This “Complete Machine Learning Course With Python” course will give learners basic indispensable Python programming skills, linear algebra, and statistical understanding. Concerning the field of discussion, participants start with supervised learning and proceed to a discussion of generative methods, discriminative methods, and support vector machines. It also goes to a fair amount of detail and depth with all the unsupervised learning methods like clustering and dimensionality reduction, and theories like ‘’Bias Variance Trade-off’’, Vapnik Chervonenkis Theory, and so on…
People have become familiar with different regression, classification, and machine learning techniques, and they have learned about performance measures such as R-squared, MSE, and accuracy. The selected course, “Complete Machine Learning Course With Python,” also focuses on applying the concepts since methodologies such as bagging, boosting, and stacking are explained to combine multiple models. Furthermore, learners learn about unsupervised machine learning, such as hierarchical and K-means clustering for data analysis.
Therefore, practical exercises use Spyder and other integrated development environments (IDE). Realization of visual optimization relies on Matplotlib and Seaborn; participants can disseminate findings easily. In addition, learners in the Complete Machine Learning Course With Python develop expertise in feature engineering necessary to improve the algorithms’ outcomes and apply the most effective model assessment methods, including train/test, K-fold, and Stratified K-fold. Focusing on the tables with the source data, the tasks and practical cases are studied based on the learned concepts REAL APPLICATIONS: Handwriting recognition with SVM staff attrition prediction using decision trees.
Complete Machine Learning Course With Python Table of Contents:
- Experiment with more than 15 machine learning algorithms and various kinds of data
- Complete more than 15 assignments that will allow practicing the lessons.
- Learn about almost all the types of models – supervised and unsupervised learning models – such as the Principle Component Analysis (PCA).
- Build and/or fine-tune high-level machine learning models to solve challenges in business environments, work positions, or life experiences
- Prepare algorithms for price prediction of houses, optical character recognition, tumor detection, and much more
Who is this course for?
- Any individual who is willing and interested in learning how to use machine learning algorithms using Python.
- All those people who want to select a career in Data Science, AI, Machine Learning, or Data Analytics.
- Anyone interested in going further than this must be able to know all about machine learning algorithms.
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