ML-PYTHON.AP1
Machine Learning with Python
Calling all curious minds! Start your Machine Learning with Python coding journey today, and become an expert engineer.
- Practice in 35 Laboratorios prácticos — nothing to install
- 16 Lecciones interactivas y 105 topics mapped to the official exam objectives
- 129 Preguntas del examen de práctica
Intermediate A tu propio ritmo · 1 año de acceso 4.2/5 (87 Revisar)
35 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
16Lecciones interactivas
105Topics
35Laboratorio en vivo
129Preguntas del examen de práctica
100Tarjetas didácticas
100Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
Try Free →
No se requiere tarjeta de crédito
Learn Machine Learning with Python, a comprehensive training manual that teaches the fundamentals of coding with Python. Whether you want to improve your coding skills or you want an upgrade at your workplace, this is the ideal start. In this course, you’ll master the processes, patterns, and strategies of this user-friendly programming language. This Python ML course covers supervised learning paradigms, like classical algorithms, and regression techniques to evaluate performance metrics. Besides this, you’ll also learn feature engineering for converting raw data into meaningful features. Furthermore, you’ll also leverage the Python scikit-learn library along with other powerful tools. Practice on our Labs to solidify your understanding as you explore object-oriented programming, modules, error handling, and even file operations. By the end of this course, you'll be confidently writing Python ML scripts and resolving coding issues.
- Understand the fundamentals of supervised machine learning algorithms and their classification
- Evaluating performance metrics for assessing the efficacy of your models
- Using engineer features to convert raw data into meaningful ML algorithms
- Managing system performance by creating robust pipelines
- Apply ML to various data types
- Leverage Python scikit-learn library and other tools
- Use of advanced techniques like neural networks and graphical models
Course Highlights
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16 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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35 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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129 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
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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
16 Lecciones interactivas · 105 topics01 Let’s Discuss Learning 8 topics +
- Welcome
- Scope, Terminology, Prediction, and Data
- Putting the Machine in Machine Learning
- Examples of Learning Systems
- Evaluating Learning Systems
- A Process for Building Learning Systems
- Assumptions and Reality of Learning
- End-of-Lesson Material
02 Some Technical Background 11 topics · 7 Laboratorio en vivo +
- About Our Setup
- The Need for Mathematical Language
- Our Software for Tackling Machine Learning
- Probability
- Linear Combinations, Weighted Sums, and Dot Products
- A Geometric View: Points in Space
- Notation and the Plus-One Trick
- Getting Groovy, Breaking the Straight-Jacket, and Nonlinearity
- NumPy versus “All the Maths”
- Floating-Point Issues
- EOC
7 Laboratorio en vivo in this lesson — see the labs panel →
03 Predicting Categories: Getting Started with Classification 8 topics · 1 Laboratorio en vivo +
- Classification Tasks
- A Simple Classification Dataset
- Training and Testing: Don’t Teach to the Test
- Evaluation: Grading the Exam
- Simple Classifier #1: Nearest Neighbors, Long Distance Relationships, and Assumptions
- Simple Classifier #2: Naive Bayes, Probability, and Broken Promises
- Simplistic Evaluation of Classifiers
- EOC
1 Laboratorio en vivo in this lesson — see the labs panel →
04 Predicting Numerical Values: Getting Started with Regression 6 topics · 3 Laboratorio en vivo +
- A Simple Regression Dataset
- Nearest-Neighbors Regression and Summary Statistics
- Linear Regression and Errors
- Optimization: Picking the Best Answer
- Simple Evaluation and Comparison of Regressors
- EOC
3 Laboratorio en vivo in this lesson — see the labs panel →
05 Evaluating and Comparing Learners 9 topics · 3 Laboratorio en vivo +
- Evaluation and Why Less Is More
- Terminology for Learning Phases
- Major Tom, There’s Something Wrong: Overfitting and Underfitting
- From Errors to Costs
- (Re)Sampling: Making More from Less
- Break-It-Down: Deconstructing Error into Bias and Variance
- Graphical Evaluation and Comparison
- Comparing Learners with Cross-Validation
- EOC
3 Laboratorio en vivo in this lesson — see the labs panel →
06 Evaluating Classifiers 8 topics · 4 Laboratorio en vivo +
- Baseline Classifiers
- Beyond Accuracy: Metrics for Classification
- ROC Curves
- Another Take on Multiclass: One-versus-One
- Precision-Recall Curves
- Cumulative Response and Lift Curves
- More Sophisticated Evaluation of Classifiers: Take Two
- EOC
4 Laboratorio en vivo in this lesson — see the labs panel →
07 Evaluating Regressors 6 topics · 3 Laboratorio en vivo +
- Baseline Regressors
- Additional Measures for Regression
- Residual Plots
- A First Look at Standardization
- Evaluating Regressors in a More Sophisticated Way: Take Two
- EOC
3 Laboratorio en vivo in this lesson — see the labs panel →
