Machine Learning
This practical machine learning course equips you with Python programming and ML algorithms (supervised/unsupervised learning) to solve real-world problems like stock prediction, recommendation systems, and sentiment analysis. Designed for the AI-driven future, it prepares you for emerging roles as ML transforms industries balancing automation risks with new opportunities through hands-on training in cutting-edge data analysis.
Provided by: Prishtina REA
Course Description
The course aims to equip learners with the knowledge and skills to apply machine learning techniques to solve real-world problems. By the end of the course, learners will be able to implement machine learning models, develop an intuition for prediction and analysis, and deploy solutions in production environments.
The course emphasizes practical, hands-on experience and understanding the theoretical foundations of machine learning. This course is designed to provide both theoretical knowledge and practical experience in the field of machine learning. Students will gain a solid understanding of supervised and unsupervised learning techniques and their applications in real-world scenarios. Practical topics such as Python programming for ML, data preprocessing, and algorithm implementation will be covered in depth.
This course is designed for individuals who have a basic understanding of programming and mathematics and are interested in learning machine learning techniques.
Course Length: 250 hours
Course Delivery: Online
Fee: Free
Course details
Deep tech fields
Artificial Intelligence & Machine Learning (including Big Data)
Cybersecurity & Data Protection
Robotics
Internet Of Things | W3C | Semantic Web
Course language
English
Course certification
EU Support for the Competitiveness of Kosovo’s ICT Sector
Fee
Free course
Duration (hours)
250
Certificate provided
Yes
Skills addressed
machine learning, supervised learning, unsupervised learning, reinforcement learning, regression analysis, Naive Bayes, Support Vector Machines, K-Nearest Neighbors, Bayesian methods, bootstrap, resampling, deep learning; neural networks; optimization techniques, Python programming, NumPy, Pandas, Scikit-learn, predictive modelling, model evaluation, cross-validation, feature engineering, statistical analysis, sentiment analysis, recommendation systems, time-series forecasting; hyperparameter tuning, gradient descent, Jupyter Notebooks, data preprocessing, classification, clustering; dimensionality reduction; overfitting prevention; ensemble methods; performance metrics; confusion matrix; ROC curves, precision-recall; bias-variance trade-off.
Course format
Online
Target group
Undergraduate-level learners, Postgraduate-level learners, Professional development learners, Life-long learners
Quality check
Approved
Dates
Current no dates scheduled
Course provider
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