ML-LABS.AA1
Machine Learning Labs
Code a new ML solution, one line at a time, in a risk-free environment where data and algorithms become one.
- Practice in 25 Laboratorios prácticos — nothing to install
- 9 Lecciones interactivas y 43 topics mapped to the official exam objectives
Intermediate A tu propio ritmo · 1 año de acceso
25 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
01 / Habilidades que obtendrás
What you will be able to do
Let’s play with algorithms, shall we? Our Machine Learning specialization labs offer a non-production environment where you can challenge yourself with real-world activities.
You’ll tinker with data, train your own models, and watch as ML algorithms come to life.
We’ll guide you through the code and concepts. So roll up your sleeves, grab a cup of coffee, and start coding.
- Master machine learning basics and complex concepts, wrapped up in one course.
- Develop a profound understanding of data preprocessing and feature engineering to upskill.
- Implement various machine learning algorithms (regression, classification, clustering).
- Utilize Python programming for data manipulation and analysis using NumPy, Pandas, and Matplotlib.
- Build predictive models using popular libraries (Scikit-learn, TensorFlow, PyTorch).
- Fine-tune models using hyperparameter tuning and cross-validation.
- Use model performance metrics to measure accuracy, precision, recall, and F1-score.
Course Highlights
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9 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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25 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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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
9 Lecciones interactivas · 43 topics01 Pandas 7 topics · 5 Laboratorio en vivo +
- About DataFrames
- Creating DataFrames
- Interacting with DataFrame Data
- Manipulating DataFrames
- Manipulating Data
- Interactive Display
- Summary
5 Laboratorio en vivo in this lesson — see the labs panel →
02 NumPy 10 topics · 2 Laboratorio en vivo +
- Installing and Importing NumPy
- Creating Arrays
- Indexing and Slicing
- Element-by-Element Operations
- Filtering Values
- Views Versus Copies
- Some Array Methods
- Broadcasting
- NumPy Math
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
03 Visualization Libraries 6 topics · 1 Laboratorio en vivo +
- matplotlib
- Seaborn
- Plotly
- Bokeh
- Other Visualization Libraries
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
04 Machine Learning Libraries 4 topics · 2 Laboratorio en vivo +
- Popular Machine Learning Libraries
- How Machine Learning Works
- Learning More About Scikit-learn
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
05 Extracting, Transforming, and Loading Data 4 topics · 2 Laboratorio en vivo +
- Topic A: Extract Data
- Topic B: Transform Data
- Topic C: Load Data
- Summary
2 Laboratorio en vivo in this lesson — see the labs panel →
06 Designing a Machine Learning Approach 3 topics · 6 Laboratorio en vivo +
- Topic A: Identify Machine Learning Concepts
- Topic B: Test a Hypothesis
- Summary
6 Laboratorio en vivo in this lesson — see the labs panel →
07 Developing Classification Models 3 topics · 5 Laboratorio en vivo +
- Topic A: Train and Tune Classification Models
- Topic B: Evaluate Classification Models
- Summary
5 Laboratorio en vivo in this lesson — see the labs panel →
08 Developing Regression Models 3 topics · 1 Laboratorio en vivo +
- Topic A: Train and Tune Regression Models
- Topic B: Evaluate Regression Models
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
09 Developing Clustering Models 3 topics · 1 Laboratorio en vivo +
- Topic A: Train and Tune Clustering Models
- Topic B: Evaluate Clustering Models
- Summary
1 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
25 Laboratorio en vivos- Using the read_csv() Function
- Filtering a DataFrame Based on Index
- Indexing a DataFrame
- Sorting a DataFrame
- Creating a Series from a Dictionary Using pandas
- Creating a Multi-Dimensional Array Using numpy
- Creating a One-Dimensional Array Using numpy
- Creating a Scatter Plot Using matplotlib
- Using scikit-learn
- Applying Box-Cox Transformation
- Handling the Missing Values
- Performing Data Cleaning
- Performing Chi-Square Test
- Performing Two-Way ANOVA
- Calculating the Euclidean Distance between Two Series
- Performing Feature Selection Using Chi-Square Test
- Performing One-Way ANOVA
- Performing the Goodness of Fit Test
- Performing Logistic Regression
- Performing Bagging
- Creating a Decision Tree
- Creating a Confusion Matrix
- Creating a Contingency Table
- Performing Linear Regression on the Salary Dataset
- Performing K-Means Clustering
03 / Preguntas frecuentes
Preguntas antes de empezar
What is this course level?+
What programming languages will be used?+
What kind of datasets will I work with?+
What are the career opportunities for machine learning professionals?+
Enrolling in our Real-world Machine Learning course can provide numerous career benefits, such as:
- Enhanced expertise
- Improved job prospects
- Increased earning potential
- Career Advancement
- Networking opportunities
Which roles can I pursue after taking this training?+
Keep going
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Build Intelligent Systems
Join our Machine Learning labs online to develop practical skills to create powerful AI models.
- 1 año de acceso completo
- 25 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito