Stopping criterion and pruning loss functions in decision trees
- Categorical attributes, multiway splits, and missing values in decision trees
- Instability in decision trees
- Conclusion
- Questions
10 Unsupervised Learning 6 topics +
- Introduction
- Clustering
- K-means clustering
- Principal Component Analysis (PCA)
- Conclusion
- Questions
11 Theory of Generalization 6 topics +
- Introduction
- Training versus testing
- Bounding the testing error
- VC dimension
- Conclusion
- Questions
12 Bias and Fairness in Machine Learning 5 topics +
- Introduction
- How to detect bias?
- How to fix biases or achieve fairness in ML?
- Conclusion
- Questions
03 / Preguntas frecuentes
Preguntas antes de empezar
Can I learn machine learning with Python?+
Yes! Python is the most popular language for machine learning due to its simplicity and powerful libraries like scikit-learn, TensorFlow, and PyTorch. This course will teach you ML concepts and hands-on implementation using Python.
Can I learn ML in 1 month?+
You can learn the basics of ML in 1-6 months with focused study, but mastering it takes longer. This Python for machine learning course provides structured learning to help you build a strong foundation quickly.
What is the best way to learn Python?+
The best way is through hands-on practice. Start with basics (syntax, loops, functions), then work on projects. This course includes Python coding exercises for ML, helping you learn by doing.
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