Data Science & AI Masters 2026 - From Python To Gen AI

Learn Python from scratch with practical installation guidance, covering Python 3.10/3.13, Anaconda Navigator, IDE options, and Google Colab as an online workflow for data analytics.

Explore Google Colab to run ipynb notebooks, practice Python basics with interactive Jupyter notebooks, and upload files like tuples while debugging with ChatGPT assistance.

Leverage ChatGPT to troubleshoot Python and data analytics issues when no instructor is available. Resolve errors such as undefined variables, pd not defined, and missing csv files with guided explanations.

Discover introduction to Python, an open-source, general-purpose, object-oriented and interpreted language with a large standard library and cross-platform support, ideal for data science and scripting.

Install python using python.org, anaconda, or colab, then master python basics by exploring variables and keywords, data types, and simple operations with practical examples.

Explore Python data types, operators, and operands, including numeric types, dictionaries, booleans, sets, and sequence types like strings, lists, and tuples. Learn type casting and operator precedence.

Explore Python data structures with a focus on lists: indexing, slicing, mutability, and methods like extend, append, pop, remove, sort; compare lists with tuples and introduce sets and dictionaries.

Explore tuples in Python, including how round brackets define them, compare them with lists, and cover immutability, indexing, slicing, concatenation, dictionary keys, and nested structures.

Master Python dictionaries by exploring key–value pairs, key immutability, and value mutability. Learn to access, update, delete, and sort dictionary data with practical examples.

Master Python loops and iterations by exploring for and while loops, iterables like lists, strings, dictionaries, and ranges; practice with conditionals, an odd/even check, input handling, and list comprehensions.

Learn how to define and use functions in Python, including user defined functions with def and lambda expressions. See examples of even/odd checks, factorials, and using map, filter, and reduce.

Explore map, filter, and reduce in Python to write concise, readable code with lambda expressions. See practical examples of transforming, filtering, and reducing data.

Master Python file handling concepts, including open, read, write, append, and close, with read line and read methods, and learn how pandas read_csv simplifies data access.

Explore control structures in Python, including binary and relational operators and operands. Learn if-else decision making, assignment and equality checks, input, range, and basics like list comprehensions, map, filter, reduce.

Explore object oriented programming in Python by defining classes and objects, using constructors, and understanding inheritance, encapsulation, polymorphism, and method overriding.

Master numpy, a Python library for fast numerical operations on multi-dimensional arrays. Install via pip, import as np, and use zeros, ones, full, and identity.

Explore pandas, a fast Python library for data analytics. Learn to read data, inspect datasets with head and info, and perform core operations like sorting, filtering, and merging.

Explore data visualization in Python to transform numbers into insights using matplotlib, seaborn, and plotly, revealing bar, area, line, and pie charts for data-driven decisions.

Master matplotlib in Python to plot histograms, area, bar, line, scatter, and pie charts using pandas and NumPy, including converting dictionaries to data frames and reading csv data.

Explore seaborn, a data visualization library that makes complex plots easier than matplotlib. Install via pip, import as sns, load iris, and generate density plots, dist plots, and pair plots.

Statistics teaches collecting and studying large data to support analytics with charts, tables, and graphs. It explains population versus sample and how scatter plots reveal trends in age and salary.

Explore descriptive and inferential statistics, and distinguish data types—categorical (qualitative) and numerical (quantitative)—to build foundational data science skills for gen ai.

Explore descriptive statistics, including mean, median, and mode, plus variance, standard deviation, and frequency distribution to describe data characteristics.

Explore inferential statistics, which use sample data to draw inferences about population parameters, including estimating the parameter and conducting hypothesis testing.

Explore categorical (qualitative) and numerical (quantitative) data, including nominal and ordinal types with examples like gender and economic status, and visualize them with pie and bar charts.

This module explains quantitative data, defined as numerical values, and contrasts discrete data (integers, counts like number of students) with continuous data (measured values like height, temperature), including plotting examples.

Explore core sampling concepts in statistics by defining population and sample, examining sample size, and overviewing random and nonprobability sampling techniques.

Define population as the full group you want to study and sample as a subset. Use a country’s average age to illustrate why we analyze samples before drawing population conclusions.

Explore how sampling enables efficient analysis of large populations by using representative samples. See how this approach reduces data collection costs and errors while speeding up analysis and visualization.

Explore probability and non-probability sampling, understanding how equal chances govern simple random, stratified, cluster, and systematic methods, versus non-probability approaches like convenience, purposive, voluntary, and snowball sampling.

learn how to perform cluster random sampling by defining the population, dividing into diverse clusters with no repetition, randomly selecting clusters, and collecting data from them.

Explore probability sampling techniques, including random, systematic, stratified, and cluster sampling, with practical examples that preserve population distributions and illustrate sampling from clusters.

Discover non-probability sampling techniques—convenience, purposive, voluntary response, and snowball sampling—and understand how they contrast with probability sampling in shaping sample representativeness.

Explore population sampling, why a representative sample saves time and money. Learn variance and standard deviation, their formulas, and the population versus sample denominators.

Learn why sample variance uses n minus one instead of n, via Bessel's correction, and how this unbiased measure relates to population variance and degrees of freedom.

Explore descriptive analytics by examining central tendency (mean, median and mode) and dispersion measures like range, interquartile range, variance, standard deviation, and mean deviation.

Explore measures of central tendency, including mean, median, and mode, as descriptive statistics that reveal the central position and concentration of data in a distribution.

Mean is the common measure of central tendency, calculated as the sum of all observations divided by n. Use mean or median to impute missing values.

Learn to determine the median, a measure of central tendency, by ordering data and averaging middle values for even sets, and compare it with the mean when data contain outliers.

Explore mode as a measure of central tendency, including unimodal, bimodal, and multimodal cases, the grouping method to find mode, and its use for imputing missing data in categorical datasets.

Explore measures of dispersion to understand data variability and distribution. Learn about range, variance, standard deviation, interquartile range, and mean deviation.

Explore the range in data science and ai contexts, a simple dispersion measure defined as max minus min, and its use in quality control and focusing on data extremes.

Explore the interquartile range (IQR), a measure of dispersion for the middle 50% of data, computed as Q3 minus Q1, with Q1 and Q3 derived from the median-based halves.

Learn mean deviation, the average of absolute deviations from a central value, with the formula and a numeric example yielding 0.88, and note that the median minimizes this measure.

Explore the probability syllabus by outlining what probability is, its theories and formulas, why we use it, the addition rule, independent events, cumulative and conditional probability, and Bayes theorem.

