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Introduction to Machine Learning Course

• The course aims to provide students with knowledge and skills on supervised machine learning algorithms for regression and classification problems.

• The course requires a strong foundation in linear algebra, calculus, statistics, and probability theory, as well as basic Python programming skills.

• The course literature includes books on statistical learning, machine learning, and Python data science.

• The course agenda includes lectures on machine learning techniques, supervised learning models, and machine learning diagnostics, and labs on exploratory data analysis, machine learning modeling, and case studies.

• The final grade is based on a mid-term theoretical exam and two machine learning projects, with a total weight of 100 points.

• The course is challenging and requires several hours of study per week, as well as active participation in classes.

• Machine learning is the process of using mathematical models to help a computer learn without direct instruction, and it uses algorithms to identify patterns within data.

• There are four types of machine learning: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

• Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs, while unsupervised learning learns patterns from untagged data.

• Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data during training, and reinforcement learning trains machine learning models to make a sequence of decisions.

• The fundamental assumption of machine learning is that there is a function that represents a causal relationship between features and target, and the goal is to estimate this function by minimizing the error between predictions and actual values.

• Gradient descent is a common optimization algorithm used to minimize the cost function, and it involves taking steps in the negative gradient direction until a (local) minimum is reached.Introduction to Machine Learning - Key Concepts and Techniques

• The choice of estimator in machine learning depends on whether the focus is on prediction, inference, or both.

• Linear regression is a supervised learning algorithm for predicting continuous variables from independent variables and can be estimated using different methods, with ordinary least squares (OLS) being the most popular.

• Logistic regression is a supervised learning algorithm for predicting nominal binary variables from independent variables, and its results are interpreted using marginal effects and odds.

• Multinomial logistic regression is a generalization of logistic regression for classifying more than two classes.

• Generalized Linear Models (GLMs) generalize linear regression by allowing the linear model to be related to the response variable via a link function and allowing the magnitude of the variance of each measurement to be a function of its predicted value.

• Before starting any machine learning project, it is essential to formulate a problem statement worksheet to define the business task clearly.

• Data preparation involves selecting, extracting, transforming, exploring, cleaning, and engineering data to a convenient analytical form for machine learning.

• Exploratory data analysis involves analyzing sets of data stored in a data frame and using visualization techniques to better analyze the data and statistical tools to explore properties and relationships between data.

• Missing values in data can be dealt with using techniques such as imputation, removal of variables or examples, or doing nothing.

• Feature engineering involves generating new variables and is a key stage of modeling that can be performed during the ETL process or after.

• Model selection involves choosing the best model to fit the data, and this can be done using techniques such as cross-validation and hyperparameter tuning.

• Model evaluation involves assessing the performance of a model using metrics such as accuracy, precision, recall, and F1-score, and this can be done using techniques such as confusion matrices and ROC curves.Overview of Feature Engineering and Evaluation Metrics in Machine Learning

• Feature engineering is the process of transforming data into a form that can be consumed by machine learning models.

• This process involves aggregating data using descriptive statistics, such as mean, median, and quantiles, to create new variables or process existing ones.

• Numeric variable transformations include scaling, clipping, log scaling, z-score, quantile transformer, power transformer, bucketing, polynomial transformer, spline transformer, rounding, replacing with PCA, and other arithmetic operations.

• Categorical variable transformations include one-hot encoding, ordinal encoder, BaseN, CatBoost Encoder, Count Encoder, Hashing, Helmert Coding, James-Stein Encoder, Leave One Out, Polynomial Coding, Quantile Encoder, Sum Coding, Summary Encoder, Target Encoder, Weight of Evidence, and more.

• Interactions between variables can also be explored by attempting multiplication, division, subtraction, and other mathematical operations.

• The best variables created in feature engineering are often those with a strong business, economic, or theoretical basis.

• Evaluation metrics are calculated after machine learning models are created using different cost functions.

• Regression metrics include mean square error, root mean square error, mean absolute error, mean absolute percentage error, mean squared logarithmic error, R score, median absolute error, mean absolute scaled error, and more.

• Classification metrics are based on the confusion matrix and include accuracy, true positive rate, true negative rate, positive predictive value, negative predictive value, false positive rate, false negative rate, F beta score, Matthews correlation coefficient, and more.

• ROC curves and precision/recall curves are used to evaluate classification models based on probabilities, and AUC ROC and AUC PR are used to calculate a single representative number for the whole model.

• Precision, recall, F1-score, and other evaluation metrics are important for assessing the accuracy of machine learning models.

• In regression, models can estimate confidence intervals of the forecast in addition to the expected value.Machine Learning Fundamentals: Bias/Variance Trade-Off, Cross-Validation, and K-Nearest Neighbors

• The Continuous Ranked Probability Score (CRPS) generalizes the Mean Absolute Error (MAE) for probabilistic forecasts.

• The bias of a model is the difference between the expected prediction and the correct model, while the variance is the variability of the model prediction for given data points.

• The simpler the model, the higher the bias, and the more complex the model, the higher the variance. The Mean Squared Error (MSE) can be decomposed into bias squared, variance, and noise.

• Overfitting occurs when a model is too complex and performs well on the training data but poorly on the testing data. Underfitting occurs when a model is too simple and performs poorly on both training and testing data.

• Learning curves, such as Train Learning Curve, Validation Learning Curve, Optimization Learning Curves, and Performance Learning Curves, can show a model's learning performance over time or experience.

• It is good practice to split a dataset into a training set, validation set, and testing set to avoid overfitting. Stratified approaches are important for imbalanced datasets.

• Cross-validation (CV) is a resampling method that uses different portions of the data to validate and train a model on different iterations. It is more robust than a single train-validation split and is useful for hyperparameter tuning.

• There are many types of cross-validation, such as Hold-out, K-folds, Leave-one-out, Leave-p-out, Stratified K-folds, Repeated K-folds, Nested K-folds, and Time series CV. The choice of CV depends on data specifics, business problems, dataset size, and computing resources.

• K-nearest neighbors (KNN) is a non-parametric and instance-based algorithm for both classification and regression problems, which uses the idea of locality.

• The three key hyperparameters for the KNN model are the distance metric, number of K neighbors, and weights of individual neighbors. Feature scaling is necessary for KNN to get rid of the lack of homogeneity of features.

• The most popular scaling approaches for continuous variables are standardization, rescaling, and quantile normalization. The most popular standardization approaches for nominal variables are one hot encoder and ordinal encoder.

• Tree-based approaches, such as K-D Tree and Ball Tree Search Algorithms, can make the search process more efficient than brute force searching. The curse of dimensionality problem occurs in KNN when points are drawn from a probability distribution and tend to never be close together in high dimensional spaces.