Master the Machine Muse Build Generative AI with ML

Data cleaning, the unsung hero of ML, drives model performance by handling missing values, scaling, and feature engineering, then trains logistic regression and evaluates with F1, accuracy, and log loss.

Learn data preprocessing for churn prediction with feature engineering, imputing missing values, one-hot encoding, and scaling. Build and evaluate a logistic regression model using cross-validation and AUC metrics.

Analyze how k-fold cross validation with five folds evaluates a logistic regression model, using feature importance, forward and backward feature selection, and metrics like precision, recall, and auc.

Explore NLP fundamentals from tokenization to converting text into numerical vectors, using coronavirus datasets and Kaggle files, and compare bag-of-words with TF-IDF and word embeddings, highlighting semantic limitations.

Compare bag of words with tf-idf and the word-to-vec model representations using scikit-learn feature extraction for NLP tasks. Learn how vectorization captures word meaning and supports classification.

Explore transfer learning as an NLP shortcut, using tf-idf weighted average word2vec to improve text representations beyond bag of words, and visualize datasets with matplotlib for EDA.

Analyze covid-19 data with Python, computing death-to-case ratios and creating new columns. Visualize trends with a bar plot and color schemes, and compare states by cases per 10 million.

Explore data visualization and correlation analysis of covid-19 data, using heatmaps and a correlator matrix to reveal patterns between confirmed, cured, and death cases across districts, with time-series insights.

Define the machine learning lifecycle from data to deployment. Learn data selection, preprocessing, tokenization, exploratory data analysis, model training and evaluation, and low-latency deployment considerations.

Clean and normalize text data through tokenization, stopword removal, and HTML tag stripping, then compare feature selection methods to optimize model precision and robustness.

Explore advanced text preprocessing, including stemming and lemmatization, and apply cleaning, deduplication, and TF-IDF and bag-of-words to Amazon reviews data for binary sentiment classification.

Learn to clean text data and handle duplicates, missing values, and outliers using IPython notebooks, regular expressions, stopwords, and stemming techniques for robust EDA of large text datasets.

Explore feature engineering for NLP by comparing bag-of-words with count vectorizers, unigrams and n-grams, tf-idf, and word2vec, then apply logistic regression and evaluate with AUC.

Explore hyperparameter tuning for logistic regression with grid search, comparing l1 and l2 penalties, using train/test splits and cross-validation to optimize auc.

Explore data cleaning, bag-of-words and tf-idf with word2vec features, and tune logistic regression via c to maximize auc, identifying top positive and negative keywords.

Master machine learning regularization to prevent overfitting, compare L1 and L2 with tf-idf and bag-of-words, tune hyperparameters, evaluate with train-test splits and confusion matrices, and explore feature visualization.

Compare multiple machine learning models for text classification, from logistic regression to SVM and tree-based methods, using tf-idf and word2vec features, with AUC, confusion matrices, regularization, and cross-validation insights.

Learn how linear regression uses squared loss to minimize prediction error on a line, using w and intercept w0, with L1 and L2 regularization.

Explore linear regression fundamentals, its interpretability under collinearity and multicollinearity, and implement a real-world Boston housing example using scikit-learn with standardization and train-test splits.

Explore linear regression with L1 and L2 regularization, compare ridge and lasso, and implement custom approaches to handle irreducible errors and outliers.

Explore decision trees for classification and regression, mastering root, internal, and leaf nodes, yes/no splits, and majority vote predictions through cancer prediction examples.

Discover how decision trees split data to form root nodes and depths using entropy, information gain, and gini impurity. Apply outlook, temperature, humidity, and wind features to classify play tennis.

Explore how entropy and information gain split data into partitions to build decision trees, comparing entropy, weighted child entropy, and Gini impurity for interpretable models.

Apply majority vote at leaf nodes in decision trees for classification. Explore regression with mean squared error and mean absolute error, and note depth and regressor concepts.

Transform categorical data using one hot encoding instead of label encoding to avoid false ordinal relationships. Manage dimensionality with feature binning before applying a decision tree to reduce overfitting.

train a decision tree classifier in scikit-learn, tune max depth, and compare entropy, information gain, and gini impurity; address class imbalance with oversampling/undersampling and class weights; visualize trees with graphviz.