Master the addition rule of probability by applying mutually exclusive and not mutually exclusive formulas, using die rolls and class examples to compute P(A or B).

Explore cumulative probability, the likelihood that a random variable lies within a range a to b, with examples from fair two-coin flips and dice sums.

Explore conditional probability concepts and rules, including P(B|A), P(A∩B), and multiplication, through urn and survey examples with and without replacement.

Explore Bayes theorem as an extension of conditional probability, learn the formulas for P(A|B) and P(B|A), and see how probability of A intersect B underpins this approach.

Apply Bayes' theorem to a two-bag drawing problem, calculating the probability a black ball came from bag x using conditional probabilities and Bayes' formula.

Explore probability distributions in statistics, including uniform, binomial, Poisson, and normal distributions, with examples like coin toss outcomes and weather, and learn feature scaling to convert to standard normal.

Explore the binomial distribution, a probability distribution for two independent outcomes across n trials, using the formula n choose x p^x (1-p)^{n-x} and noting its relation to discrete data.

Explore the Poisson distribution, a discrete probability model for the number of events in a time period, with lambda as the average rate.

Explore the normal distribution (Gaussian bell curve), its relation to the central limit theorem, the empirical rule, and the 68/95/99.7 percent ranges with mu and sigma.

Demonstrate calculating mean and standard deviation, evaluate data against a normal distribution, and explore skewness, kurtosis, and transformation techniques to normalize skewed data.

Understand skewness and its impact on data distribution, contrasting right and left skew with the Gaussian distribution. Learn transformation techniques: logarithmic, square root, cube root, reciprocal, using pandas and numpy.

Explore kurtosis, the measure of a distribution's thickness, including mesokurtic and platykurtic shapes, and contrast it with skewness while applying log, reciprocal, square root, and cube root transformations.

Explore normal and standard normal distributions, compute z-scores with x minus mu over sigma, and apply standardization and normalization to prepare data for probability calculations.

Compute John's z score as (700−600)/150 ≈ 0.67, then use the z table to place him in the normal distribution, meaning about 74.86% score below him among peers.

Understand covariance as the relationship between two variables, showing direct or inverse associations. See the covariance formula, population versus sample, and a stock example, with correlation introduced next.

Explore the concept of correlation and its relation to covariance, learn Pearson’s correlation coefficient, and apply linear relationships, interpretation, and heat map diagrams in data analysis with Python.

Explore the difference between covariance and correlation, including how they describe how two variables move together, their ranges, and when to use correlation in data analysis.

Master hypothesis testing to make statistical decisions from experimental data, using null and alternate hypotheses. Learn how sample data assess hypotheses with a standard normal distribution and tailed tests.

Explore one-tailed and two-tailed tests in hypothesis testing, their rejection regions on the sampling distribution, and how they relate to the null and alternative hypotheses.

Explore p values and significance levels in hypothesis testing, learn how alpha and the null hypothesis influence rejection decisions through coin-toss examples and probability concepts.

Explore the major statistical tests for different data types, including t, z, anova, chi-square, and correlation tests, and learn how to visualize and compare categorical and numerical features.

Explore one-sample and two-sample t tests using gender age data, setting a hypothesized mean of 30 and a 0.05 significance level, and interpreting p values.

Analyze the means with a z test to compare two samples, formulate null and alternative hypotheses, and interpret p-values at a 0.05 significance level.

Learn how to apply the chi square test to categorical data, compute observed and expected values via a pivot table, and interpret p-values to assess association between variables.

Learn anova, a parametric test for comparing means across three groups, covering hypotheses, sum of squares, degrees of freedom, f statistics, p values, and a 0.05 significance benchmark.

Learn how the correlation test reveals linear relationships, contrasts with covariance, and is calculated via scatter plots and the CORREL function, with height–weight and age–salary examples.

Explore the data analytics process, from data collection and processing to cleaning, exploratory data analysis, and model building, yielding actionable insights and visualizations.

Explore how visualization presents data graphically to reveal insights, trends, and patterns, using charts such as bar, pie, line, and scatter plots for clear, impactful analytics.

Explore the fundamentals of exploratory data analysis (EDA), from data cleaning and preparation to visual analytics, univariate and bivariate analysis, and deriving metrics with Python.

Explore the steps of the EDA process, starting with data sourcing from public and private sources and accessing data through databases or connectors.

Transform unclean data into high-quality data to improve analysis and model accuracy by mastering missing values, scaling, outlier treatment, and fixing invalid data for eda and model building.

Explore techniques to handle missing values in data cleaning, including row/column deletion, mean/median/mode imputation, and algorithmic approaches, with practical Python guidance.

Explore practical techniques to handle missing values in a churn modeling dataset using Python, pandas, and imputation methods like mean, median, mode, and forward or backward fill.

Learn how standardization converts data to z scores using the mean and standard deviation, with a step-by-step income example and a preview of sklearn's standard scaler.

Explore a min-max normalization example that scales income features to 0-1 to reduce bias in predictive models, with a preview of Python libraries for standardization.

Explore feature scaling with scikit-learn by applying normalization and standardization, using min max scaler and standard scaler on a numeric dataframe, imputing nulls with the mean.

Explore outlier treatment in data cleaning, learn detection methods—box plots, histograms, scatter plots, and normal distribution rules—and decide when to remove, replace, or keep anomalies in time series forecasting.

Explore identifying and treating outliers with the three-sigma method, not normally distributed data, and transformations, plus box plots and percentile analysis in Python.

Identify and clean invalid values in data by correcting formats, out-of-range values, and data types, and apply proper Unicode encoding when reading data with pandas to ensure accurate analysis.

Explore the two main data types, qualitative (categorical) and quantitative (numerical), with subtypes nominal vs ordinal and discrete vs continuous, plus practical examples.

Explore the different types of analysis and apply univariate analysis to a single variable, using categorical and numerical data and churn examples to reveal summary statistics.

Explore univariate analysis for both numerical and categorical data, using summary statistics and distribution insights to understand age, gender, and churn patterns in a telecom context.

Explore bivariate analysis of two variables, whether numerical, categorical, or mixed, using scatter plots, box plots, and churn insights from telco data.

Explore multivariate analysis to gain deeper insights by examining more than two variables, including combinations of categorical and numerical attributes within the exploratory data analysis framework.

Explore numerical data analysis with univariate and multivariate statistics, including mean, median, percentiles, min and max, plus correlations via box plots, heat maps, kernel density estimation plots, and histograms.

Explore univariate, bivariate, and numerical analysis with python to uncover churn insights in a telco dataset, using seaborn visuals, correlation heat maps, kernel density plots, and end-to-end case study.