08 More Classification Methods 8 topics · 3 Laboratorio en vivo +
- Revisiting Classification
- Decision Trees
- Support Vector Classifiers
- Logistic Regression
- Discriminant Analysis
- Assumptions, Biases, and Classifiers
- Comparison of Classifiers: Take Three
- EOC
3 Laboratorio en vivo in this lesson — see the labs panel →
09 More Regression Methods 6 topics · 2 Laboratorio en vivo +
- Linear Regression in the Penalty Box: Regularization
- Support Vector Regression
- Piecewise Constant Regression
- Regression Trees
- Comparison of Regressors: Take Three
- EOC
2 Laboratorio en vivo in this lesson — see the labs panel →
10 Manual Feature Engineering: Manipulating Data for Fun and Profit 8 topics · 2 Laboratorio en vivo +
- Feature Engineering Terminology and Motivation
- Feature Selection and Data Reduction: Taking out the Trash
- Feature Scaling
- Discretization
- Categorical Coding
- Relationships and Interactions
- Target Manipulations
- EOC
2 Laboratorio en vivo in this lesson — see the labs panel →
11 Tuning Hyperparameters and Pipelines 6 topics +
- Models, Parameters, Hyperparameters
- Tuning Hyperparameters
- Down the Recursive Rabbit Hole: Nested Cross-Validation
- Pipelines
- Pipelines and Tuning Together
- EOC
12 Combining Learners 6 topics · 1 Laboratorio en vivo +
- Ensembles
- Voting Ensembles
- Bagging and Random Forests
- Boosting
- Comparing the Tree-Ensemble Methods
- EOC
1 Laboratorio en vivo in this lesson — see the labs panel →
13 Models That Engineer Features for Us 4 topics · 4 Laboratorio en vivo +
- Feature Selection
- Feature Construction with Kernels
- Principal Components Analysis: An Unsupervised Technique
- EOC
4 Laboratorio en vivo in this lesson — see the labs panel →
14 Feature Engineering for Domains: Domain-Specific Learning 4 topics · 1 Laboratorio en vivo +
- Working with Text
- Clustering
- Working with Images
- EOC
1 Laboratorio en vivo in this lesson — see the labs panel →
15 Connections, Extensions, and Further Directions 7 topics · 1 Laboratorio en vivo +
- Optimization
- Linear Regression from Raw Materials
- Building Logistic Regression from Raw Materials
- SVM from Raw Materials
- Neural Networks
- Probabilistic Graphical Models
- EOC
1 Laboratorio en vivo in this lesson — see the labs panel →
16 Appendix A: mlwpy.py Listing +
Laboratorios prácticos Our edge
35 Laboratorio en vivos- Plotting a Probability Distribution Graph
- Using the zip Function
- Calculating the Sum of Squares
- Plotting a Line Graph
- Plotting a 3D Graph
- Plotting a Polynomial Graph
- Using the numpy.dot() Method
- Displaying Histograms
- Defining an Outlier
- Calculating the Median Value
- Estimating the Multiple Regression Equation
- Constructing a Swarm Plot
- Using the describe() Method
- Viewing Variance
- Creating a Confusion Matrix
- Creating an ROC Curve
- Recreating an ROC Curve
- Creating a Trendline Graph
- Viewing the Standard Deviation
- Constructing a Scatterplot
- Evaluating the Prediction Error Rates
- Evaluating a Logistic Model
- Creating a Covariance Matrix
- Using the load_digits() Function
- Illustrating a Less Consistent Relationship
- Illustrating a Piecewise Constant Regression
- Manipulating the Target
- Manipulating the Input Space
- Calculating the Mean Value
- Displaying a Correlation Matrix
- Creating a Nonlinear Model
- Performing a Principal Component Analysis
- Using the Manifold Method
- Encoding Text
- Building an Estimated Simple Linear Regression Equation
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What prior knowledge is required to take this Python ML course? +
This is a beginner-friendly course and you can literally start with very basic or no prior knowledge of this coding language, and gradually build up your logic building skills as you progress.
However, it will be much easier if you have some basic knowledge of the programming concepts. And, some bit of prior coding experience with Python.
What will I learn from this Machine Learning with Python training course? +
This ML training course will transform you from a curious onlooker to a machine learning expert. There’s a lot you’ll be learning:
- Supervising ML algorithms; classification (spam filters), and regression (predicting prices)
- Build models, assessing their performances, and delivering results
- Master feature engineering
- Exploring data diversity
- Leveraging Scikit-learn and other python tools
What ML Python algorithms will I learn in this course?+
You’ll learn these 2 algorithm categories:
- Classification - Support Vector Machines (SVM) * Random Forests * K-Nearest Neighbors (KNN) * Logistic Regression
- Regression* Linear Regression * Decision Tree Regression
Are deep learning contents covered in this course?+
No, this python course majorly focuses on core fundamentals of ML concepts and algorithms.
Is there a special IDE recommended for this online ML course?+
There isn’t any one particular IDE recommended for this course. Some of the most popular IDE options for python include Jupyter Notebook, PyCharm, and Visual Studio Code (VS code).
Who should enroll for this ML learning course? +
This course is perfect for all those who want to become expert ML engineers, or those wanting to solidify their understanding of Python fundamentals and Machine Learning algorithms.
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- 1 año de acceso completo
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