Full Stack Python Development Building RealWorld Application

Explore Python recursion and iteration through hands-on patterns, using the global keyword, map function, and Fibonacci style problems like counting handshakes, with memory considerations.

Learn to convert and format date time formats, extract hour and minute, day of week name, and other features from a data frame using the datetime library.

Explore the iris dataset and key classification metrics such as the confusion matrix, precision, recall, F1 score, and AUC-ROC to evaluate supervised models for binary and multi-class tasks.

Explore python's math library to perform ceiling, floor, factorial, and gcd operations, and use the random toolbox for data cleaning and sampling, including random selection and generating numbers for plots.

Navigate the OS library to load the Kaggle CFD 13 dataset, and perform numpy and pandas EDA with matplotlib, summarizing mean, standard deviation, and percentiles of numerical features.

learn how to handle small data with many features by converting categorical to numeric, assessing correlations, using heatmaps to guide feature selection and avoid high collinearity, compare with pair plots.

Explore how distribution plots reveal skewness and outliers, assess Gaussian suitability with bar, density, and joint plots, and apply log, log-normal, and box-plot transformations for feature engineering and pre-processing.

Learn to identify and handle outliers with box plots, IQR, and region and density plots, and explore time-series features, strip plots, and joint plots for robust data visualization.

Master advanced outlier detection by preprocessing features, extracting month from timestamps, applying one-hot encoding for month and country, and predicting ad clicks with a 70/30 train-test split.

Explore data preparation for binary classification with logistic regression, including non-linear transformations and the sigmoid probability, training versus test evaluation, AUC-ROC metrics, and hyperparameter tuning.

Explore logistic regression for churn prediction, a classification technique using nonlinear transformation and a logistic curve to separate binary and multi-class outcomes, with geometric and probabilistic interpretations.

Explain how logistic regression uses a sigmoid to squash scores, derive a log loss with regularization, and compare L1, L2, and elastic net to control overfitting and feature sparsity.

Explore real-world logistic regression with loss interpretations, including logistic, hinge, and square losses, using scikit-learn, and learn how train-test splits, cross-validation, and normalization shape model performance on datasets like Titanic.

Learn data cleaning and feature rescaling of numerical columns, implement logistic regression with scikit-learn, and evaluate using F1 score and log loss, while addressing missing values and imbalanced data.

Explore feature engineering techniques for real-world data, including data preprocessing, missing value imputation, one-hot encoding, scaling, logistic regression, and cross-validation with AUC and ROC evaluation.

Know your model: use fivefold cross validation to compare AUC, precision, and recall, apply forward and backward feature selection, and tune logistic regression with hyperparameters.

Learn NLP basics by tokenizing text into numerical vectors and comparing bag of words, tf-idf, and word vectors, then apply logistic regression with hands-on eda on coronavirus datasets.

Explore bag-of-words limitations, one-hot encoding, and TF-IDF, then use scikit-learn's vectorizers and word2vec with transfer learning to capture semantic meaning in NLP features.

Explore transfer learning and tf-idf weighted vertovec to boost NLP tasks, compare bag-of-words, tf-idf, and vertovec approaches, and visualize covid data with matplotlib for practical insights.

Analyze covid-19 data with python and pandas, creating bar plots and calculated ratios to visualize death by confirmed cases, and compare states using sorting and percentiles.

Unmask covid-19 trends with data-driven insights through heat maps, scatter plots, and correlated features; explore time series, district and state data, and death, confirmed, and cured patterns using Python.

Explore the machine learning lifecycle from problem definition and business constraints to data selection, preprocessing, model training, evaluation, and deployment, with tokenization and exploratory data analysis.

Learn to clean and preprocess text for real-world applications, including tokenization, lowercasing, punctuation removal, and stopword handling with NLTK. Encode text with bag-of-words for NLP tasks.

Explore text preprocessing concepts like stemming and lemmatization and apply them to Amazon reviews to build a binary sentiment classifier using tf-idf, bag-of-words, and word2vec models.

Clean and deduplicate raw text data, handle missing values and outliers, and preprocess with regular expressions, stop-word removal, and stemming to enable EDA mastery.

Explore bag of words and count vectorizers for nlp, including unigrams, bigrams, and tf-idf weighting. Apply lemmatization, tokenization, and word2vec, including average word2vec, with logistic regression and auc evaluation.

Tune logistic regression with grid search on C and regularization, compare L1 and L2, and evaluate via cross-validation and roc auc across bag-of-words, tf-idf, and word vectors.