Learn how derived metrics create new features from existing data to reveal insights, using techniques like feature binning and encoding to prepare categorical data for models.

Apply feature binning to convert continuous numerical data into categorical form, identifying missing values and outliers, and compare equal width and equal frequency binning with age examples.

Learn to perform feature binning on age using pandas cut, create labeled bins, validate results, and visualize with a bar chart while cleaning data by dropping unused columns.

Learn how to encode categorical features into numbers for predictive analytics, covering label encoding, one hot encoding, dummy encoding, and the use of get dummies in pandas.

Learn practical feature encoding techniques for preprocessing categorical data in a churn dataset. Apply label encoding, one-hot/dummy encoding, and target or hash encoders, with missing-value handling.

Perform end-to-end exploratory data analysis on churn data, from business problem framing and data cleaning to univariate, bivariate, and multivariate analyses, plus predictive modeling in Python.

Explore customer churn analysis with an end-to-end EDA, from importing libraries and loading the churn dataset to inspecting shape, data types, missing values, and churn distribution for actionable insights.

Learn practical data cleaning in pandas by converting invalid types to numeric and addressing null values, including drop or impute decisions. Apply feature binning to tenure for clearer insights.

Drive univariate analysis on a cleaned telecom dataset, visualizing gender, senior citizen, contract, and churn patterns with automated plots, and previewing numerical and bivariate analyses ahead.

Perform numerical and univariate churn analysis, encode categoricals, and analyze correlations and density plots to identify key predictors like monthly charges, total charges, and tenure.

Learn how to perform bivariate analysis for churn data by comparing churners and non churners across gender, partner status, and payment methods, using reusable plots and insights for EDA.

Learn to build an end-to-end EDA report for churn analysis, covering business understanding, data assessment, missing data handling, graph-driven insights, and final recommendations.

Learn how to install MySQL using MySQL Workbench and MySQL Installer, set a root password, and understand SQL databases, DBMS, and the client–server versus file-server architecture.

Explore file server versus client-server database architectures, compare locking and memory handling, discuss data loss risks and versioning, and prep for SQL and database administration.

Master the basics of sql, including structured query language, data types, and core sql commands such as select, insert, update, delete, create, drop, alter for relational databases.

Learn how SQL constraints govern data, including not null, unique, and primary key rules, and how foreign keys link tables like department and employee through practical table design.

From table basics to ddl, learn to create, alter, and drop tables in a relational database, with columns, data types, and keys.

Master data query language dql basics, including select queries and creating tables. Learn to filter results with where, like, in, and to use aliases with as.

Explore data manipulation language (dml) basics—insert, update, and delete—through table creation and querying examples, including insert syntax, where clauses, and type casting.

Explore how to join data across multiple tables using inner, left, right, and full outer joins, including self and cross joins, with practical employee and manager examples.

Explore importing data from external files like Excel, text, and JSON into SQL databases and exporting data for analysis. Use manual and command line approaches in MySQL Workbench.

Explore SQL aggregate functions and how group by, count, min, max, and average create data insights from examples like telco churn, gender, and contract.

Learn to use sql string functions like concat, trim, substr, mid, upper and lower, and manipulate string data for clearer analysis.

Learn to manipulate SQL date and time fields with functions like date diff, date format, day, month, year, quarter, and date add or subtract.

Apply regular expressions in SQL to replace the like operator and accurately search data. Learn about reg exp, pattern matching, character classes, and ranges with telco churn examples.

Views are virtual tables that do not store data but display data from other tables, hiding complexity and enabling access control, read-only and materialized views offering performance gains.

Learn how stored procedures in MySQL automate repetitive queries by saving code, accepting input parameters, and returning results with in and out parameters, including top players by goals.

Explore windows functions in SQL to compute row-wise totals using over and partition by. Learn row_number, rank, and first_value with practical examples such as total sales by product line.

Connect sql databases from Python using the pi mysql connector, import data with pandas, and perform basic exploratory data analysis, then explore integrating sql data into Power BI for visualization.

Explore the fundamentals and scope of machine learning, from regression, classification, and clustering to time series forecasting. Cover feature scaling, encoding, selection, dimensionality reduction, ensemble methods, deployment.

Explore artificial intelligence, machine learning, and data science, including how machine learning learns from data and the AI landscape, with types like unsupervised, supervised, and reinforcement learning.

Explore the four types of machine learning - supervised, unsupervised, semi-supervised, and reinforcement - along with core tasks like classification and regression, and practical examples such as fraud detection.

Explore machine learning and deep learning use cases, including supervised, unsupervised, and reinforcement learning, with examples like recommendation systems, natural language processing, fraud detection, and healthcare and banking domains.

Explore healthcare use cases for machine learning and deep learning, including better imaging, diagnostic techniques, disease detection, personalized treatment, fraud protection, and drug discovery.

Define features as predictors or independent variables in supervised learning, explain x and y variables with churn and temperature examples, and connect to predictive analytics.

Split data into training and test sets, typically an 80/20 split, train models on the training data, and evaluate with accuracy and metrics like mean absolute error.

learn how feature scaling cleans data and prepares features for predictive modeling and exploratory data analysis, using normalization and standardization, with z-scores and 0 to 1 ranges.

Explore standardization as a feature scaling technique, learn how to convert data to z-scores using the mean and standard deviation, and apply StandardScaler in Python with a sample dataset.

Learn how normalization uses the min-max formula to scale features and reduce bias. See a practical income example and a preview of Python libraries for standardization.

Explore feature encoding to convert categorical x variables into numerical inputs for predictive models. Learn label encoding, one hot encoding, and dummy encoding, including their use and practical examples.

upload and preprocess the churn data by handling nulls, dropping unnecessary columns, and applying label encoding, one hot encoding, and dummy encoding to convert categorical features into numeric representations.

Learn the basics of regression as a supervised learning technique that predicts a continuous dependent variable from independent variables, covering simple and multiple linear regression, lasso, ridge, and polynomial extensions.

Explore regression metrics to validate model performance, from data cleaning and train-test split to evaluating predictions with MAE, MSE, RMSE, R2 and adjusted R2 for simple and multiple linear regression.

Practically apply regression metrics on a 50 startups dataset, computing mae, mse, rmse, r2, and adjusted r2 with sklearn, including train-test split and feature scaling.

learn how simple linear regression models the relationship between area and price with a linear equation y = mx + c, and uses euclidean distance to fit the best line.

Explore multiple linear regression, using features like area, age, and location to predict price, with formula y = beta0 + beta1 X1 + beta2 X2 and plane or hyperplane fit.

