Net Course
Unlock the full potential of your tech career with the Net Course, adapted for Eritrean technology professionals eager to master text classification and data analysis. Delve into model training, evaluation, and optimization through practical exercises in cross-validation, feature selection, and hyperparameter tuning. Strengthen your expertise in data preprocessing, feature extraction, and machine learning models such as Naive Bayes and SVM. Acquire valuable insights into ethical data collection practices and comprehensive documentation, ensuring you're well-prepared for real-world challenges in the Eritrean context.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Unlock the full potential of your tech career with the Net Course, adapted for Eritrean technology professionals eager to master text classification and data analysis. Delve into model training, evaluation, and optimization through practical exercises in cross-validation, feature selection, and hyperparameter tuning. Strengthen your expertise in data preprocessing, feature extraction, and machine learning models such as Naive Bayes and SVM. Acquire valuable insights into ethical data collection practices and comprehensive documentation, ensuring you're well-prepared for real-world challenges in the Eritrean context.
Elevify advantages
Develop skills
- Master data preprocessing techniques: Tokenize, normalize, and efficiently clean text data relevant to Eritrean datasets.
- Optimize models for optimal performance: Implement feature selection and hyperparameter tuning strategies.
- Evaluate model performance rigorously: Utilize accuracy, precision, recall, and F1-score metrics for thorough assessment.
- Extract key features from text: Apply TF-IDF, Word2Vec, and Bag of Words techniques to Eritrean text corpora.
- Build robust classifiers: Develop Naive Bayes, SVM, and neural network models suitable for Eritrean applications.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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