Complete Machine Learning With Real-World Deployment

Explore logistic regression as a classification technique with geometric and probabilistic interpretations, and learn loss minimization, regularization, feature scaling, and cross-validation for real-world churn prediction.

Learn how tokenization converts text to numbers and compare bag-of-words, tf-idf, and word2vec embeddings, including their advantages, limitations, and semantic meaning.

Explore the machine learning lifecycle with hands-on text preprocessing of amazon reviews, covering tokenization, bag-of-words, tf-idf, stop words, stemming, and logistic regression for sentiment analysis.

Learn linear regression fundamentals and practical sentiment analysis with bag-of-words and tf-idf features, logistic regression with L1/L2 regularization, AUC and confusion matrix evaluation, and deployment implications.

Explore how decision tree classifiers handle classification and regression, building root and leaf nodes, and using entropy, information gain, or Gini impurity to split data.

Explore geometric intuition of ensemble models with random forest, bagging, and random sampling with replacement, then deploy using Flask to build a loan prediction app.

Explore NLP sentiment analysis on a large movie review dataset using tokenization, bag of words, and tf-idf, with data cleaning, imputation, and univariate, multivariate, and time series analytics for deployment.

Learn how the k-means algorithm performs unsupervised clustering by iterating assignment of points to nearest centroids, recomputing centroids, and converging, with initialization methods like k-means++ and elbow method.

Explore deep learning foundations by linking neural networks to brain-inspired concepts. Learn how multi-layer perceptrons, backpropagation, activation functions, forward and backward passes enable automatic feature learning.

Explore deep learning concepts from RNN and LSTM to sequence-to-sequence models, using Python notebooks and TensorFlow to build, train, and evaluate on sine, square, and triangle datasets.

Explore convolutional neural networks for image classification, using padding, stride, and max pooling to extract features, and apply data augmentation with TensorFlow or Keras for real-world deployment.

Learn convolutional neural networks for pizza versus stick image classification, with data loading, preprocessing, padding and batch sizing, using activation functions and CNN architectures.

Learn transfer learning with the VGG16 model for image classification, applying CNNs, preprocessing, rescaling, data augmentation, and batch size tuning to distinguish pizza versus stick images.

Build a deep learning web app with a CNN in Flask to recognize wild animals using pre-trained ImageNet models, handling image uploads, server routes, and low-latency predictions.

Explore time series forecasting fundamentals, including trend, seasonality, stationary and non-stationary data, decomposition, differencing, transformations, moving averages, autocorrelation, and ARIMA for real-world deployment.

Master time series forecasting by blending probability and statistics, including the central limit theorem, distributions, hypothesis testing with p-values, and Gaussian assumptions, applying AR and Prophet models with confidence intervals.

Explore foundational business statistics and data visualization techniques using pandas and seaborn to analyze medical data, identify distributions, detect outliers, and understand relationships through univariate, bivariate, and multivariate analyses.

Explore a flight fare prediction project using a Kaggle dataset with journey date, source, destination, route, times, and stops to predict ticket prices; perform data cleaning and preprocessing for modeling.

Apply feature engineering for flight fare prediction with time-based and journey features, extract hours and minutes, encode stops and airlines, and explore classical models and flask deployment.

Deploy a flight fare prediction model using a Flask app, with a simple HTML form and get/post handling to predict price from departure date, time, stops, airline, origin and destination.

Explore mushroom classification through exploratory data analysis, data cleaning, and feature engineering to distinguish edible from poisonous mushrooms, using classical models, preprocessing steps, and evaluation techniques.

Analyze data preprocessing and cleaning, drop non-informative features, impute missing values, apply one-hot encoding, establish a baseline with logistic regression, support vector classifiers, and XGBoost, evaluated by accuracy and cross-validation.

Train a multi-class classifier for nursery school applications, predicting not recommended, priority, or very recommended labels, using exploratory data analysis, preprocessing, one-hot encoding, and cross-validated model benchmarking.

Train baseline models—logistic regression, decision tree, and SVM—to predict nursery school recommendations. Apply ten-fold cross-validation and hyperparameter tuning to compare accuracy and reveal feature importance guiding deployment.

Explore NLP for toxic comment classification using Kaggle's Jigsaw Wikipedia comments dataset and build a multi-headed model to detect toxic, insult, and identity hate.

Explore tokenization with regular expressions and Keras preprocessing to convert text to numerical data, manage lowercasing and stopwords, and visualize word frequency and toxic classifications with a word cloud.

Explore NLP classification of toxic comments using Kaggle jigsaw and Wikipedia data. Learn data cleaning, pre-processing, and feature engineering to build multi-label models identifying toxic, insult, and identity hate categories.

Explore UK road accident time series data from 2005–2014 (Kaggle dataset) using exploratory data analysis to forecast future accidents, examining trends, seasonality, regions, and highway authorities.

Forecast UK accident trends using sarima, fbprophet, and lstm variants (gru and bidirectional lstm), through data preparation, model tuning, and evaluation for two-year ahead predictions.