Practice linear regression on the 50 startups dataset, performing simple and multiple regression with one hot encoding, train-test split, and feature scaling, then train, predict, and plot results.

Master practical multiple linear regression by generating a synthetic dataset with make_regression, training a linear model, and evaluating it with MAE, MSE, RMSE, and R2 on a test set.

Explore polynomial regression as a linear regression variant that uses polynomial features to model non-linear relationships. Understand degree choices, underfitting and overfitting, and how beta coefficients create the regression equation.

Experiment with polynomial regression to fit non linear data, compare linear vs polynomial models across degrees two, three, five, ten, and beyond, using train-test splits, R2 scores, and visualizations.

Identify the bias variance trade off in machine learning and distinguish underfitting and overfitting. Explore the sweet spot that balances bias and variance, and preview regularization, bagging, and boosting.

Learn ridge regression, a regularization method that penalizes large slopes to reduce overfitting and improve generalization, using lambda times slope squared in a two-feature model.

Learn how lasso regression uses L1 regularization to shrink coefficients, enable feature selection, and address overfitting, contrasting with ridge's L2 approach for better model generalization.

Explore practical ridge and lasso regression by building a linear model on the Boston data set, evaluating via the R2 score, and comparing models with different alpha values.

Explore binary and multi-class classification, with examples like fraud detection and disease prediction, and learn how models assign data to two classes or more using SVMs and logistic regression.

Learn how log loss evaluates prediction probabilities in binary classification, with intuition and a formula, and compare it to metrics like confusion matrix and recall using fraud and spam examples.

Explore the confusion matrix and its components—true positives, false positives, true negatives, false negatives—and learn recall, precision, F1 score, and accuracy, with fraud detection and medical examples.

Learn how the area under the ROC curve (AUC ROC) evaluates binary classifiers across thresholds, by plotting true positive rate versus false positive rate and relating to sensitivity and specificity.

Explore how the classification report in sklearn provides precision, recall, f1 score, and support, and interpret the minority class within imbalanced data using confusion matrix context.

Explore the k nearest neighbors algorithm, a simple supervised method for classification and regression that uses Euclidean distance and a chosen k to vote on the class.

Use a kNN classifier on age and dummy-encoded gender, compute Euclidean distance to select nearest neighbors, and predict cricket or football for a case like Sathyajith, age 30, male.

Learn to build a k-nearest neighbors classification model in Google Colab, performing data cleaning, one-hot encoding with dummy features, train-test split, and standardization for scalable predictions.

Explore how to build a kNN classifier, preprocess data with dummy encoding and scaling, evaluate accuracy, and predict churn on new data.

Examine decision trees as a supervised learning model for classification and regression, from root node to leaf node, covering features, splits, and impurity measures like entropy and Gini.

Explore an entropy-based decision tree to identify the root node and predict profit or loss using features like age, competition type, and time, and implement it in Python.

Explore how the Gini index and entropy drive decision tree classifiers, using age, competition, and type as features to predict profit or loss.

Explore practical decision tree classification by building, evaluating, and comparing with KNN, handling missing values, encoding, and train-test splits, then plan for hyperparameter tuning and future models like random forest.

Visualize decision tree classifiers and compare gini versus entropy to understand root, internal, and leaf nodes in churn predictions. Use plot_tree and graphviz to simplify trees, explore max_depth, and accuracy.

Explore random forest as an ensemble learning method that combines multiple decision trees, built from randomized data samples and feature subsets, to improve classification accuracy through majority voting.

Explore implementing the random forest classifier within an ensemble, compare its 79% accuracy to knn and decision tree, and learn hyperparameter tuning with estimators.

Explore naive Bayes classifiers built on Bayes' theorem, assuming feature independence, and apply them to a play decision example with day, temperature, humidity, and wind.

Understand the mathematics of support vector machines and hyperplanes. Learn the generalized hyperplane equation w^T x + w0 = 0 and the intercept and dot product.

Explore logistic regression as a supervised binary classifier, modeling p(y=1) with a sigmoid function and coefficients for multiple features. See practical encoding and use cases such as fraud detection.

Explore practical classification with KNN, decision trees, random forest, Naive Bayes, SVM, and logistic regression on a churn dataset, including eda, encoding, train-test split, and model comparison.

Explore underfitting and overfitting as key classification issues, and examine how training versus testing data, pattern differences, and class imbalance affect model generalization.

Learn how to tackle imbalanced datasets in classification with oversampling and undersampling, and explore random oversampling, SMOTE variants, Tomek links, ADASYN, and precision-recall evaluation.

Explore ensemble learning as a group approach that combines multiple models for better predictions. Learn voting, stacking, bagging, boosting, and key examples like random forest and diverse base algorithms.

Bagging combines bootstrapped training samples of diverse base models with aggregation voting to reduce bias and variance, with random forest and extra trees as key examples.

Compare bagging and random forest ensembles, and observe how max features influence splits in each model. Visualize the trees using a generated classification dataset.

Explore bagging and ensemble methods by implementing bagging classifiers, random forests, and pasting on synthetic data, compare accuracies, and discuss key parameters such as n estimators and max samples.

Bagging extends to regression by averaging outputs from multiple regressors—decision tree regressor, svr, dnn regressor, and random forest regressor—using bootstrap and pasting.

Discover boosting, a sequential ensemble that fixes mispredictions of weak learners to create a strong predictor, contrasting with bagging's parallel approach, including gradient boosting, AdaBoost, CatBoost, and extreme gradient boosting.

Discover how AdaBoost combines weak learners, updates sample weights, and uses decision stumps to build a strong classifier through additive boosting.

Gradient boosting trains models sequentially to convert weak learners into a strong one via loss optimization with cross entropy, unlike adaboost’s weight updates and decision stumps.

Explain the difference between loss and cost functions, showing how losses quantify prediction error and how costs sum these losses, with regression examples and notes on cross entropy and XGBoost.

Explore cross entropy, its use as a loss function in classification, and its binary and multi-class forms, with a practical weather example.

Explore XGBoost, the extreme gradient boosting algorithm, an ultra-powerful ensemble method for regression, classification, and ranking, featuring parallel processing, optimized data structures, GPU support, and Kaggle competitions.

Explore ensemble methods for churn prediction, comparing random forest, AdaBoost, gradient boosting, and XGBoost using encoding, scaling, and train-test splits, with accuracy as the focus and hyperparameter optimization ahead.

Explore clustering algorithms in unsupervised learning to group similar data into clusters, compare k-means and hierarchical methods, and apply semi-supervised strategies to fraud detection and segmentation.