Clean and preprocess reviews from SQL and CSV data, apply tf-idf and Word2Vec (Google News vectors) feature engineering, and optimize logistic regression with regularization to identify top sentiment features.

Apply and compare L1 and L2 regularization to prevent overfitting in text classification pipelines using tf-idf and bag-of-words, and evaluate model performance with ROC AUC, train/test splits, and cross-validation.

Explore a showdown of classification and regression models, from logistic regression and svm to tree-based methods, comparing auc, confusion matrices, and regularization across train, test, and cross-validation data.

Minimize the squared error between actual y and predicted y hat to fit a line with weights w and intercept w0, using L1 or L2 regularization to handle outliers.

Explore linear regression basics, interpretability challenges from feature collinearity, and compare with logistic regression, using the Boston housing dataset in Python with scikit-learn.

Explore linear regression with regularization concepts like L1 and L2, compare sklearn and custom implementations, and apply ridge and lasso to real datasets while evaluating error and distributions.

Discover how decision trees perform classification and regression by building a tree with root and leaf nodes, covering internal nodes, majority voting, and a cancer prediction example.

Explore decision trees, from root node depth to splits, using entropy, information gain, and Gini impurity, applied to a tennis dataset with outlook, temperature, humidity, and wind.

Explore entropy and information gain for building decision trees, compare with Gini impurity, and learn to compute weighted average entropy across partitions to choose splits.

Explore decision trees' majority vote for classification, mean-based regression predictions, and assess with mean squared error and mean absolute error, while depth shapes time and space complexity and overfitting risk.

Explore converting categorical values for machine learning, compare label encoding with one hot encoding, and apply feature binning with a decision tree to reduce dimensionality and avoid overfitting.

Explore decision trees with scikit-learn, tune max depth, entropy, information gain, and Gini impurity, and address imbalanced data via class weights and oversampling or undersampling.

Explore bagging to reduce variance and build a random forest from decision trees using row and column sampling, with majority vote for classification and mean for regression.

Explore end-to-end machine learning with Flask, building a loan prediction app using a random forest model; compare static and dynamic templates and learn get and post workflows for cloud deployment.

Master dependency management and productionize models using requirements.txt and pip freeze. Build automated pipelines with Azure Data Factory for end-to-end ML workflows.

Explore bivariate analysis with scatter plots, perform preprocessing and feature engineering, and tune a random forest via grid and random search, then deploy a Flask app predicting loan outcomes.

Build and run a real-world loan predictor with Flask from the command prompt, edit HTML templates, test form inputs with get and post requests, review logs, and deploy on Heroku.

Explore NLP-based sentiment analysis on a movie review dataset using tokenization, bag-of-words, tf-idf, word2vec, and Bert, with data cleaning and univariate to multivariate analyses. Covers time series analysis and forecasting.

Explore loan data through distribution plots, density, and box plots to analyze funded amount, interest rate, dti, and emi ratio, identify outliers, and derive insights for loan status decisions.

Explore univariate and bivariate analysis, correlation heat maps, and segmentation plots to derive insights, highlight high correlations, and apply percentile-based categorization and feature engineering.

Explore univariate and segmented plots to relate loan status and interest rate. Show how installments, emi, salary, and loan terms relate to charged-off and fully paid outcomes.

Explore advanced bivariate, multivariate, and univariate analysis concepts to uncover relationships among grade, interest rate, funded amount, and loan status using box plots, scatter plots, and cat plots.

Evaluate clustering results by identifying outliers and patterns in loan statuses such as charged off and fully paid, using plots and a heat map.

Explore how deep learning, popular since 2012, relates to neuroscience and classical ML. Trace neural networks from input to hidden to output, with MRI, NLP, translation, and voice recognition applications.

Learn how deep learning automatically learns features with weights and activation functions, using MLPs and CNNs/RNNs for image and NLP tasks, powered by backpropagation.

Dive into backpropagation by performing forward passes, computing loss and derivatives via chain rule, updating weights, and repeating through epochs to train differentiable activation networks, including RNNs in NLP.

Explore how recurrent neural networks preserve information across words with hidden states and word vectors, including LSTM and GRU variants and SGD-based training.

Explore hands-on building of rnn architectures in tensorflow, including lstms and gru, with one-to-many, many-to-one, and many-to-many relationships; compare keras and tensorflow, apply to iris data and nlp tasks.

Train recurrent neural networks in TensorFlow with Keras, building with dense and RNN layers to classify sine, square, and triangle datasets using softmax.

