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\n\tWhat will you learn?<\/h2><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n\n\t\t\n\t\t\t
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\n\tAfter the training, you will be able to:<\/p>\t
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Perform exploratory data analysis on your datasets with pandas<\/p><\/p><\/div>\n\n\n\n
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Train and evaluate machine learning models with scikit-learn.<\/p><\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tIdentify a suitable machine learning algorithm and metric for your data problem.<\/p><\/div>\n\n\n\n
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Prepare complex data for machine learning with techniques such as scaling, encoding, and imputing.<\/p><\/p><\/div>\n\n\n\n
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\n\t <\/svg><\/i>\n\tApply best practices for data wrangling and model building.<\/p><\/div>\n\n\t<\/div>\n<\/div>\n\t\t<\/div>\n\t<\/div>\n\n<\/div>\n\n<\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n
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\n\tProgram<\/h2><\/div>\n\t\t<\/div>\n\t<\/div>\n\n<\/div>\n\n\n\n\n\t\n\n
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\n\t\t\n\tDay 1<\/h2>\t\t\n\t\t\n\t\t\t\n\t <\/svg><\/i>\t\t<\/span>\n\t<\/button>\n\n\t\n\t\t
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\n\tMaster exploratory data analysis with Pandas<\/strong><\/p><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n\n\t\t\n\t\t\t
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\n\tFetch descriptive summary statistics of your data with simple operations<\/li> Effectively select and filter parts of your data with loc<\/em><\/li>Retrieve advanced statistics with group by<\/em> aggregations<\/li>Extend your dataset by creating new columns with assign<\/em>\u00a0<\/li>Structure your code neatly by chaining methods\u00a0<\/li><\/ul><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n\n\t\t\n\t\t\t
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\n\tIntroduction to machine learning<\/strong><\/p><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n\n\t\t\n\t\t\t
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\n\tIdentify the type of machine learning task (classification or regression, supervised or unsupervised, and others)<\/li> Use scikit-learn to train a classification model<\/li> Understand how to evaluate your model’s effectiveness with various metrics (such as precision & recall, F1, root mean squared error, r2)<\/li><\/ul><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\t\t<\/div>\n\t<\/div>\n<\/div>\n\n\n\n\n\t
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\n\tMachine Learning Theory<\/strong><\/p><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n\n\t\t\n\t\t\t
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\n\tDifferentiate between several machine learning algorithms (such as linear regression, decision tree, support vector machine)<\/li> Create models that generalize (underfitting and overfitting, train-test split, k-fold cross-validation)<\/li><\/ul><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n\n\t\t\n\t\t\t
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\n\tMachine Learning with Scikit-Learn<\/strong><\/p><\/div>\n\t\t<\/div>\n\t<\/div>\n\n\n\n