Explore k-means clustering, an unsupervised learning method, using centroids and euclidean distance on features like annual income and spending score, with elbow method to choose k.

Learn practical k-means clustering with the elbow method to form customer segments from spending score and annual income, visualize five clusters, and compare to hierarchical clustering.

Explore hierarchical clustering, an agglomerative method that builds a dendrogram illustrating the hierarchy of clusters from single data points to a global cluster, using euclidean distance.

Apply hierarchical clustering with dendrograms from SciPy to partition data using annual income and spending score into four or five clusters, comparing agglomerative clustering to k-means and visualizing results.

Explore mean shift clustering, a non-parametric, density-based algorithm that discovers clusters without predefining their number. Learn how bandwidth settings shift centroids and enable practical Python implementations with sklearn.

Discover dimensionality reduction through feature engineering and feature selection, including domain-level reduction, wrapper, filtering, and embedded methods plus PCA, LDA, and kernel PCA to boost model performance before coding.

Learn recursive feature elimination and sequential feature selector to prune features, improve model performance, and reduce computational complexity with practical demonstrations in sklearn and ML trend.

Explore practical recursive feature elimination for feature selection with logistic regression, including data preparation (encoding and missing values), RFE execution, and comparing reduced-feature models to full models.

Explore recursive feature elimination and sequential feature selector on a telco churn dataset with logistic regression, showing how five features can match near 79% accuracy.

Explore chi square based feature selection to identify statistically significant associations between categorical features and the target, enabling reduced dimensionality, interpretability, and a Python implementation.

Explore chi square based feature selection to identify the top five features for the y variable and evaluate a logistic regression model with SelectKBest.

Learn how principal component analysis reduces dimensionality to two or three components, creating new features (PC1, PC2) that retain variance and help visualization and prevent overfitting.

Implement the practical PCA workflow in Python, including data cleaning, dummy encoding, and a train-test split, then apply PCA to reduce 30 features to principal components. Compare PCA with LDA, kernel PCA, and QDA, analyze explained variance, and evaluate logistic regression accuracy to understand PCA's impact.

Explore linear discriminant analysis, a supervised dimensionality reduction method that uses Y train to maximize class separability and minimize within‑class scatter, based on class distribution, unlike PCA.

Explore lda practicals, compare lda with pca, reuse code, perform data cleaning and encoding, train and test models, and assess accuracy, with notes on kernel pca and qda.

Explore kernel PCA and quadratic discriminant analysis as nonlinear extensions of PCA and LDA. Apply them to real-world data using sklearn to compare performance.

Explore Kernel PCA and QDA for feature engineering and dimensionality reduction, compare with PCA and LDA on linear vs non-linear data, and assess performance.

Explore the basics of hyperparameter optimization, including why hyperparameters matter, pre-training settings, and grid, random, manual, and Bayesian search methods to boost model validation accuracy.

Explore the four common hyperparameter optimization methods: manual, grid search CV, random search CV, and Bayesian approach, and discuss manual HPO workflows and their drawbacks.

Explore grid search and randomized search for hyperparameter optimization, comparing exhaustive sweeps with probabilistic sampling to speed tuning, and discuss practical trade-offs for max depth, criterion, and min samples split.

Explore manual hyperparameter optimization on a breast cancer classification task with a random forest, including data prep, feature scaling, train/test split, and accuracy evaluation, with grid and randomized search upcoming.

Utilize randomized search CV to tune a random forest's hyperparameters—n_estimators, max_depth, min_samples_split, bootstrap—by sampling 100 iterations from 15,400 combinations and evaluating with threefold cross-validation to improve accuracy.

Discover how grid search cross-validation optimizes hyperparameters, compare grid search with randomized search, and improve model accuracy from 93% to about 94% using real-world parameter grids.

Identify trends, patterns, relationships, and seasonality in time series data and forecast future values using ARIMA, SARIMA, Facebook Prophet, and LSTM. Learn data preparation, cleaning, and visualization for interpretation.

Differentiate time series from regression by the presence of a date column and intervals, applying ARIMA, SARIMA, LSTM, or Prophet for time series, and linear or tree regressors for regression.

Learn time series analysis and forecasting, distinguish it from regression, and prepare time-based data with a consistent date interval for accurate predictions. Preview anomaly detection as a prerequisite.

Explore anomaly detection in time series, learn how outliers affect patterns, and compare methods like ARIMA, Facebook Prophet, and LSTMs that handle anomalies.

Explore the four time series components: trend, seasonality, irregularity, and cyclic, and learn how to detect them using decomposition and autocorrelation for accurate pattern analysis.

Explore time series decomposition to break data into trend, seasonality, irregularity, and cyclic components, and learn to apply additive and multiplicative methods using Python and the Statsmodels library.

Explore time series decomposition with statsmodels, loading data, and applying seasonal decomposition to extract trends, seasonality, and residuals using additive and multiplicative models.

Explore additive and multiplicative decomposition of time series to separate trend, seasonality, and residuals, and learn when to apply each method based on seasonality relative to the trend.

Stationarity keeps a time series' mean and variance constant, enabling reliable forecasting; use differencing, log transformations, and tests like ADF or KPSS to confirm and reduce trend and seasonality.

Learn how to test time series stationarity, remove trend and seasonality for models like ARIMA, and apply the augmented Dickey-Fuller test with p-values to assess stationarity.

Learn to transform time series to stationarity by removing seasonal and trend components using differencing, logarithmic, and smoothing techniques; assess with ADF tests and p-values, then apply inverse transformations.

Pre-process and clean time series data by handling missing values, removing duplicates, and addressing outliers, then apply feature scaling, encoding, and feature engineering for forecasting.

Explore techniques to handle missing values in data cleaning, including deleting rows or columns and imputing with mean, median, or mode, plus algorithmic imputation and Python basics.

Learn practical techniques for handling missing values in a churn modeling dataset with pandas, including dropping data, forward/backward fill, and imputing with mean, median, or mode.

Identify outliers with the three-sigma rule in data following a normal distribution, then apply log and double-log transformations to reduce skewness, and compare box plots and percentiles.

Explore feature scaling as a data cleaning step essential for predictive modeling and EDA, using normalization and standardization to rescale features by distribution.

Learn standardization as a feature scaling technique by converting data to z-scores using mu and sigma, so standardized values center at zero with most data within ±3.

Understand normalization as a feature scaling method, applying the x minus x min over x max minus x min formula to map income values to 0–1.

Learn feature scaling in Python using scikit-learn to standardize and normalize data with StandardScaler and MinMaxScaler, including mean imputation for missing values and applying to age and tenure.