Train a four-hidden-layer simple RNN for binary classification, learning to compile, fit, and monitor loss and accuracy across epochs; explore tensorboard, early stopping, transformers, and sequence-to-sequence concepts.

Explore sequence-to-sequence models with encoders and decoders, using cell states and context to transform sequences, and compare zero initialization, random initialization, and He initialization through transfer learning.

Explore LSTM networks and their gates—including input, forget, and output—along with a candidate gate, and compare random, zero, and Xavier/He initializations to mitigate vanishing gradients in RNNs.

Learn how lstm cells in tensorflow manage long sequences through input, forget, and memory gates with tanh and sigmoid activations, via a custom implementation.

Develop a custom neural network pipeline without heavy dependencies, comparing tensorflow-driven and diy implementations. Analyze rnn, lstm, gru, gates, and activation functions to optimize production-ready models.

Learn how convolutional neural networks classify images by using kernels to extract patterns and edges from pixel arrays, representing data as height, width, and color channels.

Understand how CNNs use different kernels, including Sobel, through convolutional and max-pooling layers, to extract features from grayscale image matrices for dog-vs-cat classification.

Explore how padding extends inputs with zeros to preserve edges in CNNs, and how strides shift the kernel to control output size and learned features.

Learn how convolution, padding, and stride shape CNN outputs, apply max pooling and flatten layers, and use data augmentation in TensorFlow to boost image classification.

Build and train CNN models for image classification using OpenCV, image augmentations, data loading, transfer learning, and hyperparameter tuning, including production readiness for unseen data.

Apply LSTM and CNN architectures with hidden layers, preprocessing grayscale images, and concepts for binary, multi-class, and regression tasks, including padding, activation functions, and training through epochs.

Train and evaluate CNN models with TensorFlow on pizza vs. stick images, covering data loading, preprocessing, rescaling, padding and striding, and clustering-based labeling for unlabeled data.

Explore convolutional neural networks using TensorFlow, covering data loading, preprocessing, visualization of images, and tuning hidden layers with padding and strides to transform input images into output representations.

Train deep learning models on image data using a 1500-image dataset, applying pre-processing and rescaling for RGB images to classify visa versus stick, and evaluate model performance.

We rescale each image’s color values, mapping white to 255 and black to 0, using train and test data at 224 for binary classification.

Learn mini-batch size strategies to prevent training hangups, manage raw image datasets, and prepare labeled data with clustering when labels are absent.

Use k-means clustering to separate piezas and stick groups, build similarity via edge features like Sobel, then label clusters to train models across ML, deep learning, NLP, and time series.

Explore multi-class image classification with CNNs in TensorFlow, using pre-trained Vgg16 and transfer learning, including data preprocessing, augmentation, and evaluating with accuracy and log loss.

Explore multi-class image classification with CNNs, using softmax and categorical cross entropy, and Adam optimization; enhance training with data augmentation, shuffling, and TensorBoard to diagnose underfitting across ten classes.

Explore the VGG16 convolutional neural network architecture, its 16 layers, and how transfer learning with pre-trained weights enables efficient fine-tuning on custom datasets using Keras.

Explore convolutional neural networks using a VGG16 model to recognize images, train with gradient descent, and optimize with backpropagation, pooling, and softmax-activated fully connected layers toward global minima.

Analyze gradient descent dynamics to update weights and reduce loss toward convergence, comparing gradient descent with all data points, stochastic gradient descent, and mini-batch approaches for efficiency.

Compare gradient descent approaches—from full batch to mini-batch and sgd—learn how momentum and Adadelta help denoise and stabilize weight updates in Flask-based model deployment.

Learn how Adam, Adadelta, and other optimizers improve convergence by using momentum and exponential moving average, with learning rate smoothing via epsilon.

Build a flask-based web app that serves a Keras image classification model using pre-trained ImageNet architectures, applying transfer learning for wild animal predictions.

Achieve low latency in model deployment with a Flask application, using ImageNet pre-trained weights, and handle post and get routes for predictions.

Deploy a flask app for real-world predictions by decoding base64 images to pil and returning json with image paths, labels, and maximum probabilities.

Learn how to deploy a Flask app for image classification, test with drag-and-drop images, view logs, and optimize for low-latency predictions using pre-trained models like VGG16, ResNet, and others.

Explore business statistics with descriptive statistics, probability, and inferential methods, and learn data types, central tendencies, dispersion, and visualizations like boxplots and skewness.

Explore the differences between qualitative and quantitative data, including nominal, ordinal, and binary types, and distinguish discrete from continuous variables for applied statistics in real-world datasets.

