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The course covers topics such as linear regression, logistic regression, and decisiontrees. Machine Learning for Absolute Beginners by Kirill Eremenko and Hadelin de Ponteves This is another beginner-level course that teaches you the basics of machine learning using Python.
D Data Mining : The process of discovering patterns, insights, and knowledge from large datasets using various techniques such as classification, clustering, and association rule learning. DataWrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.
Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decisiontrees, random forests, support vector machines, and neural networks. Differentiate between supervised and unsupervised learning algorithms.
Explore Machine Learning with Python: Become familiar with prominent Python artificial intelligence libraries such as sci-kit-learn and TensorFlow. Begin by employing algorithms for supervisedlearning such as linear regression , logistic regression, decisiontrees, and support vector machines.
Data Cleaning and Transformation Techniques for preprocessing data to ensure quality and consistency, including handling missing values, outliers, and data type conversions. Students should learn about datawrangling and the importance of data quality.
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