Explore feature encoding for predictive analytics by transforming categorical features into numerical values using label encoding, one hot encoding, and dummy encoding, with Pandas get_dummies examples and drop options.

Perform practical feature encoding on a churn modeling dataset by cleaning data in pandas, handling missing values, and applying label and one-hot (dummy) encoding with sklearn and get_dummies.

Explore popular time series forecasting algorithms—ARIMA and its variants, SARIMA, ARIMAX, ARMA, VAR, GARCH, Prophet, LSTMs, and Holt’s winter smoothing—with practical insights for forecasting.

Discover the autoregressive integrated moving average (arima) framework for forecasting time series with trend, seasonality, and autocorrelation. See how ar, ma, and differencing (p, d, q) yield stationary, accurate predictions.

Learn how AR, MA, ARMA, and ARIMA models differ and how p, D, and q shape forecasts. Explore stationarity, differencing, and inverse transformations that anchor reliable predictions.

Explore the autoregressive component of ARIMA, uncovering what p means and how past values influence forecasts. Learn how partial autocorrelation (PACF) guides AR model selection and interpretation.

Explore the mathematics behind the Maa model and how the moving average component uses past error terms to forecast.

Deep dive into autocorrelation analysis using ACF and PACF plots to determine AR and MA orders and help identify p, d, q in ARIMA models.

Explore how to determine ARIMA p, d, q using grid search, comparing manual ACF/PACF identification with automated Auto.arima, and applying RMSE to select the best parameters.

Explore arima practicals with air passengers data, examining acf and pacf, applying adf test, and transformations like differencing and log differencing to forecast future passengers with multiplicative seasonality.

Assess time series stationarity with the augmented Dickey-Fuller test, apply transformations for non-stationary data, and forecast with ARIMA models.

Apply transformations such as log, double log, and differencing to make time series stationary, test using stationarity tests, and use inverse transformations to obtain final forecasts in ARIMA modeling.

Explore decomposition in time series using Statsmodels' seasonal_decompose, separating trend, seasonality, and residuals with moving averages, and assess stationarity before moving to ACF and PACF for model selection.

Explore why we use log transformations in time series and how to read ACF and PACF to estimate p and q for ARIMA, with grid search for production.

Explore end-to-end time series transformations and their inverses, focusing on log, double log, and log differencing, with numpy exp and cumsum techniques for restoration and testing.

Master grid search for arima time-series models by testing p, d, q combinations, tracking rmse, and identifying the best p, d, q values while noting time costs and automation tips.

Learners explore training-test split, log transformation, ARIMA forecasting with step forecasting, and evaluating predictions for deployment and end-to-end use cases.

Explore Facebook Prophet for time series forecasting, featuring fast additive regression with yearly, weekly, and daily seasonality, holiday effects, and robust handling of missing data and outliers.

Train and forecast with Facebook Prophet on air passengers data, applying log transformation, fitting the model, generating future predictions with y hat, lower, and upper, along with trend and seasonality.

Learn how to incorporate holiday effects into the Facebook Prophet model by adding a holidays data frame or built-in country holidays, then fit, forecast, and compare with earlier models.

Delve into multivariate time series analysis, contrasting univariate forecasting, linking multiple features with VAR and Facebook Prophet using ad regressor to capture interactions and improve forecasts.

Explore univariate and multivariate forecasting with Facebook Prophet, applying log transformations, inverse transformations, and added regressors like humidity, wind speed, and mean pressure.

Learn to evaluate forecasting performance by comparing trained time series models like arima and prophet using metrics such as mae, mse, rmse, and mape to guide model selection and improvement.

Explore mean absolute error as a forecasting evaluation metric by calculating the average absolute difference between actual values and predictions. See how to apply the formula with y and y_hat.

Explore mean squared error, the metric for the average of squared differences between actual and predicted values using the MSE formula. Note that MSE, MA, and RMSE are distinct metrics.

Learn that RMSE is the square root of MSE and use it to compare regression models, selecting the model with the lowest RMSE.

Introduce mean absolute percentage error (MAPE) as a forecasting and regression metric, explain its formula, and show a numeric example to compare models by the lowest MAPE.

Explore energy demand forecasting with ARIMA, using time series data from Kaggle to account for seasonalities and irregularities, outlining an end-to-end project for a utility company.

Explore loading time series data with pandas, visualizing load and solar generation, converting timestamps, and assessing trend and seasonality before end-to-end arima forecasting, including stationarity tests and missing value handling.

Forecast stock prices using univariate time series with Facebook Prophet, applying Bayesian regression to historical data and volumes to generate predictions that inform trading strategies.

Leverage Prophet for stock market forecasting by migrating code from Jupyter to Google Colab, fitting a daily-seasonality model and forecasting 365 days, with trend and weekly seasonality insights.

Forecast Tesla stock prices using time series analysis and Facebook Prophet, after validating data quality, exploring trends, volatility, and moving averages to guide predictions.

Explore demand forecasting for e-commerce data by building and comparing ARIMA, Holt's Winter, and Facebook Prophet models using time series analysis, with training-test splits and feature engineering.

Explore end-to-end demand forecasting in Google Colab by cleaning data, imputing missing values with median, aggregating weekly, and performing EDA.

Analyze time series components by decomposing trend, seasonality, and residuals to assess stationarity and volatility, then apply moving averages and prepare for autocorrelation modeling in demand forecasting.

Visualize and interpret autocorrelation plots to confirm seasonality in weekly time series, using pandas, and prepare for the upcoming model-building in demand forecasting.

Explore demand forecasting with time series methods by cleaning data, EDA, division into training and test, and evaluating Holt's winter, ARIMA, and Facebook profit models using RMSE.

The lecture compares ARIMA, Holt-Winters, and Prophet for Facebook profit forecasting, notes messy data and spikes, evaluates RMSE and plots, and demonstrates Plotly plotting with a path toward Streamlit deployment.

Explore deep learning and neural networks, including neurons, activation functions, gradient descent, and architectures like CNN and RNN, with hands-on projects, hyperparameter tuning, transfer learning, and deployment.

Understand how deep learning differs from machine learning, why data and neural networks matter, and how the brain-inspired approach, Geoffrey Hinton's work, uses cat and dog examples to advance AI.

Discover the neuron as the core unit of neural networks, with inputs and weights, leading to an activation function and forward propagation across input, hidden, and output layers.

Explore activation functions in deep learning, learning how weighted sums plus bias determine neuron firing across layers, with examples of step, linear, sigmoid, tanh, and ReLU.

Learn the step function as a threshold based activation that activates outputs above a threshold in binary classification. It cannot handle multi-class problems like iris classification.