Explore central tendency in data analytics by examining mean, median, and mode, plus alternative averages like geometric and harmonic means, with practical notes on data types and feature engineering.

Explore absolute and relative measures of dispersion, including range, variance, standard deviation, and quartile deviation, and learn how they reveal data spread and outliers.

Explore population versus sample data, compute means, and apply the central limit theorem using NumPy, SciPy, and Matplotlib to transform skewed distributions toward normal, with outlier handling.

Explore sampling and z-scores in data analysis, covering the central limit theorem, Gaussian and uniform distributions, and data rescaling for machine learning.

Explore hypothesis testing and p-value interpretation using normal distribution concepts, z-scores, and t-tests, with practical coin flip and mean comparisons to interpret null and alternative hypotheses.

Explore t-tests, confidence intervals, and anova with practical SciPy tips. Understand p-values, sample means, standard deviations, and correlation using penguin data and heat maps.

Explore the Pearson correlation coefficient, covariance, and p-values, with z-scores and heat maps, using iris data to illustrate feature correlations and discuss correlation versus causation.

Learn advanced hypothesis testing and correlation analysis, applying null and alternative hypotheses, p-values, and test statistics to real-world business decisions, with data cleaning and preprocessing.

Learn data cleaning and preprocessing through practical business statistics, including distributions, histograms, box plots, interquartile range outlier detection, and plotting with seaborn and matplotlib.

Explore detecting outliers, describing data with pandas, and visualizing distributions using histograms, box plots, CDFs, and PDFs, including joint plots, Q-Q plots, and distribution plots.

Analyze univariate, bivariate, and multivariate relationships using q-q plots, distribution plots, and pair plots. Understand mean, standard deviation, skewness, and Gaussian tendencies with Seaborn and Matplotlib.

Analyze feature relationships with correlation matrices, heatmaps, color maps, and scatter plots, bar plots; impute missing values and compare mean, median, and mode.

Explore how covariance and Pearson correlation matrices reveal high correlations; visualize with heat maps; use hypothesis testing, Z scores, and distribution functions to analyze means, variances, and outliers.

Explore the basics of time series data and forecasting, including how plots reveal trends, seasonality, and stationarity, and how sales or prices can be forecast over time.

Explore time series components, including mean, standard deviation, irregularity, cyclicity, and seasonality, and learn how to achieve stationarity with rolling statistics, ADF tests, differencing, and transformations.

Examine stationarity and its importance in time series analysis by analyzing autocorrelation, ACF and PACF, moving averages, seasonality, lags, and ARIMA/SARIMA forecasting.

Explore arima fundamentals, including autoregressive and moving average components with differencing, tune p d q, and assess stationary time series with rolling statistics and adf on the air passengers dataset.

Learn to build and evaluate ARIMA models by turning non-stationary time series into stationary with differencing and transformations, applying ADF tests, and using moving averages for forecasting.

Learn statistics for time series analysis, covering distributions, central limit theorem, central tendencies, sampling, hypothesis testing, Bayesian methods, and Gaussian-based modeling to support ML and seasonal decomposition.

Explore the central limit theorem in time series, showing how sample means form a Gaussian distribution and how log transformations help address skewness.

Explore descriptive statistics and exploratory data analysis by examining mean, median, variance, standard deviation, z scores, and hypothesis testing with p values and confidence intervals for time series forecasting.

Explore variance analysis, hypothesis testing with p-values and confidence intervals, and correlation concepts using Pearson metrics, while delving into arima time series forecasting, model evaluation, and backtesting.

Forecast time series with ARIMA and Prophet, interpreting predictions through confidence intervals and upper/lower bounds. Examine training data versus forecasted values, and analyze trend, seasonality, and stationary behavior.

Explore basic statistics concepts for business analysis and learn to plot distributions. Analyze a dataset with diagnosis and radius mean using histograms, box plots, and outlier detection via interquartile range.

Explore descriptive statistics with pandas, including mean, standard deviation, variance, and quantiles, visualize distributions with histograms and CDF, and perform univariate, bivariate, and multivariate analysis, including hypothesis testing.

Explore time series data visualization by analyzing radius mean and area mean with joint plots, q-q plots, and distribution plots to assess correlations, skewness, and gaussian assumptions.

Analyze time series features F1, F2, and F3 with scatter, density, and pair plots; evaluate correlations via heat maps and correlation matrices, and identify outliers and distribution patterns.

Examine missing data handling with mean, median, and mode imputations, and analyze feature relationships using covariance, Pearson correlation, and heatmaps, plus distribution concepts and hypothesis testing.