– The course provides students with important and necessary knowledge about Machine Learning, a very “hot” branch of Artificial Intelligence (AI).
– Equip knowledge and skills to apply the algorithms of Supervised Learning (Classification, Regression), Unsupervised Learning (Clustering, Association Analysis, Dimensionality Reduction) algorithms through the use of powerful libraries, tools, code Open source like Python, Jupyter Notebooks, Numpy, Pandas, Matplotlib, Seaborn, sklearn…
– Undertake specific projects in the context of solving compelling data science problems
– Build a solid foundation in Machine Learning with Python, creating a premise for learning about Deep Learning.
Duration: 40 hours.
– Students of universities and colleges
– Students with orientation will work in the field of Machine Learning or Data Science
Upon completion of the course, students will gain the following skills:
– Apply and implement Supervised Learning group algorithms such as Logistic Regression, Linear Regression, Naïve Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM), Boosting and AdaBoost with Python
– Apply and implement algorithms in Unsupervised Learning group such as K-Means clustering, Hierarchical Clustering, Apriori, Equivalence Class Clustering and bottom up Lattice Traversal (ECLAT), Expectation–maximization (EM), Gaussian Mixture Models (GMM), Dimensionality Reduction with Principal Component Analysis (PCA), Locally Linear Embedding (LLE) with Python
– Understand and apply Machine Learning algorithms in solving concrete, real-world problems
If you have any questions or would like more information about the course, please contact us.