Explore the sigmoid activation function, a non-linear 0 to 1 output function used in deep neural networks, especially for output layers, contrasting it with ReLU, tanh, and softmax.

Explore the tanh activation function, which ranges from -1 to 1 and is a scaled form of the sigmoid, offering non-linear behavior.

Explore the ReLU activation function and its role in mitigating vanishing and exploding gradients in neural networks, contrasting it with sigmoid and tanh, and leaky ReLU to prevent dying ReLU.

Understand forward pass and backward propagation in neural networks, illustrated with a churn prediction example. See how weights update through batch size, iterations, epochs, and learning rate using gradient descent.

Explore gradient descent and stochastic gradient descent, linking forward and backward passes to cost function decay, weight updates, and the journey from local to global minima in neural networks.

Explore how artificial neural networks assemble input, hidden, and output layers to model data; learn to set feature counts and hidden units, and compare with traditional methods on churn analysis.

Apply churn modeling on a real dataset by comparing traditional machine learning models with an artificial neural network, including one-hot encoding and feature scaling after train/test split.

Discover hyperparameter optimization after feature engineering using manual, randomized, and grid search to select the best batch size, epochs, and optimizer for a Keras classifier with cross validation.

Explore convolutional neural networks (CNNs) and how 2D CNNs process images, voices, and videos with learnable weights, covering the core steps: convolution, ReLU, pooling, flattening, and fully connected layers.

Explore how convolution drives CNNs as a dimensionality reduction step that preserves features. Follow with ReLU, pooling, flattening, and a fully connected output layer for multi-class image classification.

Explore how convolutional neural networks extract features using 3x3 filters on a 7x7 image, apply ReLU, pool, flatten, and connect to fully connected layers.

Learn image augmentation to balance imbalanced data and boost cnn performance. Use resizing, flipping, rotation, noise, blur, and zoom to generate diverse training images.

Explore how epoch, batch size, and iterations govern neural network training, guided by gradient descent, forward and backward passes, and data diversity.

Build a cnn model on fashion mnist with keras and tensorflow, using conv2d, max pooling, flattening, dense layers, dropout, and a 10-class softmax classifier for 28 by 28 grayscale images.

Review the convolutional neural network model summary, detailing layers, 32 filters of 3x3 with stride one, 50% pooling, flattening to 800 features, and connected layers 128, 64, 10.

Build a convolutional neural network for x-ray image classification to detect pneumonia. Prepare grayscale 100x100 images, train with TensorFlow, and save the model.

Learn the basics of recurrent neural networks, their architecture with x_t, h_t, y_t, and weights w_i, w_r, w_y, and how they model dependencies in time series and text prediction.

Discover the four types of recurrent neural networks: 1 to 1, 1 to many, many to one, and many to many. Learn with examples in image classification, image captioning, sentiment analysis, and translation.

Explore vanishing and exploding gradients in neural networks, and learn remedies like ReLU, RMSprop, LSTMs, and truncated backpropagation to stabilize learning.

Discover how long short-term memory networks overcome vanishing and exploding gradients using input, forget, and output gates with an internal state, enabling short- and long-term learning for sequences.

Explore practical stock price prediction with Keras LSTM, using 60-time-step sequences, mean max scaler normalization, stacked LSTM layers with dropout, and a regression training setup.

Discover how pre-trained models in deep learning save time and resources by enabling transfer learning, fine-tuning or use as feature extractors for image classification with ImageNet-based architectures like VGG16.

Explore practical use of pre-trained models for X-ray image classification by building two CNNs, one from scratch and one with a pre-trained base such as VGG16, and compare accuracies.

Explore pre-trained models, with a focus on Vgg16's 16 learnable layers, transfer learning, and its 1000-class image classification architecture.

Explore depthwise and pointwise convolutions using a simple image analogy. See how MobileNet's depthwise separable design enables small, fast CNN classifiers for ImageNet tasks.

Transfer learning uses a pre-trained model as a base and fine tunes it for a new task, boosting performance and saving training time while avoiding overfitting in image classification.

Develop and deploy a pneumonia detection model from X-ray images using CNNs and pre-trained networks like VGG16, achieving 87% accuracy, and launch a Streamlit app for image upload and predictions.

Master natural language processing foundations and its role in generative AI, from text to numbers and embeddings, enabling computers to understand and generate language for chatbots and translation.

Explore the basics of natural language processing and its AI connections, cover translation, sentiment analysis, and chatbots, and introduce building PDF-based chatbots with large language models.

Explore the key NLP challenges, including pragmatic, lexical, syntactic, and anaphoric ambiguity, along with issues of standardization, ethical considerations, context understanding, and data sparsity.

Explore linguistics basics, including phonetics and phonology, morphology with morphemes, syntax rules, and semantics, and how these foundations support NLP applications like named entity recognition and machine translation.

Learn NLP basics with case folding, converting text to lowercase to normalize data and reduce vocabulary, used by search engines, while noting cases where nouns or acronyms may be affected.

Remove special characters to clean text data, reduce noise, and improve tokenization in NLP preprocessing, using regex, spaCy, and NLTK.

Learn to handle contractions in nlp. Expand them via the contractions library or regex and apply contraction handling during preprocessing with stopword and special character removal.

Learn how tokenization breaks text into sentences and words, and explore sentence, word, subword, and character tokenizers with practical Python demos using NLTK.

Learn how stop words affect NLP and how to remove them to boost text analysis. The lecture covers English stopwords and a Python example using NLTK for tokenization and preprocessing.

Learn how textual data is converted into numerical vectors for machine learning through vectorization. Explore one-hot encoding, bag of words, tf-idf, and word embeddings like Word2Vec, GloVe, and FastText.

Explore word embeddings as numerical representations that capture semantic similarity and context, from count-based models like bag of words and tf-idf to prediction-based methods like word2vec, cbow, and skip-grams.

Learn bag of words, a simple vectorization method that converts text to word-frequency features for NLP, while recognizing its practical drawbacks and that TF-IDF will be covered later.

Apply bag of words practically by building a spam detector with count vectorizer, train/test split, and Multinomial Naive Bayes, then evaluate with a classification report.

Explore tf-idf, a term weighting method that uses term frequency and inverse document frequency to highlight significant terms while downplaying common words, with practical calculation steps.

Explore practical tf-idf with sklearn's vectorizer, transform a corpus, view vocabulary and idf scores, and train a text classification model on labeled data.

Explore part of speech tagging and named entity recognition, and learn how POS tagging pre-processes NLP tasks with spaCy, including Hidden Markov and Viterbi concepts.

