Course Details
Machine Learning with Python
This training course is designed to introduce participants to the fundamentals of machine learning and how to use Python to build machine learning models. Over the course of 5 days, participants will learn how to use popular machine learning libraries such as Scikit-learn and Tensorflow to create and evaluate machine learning models. The training will cover topics such as supervised and unsupervised learning, regression analysis, classification algorithms, and clustering techniques. Participants will also learn how to preprocess and clean data for machine learning, how to evaluate the performance of machine learning models, and how to optimize models for better accuracy. Throughout the course, participants will engage in hands-on exercises and projects to reinforce their understanding of machine learning concepts and their application to real-world problems. By the end of the training, participants will have a solid foundation in machine learning with Python and be able to apply this knowledge to real-world data problems. Overall, this training course will provide participants with the skills and knowledge needed to build and deploy machine learning models using Python, and to pursue further training and education in the field of machine learning.
Learning Outcomes:
- Participants will have gained a comprehensive understanding of the fundamentals of machine learning, including supervised and unsupervised learning, regression analysis, classification algorithms, and clustering techniques. - Participants will gained experience in using popular machine learning libraries, and will have learned how to preprocess, clean, and evaluate data for machine learning. - Participants will have had the opportunity to engage in hands-on exercises and projects throughout the course, allowing them to apply the knowledge and skills they have gained to real-world problems. This experience will enable them to confidently build and evaluate machine learning models in their future work.
Course Outline:
Overview of machine learning and its applications
Introduction to Python for machine learning
Setting up a Python environment for machine learning
Linear regression
Classification algorithms (e.g. logistic regression, k-NN)
Decision trees
Clustering algorithms (e.g. k-means, hierarchical clustering)
Dimensionality reduction (e.g. principal component analysis)
Introduction to neural networks
Building neural networks with Tensorflow
Training and fine-tuning neural networks
Evaluating model accuracy
Hyperparameter tuning and optimization
Deploying models for real-world applications
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