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Working in a hands-on learning environment, led by our Scikit-learn Cookbook expert instructor, students will learn about and explore: Handle a variety of machine learning tasks effortlessly by leveraging the power of scikit-learn.Perform supervised and unsupervised learning with ease, and evaluate the performance of your model.Practical, easy to understand recipes aimed at helping you choose the right machine learning algorithm.

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February 5, 2021

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Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This course includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively. The edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the lessons, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Naïve Bayes, classification, decision trees, Ensembles and much more. Furthermore, you’ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the lesson also contains recipes on evaluating and fine-tuning the performance of your model. By the end of this course, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.

Course Curriculum

    • High-Performance Machine Learning – NumPy 00:00:00
    • Introduction 00:00:00
    • NumPy basics 00:00:00
    • Loading the iris dataset 00:00:00
    • Viewing the iris dataset 00:00:00
    • Viewing the iris dataset with Pandas 00:00:00
    • Plotting with NumPy and matplotlib 00:00:00
    • A minimal machine learning recipe – SVM classification 00:00:00
    • Introducing cross-validation 00:00:00
    • Putting it all together 00:00:00
    • Machine learning overview – classification versus regression 00:00:00
    • Pre-Model Workflow and Pre-Processing 00:00:00
    • Introduction 00:00:00
    • Creating sample data for toy analysis 00:00:00
    • Scaling data to the standard normal distribution 00:00:00
    • Creating binary features through thresholding 00:00:00
    • Working with categorical variables 00:00:00
    • Imputing missing values through various strategies 00:00:00
    • A linear model in the presence of outliers 00:00:00
    • Putting it all together with pipelines 00:00:00
    • Using Gaussian processes for regression 00:00:00
    • Using SGD for regression 00:00:00
    • Dimensionality Reduction 00:00:00
    • Introduction 00:00:00
    • Reducing dimensionality with PCA 00:00:00
    • Using factor analysis for decomposition 00:00:00
    • Using kernel PCA for nonlinear dimensionality reduction 00:00:00
    • Using truncated SVD to reduce dimensionality 00:00:00
    • Using decomposition to classify with DictionaryLearning 00:00:00
    • Doing dimensionality reduction with manifolds – t-SNE 00:00:00
    • Testing methods to reduce dimensionality with pipelines 00:00:00
    • Linear Models with scikit-learn 00:00:00
    • Introduction 00:00:00
    • Fitting a line through data 00:00:00
    • Fitting a line through data with machine learning 00:00:00
    • Evaluating the linear regression model 00:00:00
    • Using ridge regression to overcome linear regression’s shortfalls 00:00:00
    • Optimizing the ridge regression parameter 00:00:00
    • Using sparsity to regularize models 00:00:00
    • Taking a more fundamental approach to regularization with LARS 00:00:00
    • References 00:00:00
    • Linear Models – Logistic Regression 00:00:00
    • Introduction 00:00:00
    • Loading data from the UCI repository 00:00:00
    • Viewing the Pima Indians diabetes dataset with pandas 00:00:00
    • Looking at the UCI Pima Indians dataset web page 00:00:00
    • Machine learning with logistic regression 00:00:00
    • Examining logistic regression errors with a confusion matrix 00:00:00
    • Varying the classification threshold in logistic regression 00:00:00
    • Receiver operating characteristic – ROC analysis 00:00:00
    • Plotting an ROC curve without context 00:00:00
    • Putting it all together – UCI breast cancer dataset 00:00:00
    • Building Models with Distance Metrics 00:00:00
    • Introduction 00:00:00
    • Using k-means to cluster data 00:00:00
    • Optimizing the number of centroids 00:00:00
    • Assessing cluster correctness 00:00:00
    • Using MiniBatch k-means to handle more data 00:00:00
    • Quantizing an image with k-means clustering 00:00:00
    • Finding the closest object in the feature space 00:00:00
    • Probabilistic clustering with Gaussian mixture models 00:00:00
    • Using k-means for outlier detection 00:00:00
    • Using KNN for regression 00:00:00
    • Cross-Validation and Post-Model Workflow 00:00:00
    • Introduction 00:00:00
    • Selecting a model with cross-validation 00:00:00
    • K-fold cross validation 00:00:00
    • Balanced cross-validation 00:00:00
    • Cross-validation with ShuffleSplit 00:00:00
    • Time series cross-validation 00:00:00
    • Grid search with scikit-learn 00:00:00
    • Classification metrics 00:00:00
    • Regression metrics 00:00:00
    • Clustering metrics 00:00:00
    • Using dummy estimators to compare results 00:00:00
    • Feature selection 00:00:00
    • Feature selection on L1 norms 00:00:00
    • Persisting models with joblib or pickle 00:00:00
    • Support Vector Machines 00:00:00
    • Introduction 00:00:00
    • Classifying data with a linear SVM 00:00:00
    • Optimizing an SVM 00:00:00
    • Multiclass classification with SVM 00:00:00
    • Support vector regression 00:00:00
    • Tree Algorithms and Ensembles 00:00:00
    • Introduction 00:00:00
    • Doing basic classifications with decision trees 00:00:00
    • Visualizing a decision tree with pydot 00:00:00
    • Tuning a decision tree 00:00:00
    • Using decision trees for regression 00:00:00
    • Reducing overfitting with cross-validation 00:00:00
    • Implementing random forest regression 00:00:00
    • Bagging regression with nearest neighbors 00:00:00
    • Tuning gradient boosting trees 00:00:00
    • Tuning an AdaBoost regressor 00:00:00
    • Writing a stacking aggregator with scikit-learn 00:00:00
    • Text and Multiclass Classification with scikit-learn 00:00:00
    • Using LDA for classification 00:00:00
    • Working with QDA – a nonlinear LDA 00:00:00
    • Using SGD for classification 00:00:00
    • Classifying documents with Naive Bayes 00:00:00
    • Label propagation with semi-supervised learning 00:00:00
    • Neural Networks 00:00:00
    • Introduction 00:00:00
    • Perceptron classifier 00:00:00
    • Neural network – multilayer perceptron 00:00:00
    • Stacking with a neural network 00:00:00
    • Create a Simple Estimator 00:00:00
    • Introduction 00:00:00
    • Create a simple estimator 00:00:00

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