Practice named entity recognition with spaCy, identifying entities and labels like Tesla as an organization and dollar amounts. Learn to customize entities by editing doc ents and render ner results.

Introduce word embeddings with word2vec, compare bag-of-words and one-hot encoding, discuss sparsity and similarity limits, and cover cbow and skip-gram prediction-based models and pre-trained options.

Compare count-based embeddings like bag of words and tf-idf with prediction-based word2vec, cbow and skip-gram, and explore pre-trained models such as GPT and ChatGPT.

Explore pre-trained word2vec embeddings from Google News vectors using gensim, examining 300-dimensional word vectors and concepts like cosine similarity, word similarity, and king minus man plus woman analogy.

Explore word2vec intuition by building simple feature vectors for king, queen, man, woman, and elephant, showing king minus man plus woman yields queen, and discuss automating features with neural nets.

Explains the continuous bag of words cbow approach for word embeddings, where context predicts the target word using a neural network, one-hot inputs, window size, softmax output, and backpropagation training.

Explore the skip gram model as the reverse of cbow, predicting context words from target words using one-hot encodings and a neural network, with training via backpropagation and softmax.

Explore glove, a classic word embedding method using a word-to-word co-occurrence probability matrix to derive word vectors in NLP.

Explore how fasttext improves word embeddings over word2vec by using character-level or n-gram units to handle out-of-vocabulary terms, especially in non-english and domain-specific data.

Compare glove and fasttext word embeddings in a practical NLP session, loading pre-trained models with gensim and evaluating semantic similarity using the word similarity 353 dataset.

Compare glove and fasttext on word similarity tasks, evaluating Pearson and Spearman correlations and out-of-vocabulary ratio, with fasttext generally outperforming glove.

Explore cosine similarity between text vectors, using dot products and angles to measure word and sentence similarity, with bag-of-words examples and nlp applications like resume filtering.

Understand how LSTMs address the short-term memory limits of RNNs, incorporating long short term memory, forget gates, and architecture differences that enable learning from sequential and textual data.

Deep dive into LSTM architecture, defining ct, dt, ht, f_t, i_t, o_t, ct dash, and input, forget, and output gates, with emphasis on identical vector dimensions across time steps.

Explore pointwise operations inside lstm cells, including multiplication, addition, and tanh activations, and how sigmoid gates drive forget, input, and output gates.

Explore the forget gate in lstm networks, showing how previous hidden state, previous cell state, and input feed a sigmoid f_t to forget parts of memory and update c_t.

Explore the LSTM input gate: compute a candidate cell state with tanh, gate it with a sigmoid, then update the cell state as f_t times C_{t-1} plus i_t times C_t_dash.

Explore the LSTM output gate, derive ot with a sigmoid on ht-1 and xt, then compute ht as ot times tanh(kt), connecting ct to the previous state.

Apply LSTM to a language task by building a next word predictor from a text corpus, converting text to numeric sequences with a tokenizer and padding, framed as supervised classification.

Build a sequential neural network with embedding, LSTM, and dense layers. Use 119x33 inputs, 119x88 outputs, 100-dimensional embeddings, and 150 LSTM units with a softmax final layer.

Train a TensorFlow sequential LSTM model with embedding and tokenization, using pad sequences to map 119 samples of 33 features to 88 outputs, achieving about 97% training accuracy.

Discover gated recurrent unit architecture, compare GRU with LSTM, and learn when GRU or LSTM performs better, with emphasis on training time and fewer parameters.

GRU uses a single hidden state to carry long term and short term context, with two gates, reset and update, while having no cell state.

Explore how the reset gate in GRUs blends h_{t-1} and x_t through a sigmoid to form r_t, acting as short term memory and shaping the candidate hidden state.

Explore the update gate in gru, derive z_t from h_{t-1} and x_t using sigmoid over their concatenation, and compute h_t from a blend of h_{t-1} and the candidate h_t bar.

Explore GRU practicals, compare with LSTM, and replicate an LSTM notebook by swapping in a GRU unit to train on tokenized text with Keras preprocessing, padding, and TensorFlow.

Learn bidirectional RNNs and bidirectional LSTMs that use future context to boost named entity recognition and other sequence tasks.

Explore the three transformer types: encoder-only, encoder-decoder, and decoder-only, and their roles in classification, translation, and text generation in natural language processing.

Explore how transformers revolutionize NLP by enabling sequence-to-sequence learning with encoder-decoder architectures and self-attention, powering chatbots, translation, and multimodal AI.

Explore self attention within transformers, showing how encoder–decoder architectures use attention weights to focus on relevant words, converting words to vectors and leveraging cosine similarity.

Explore the encoder architecture and Bert’s bidirectional encoder representation for transformers, and learn about decoder architecture, pre-training, fine-tuning, input embeddings, language models, and contextual embeddings versus hidden states.

Explore contextual embeddings and how they capture meaning from left and right context, distinguishing them from hidden states in RNNs. Learn bidirectional context to disambiguate words like bank and Paris.

Understand decoder architecture as the next-token predictor that builds on encoder architecture, using prompts, embeddings, positional encoding, and a context vector to generate final outputs.

Explore Bert, a bidirectional encoder for transformers, and how it enables sentiment analysis, text classification, next-word prediction, and question answering through contextual embedding.

Explore Bert configurations across base, large, tiny, mini, small, and medium models, detailing encoder counts, attention heads, and hidden units to guide model selection.

Explore how fine tuning a pre-trained BERT model, trained with mask language modeling and next sentence prediction, adds final layers and updates weights for a use case with limited data.

Learn how mask language modeling pre-trains BERT by masking tokens, feeding three input types (masked, random, unchanged), and using bidirectional context from CLS and SEP tokens for accurate predictions.

Discover how Bert builds input representations by combining token, segment, and positional embeddings, including cls token and separator tokens, to form the final embedding sent to the transformer encoders.

Compare autoregressive language models and autoencoder language models, highlighting unidirectional generation (gpt) versus bidirectional embeddings (bert) for summarization, translation, and generation tasks.

DistilBert reduces Bert to 40% of its size and up to 60% faster, preserving 97% of its capabilities. It uses MLM pretraining and three losses, trained on 16 GB data.

Albert, a light Bert variant, reduces parameters through cross parameter sharing and embedding factorization, introduces sentence order prediction, and enables faster training with shared encoder weights.

Explore decoder-only GPT as a generative pre-trained transformer, highlighting embedding, masked multi-head attention blocks, and the prompt-driven input that yields the next token output.

Explore the GPT decoder architecture, from prompt embeddings and positional encoding to masked multi-head attention, normalization, and feed-forward blocks, ending with the output probabilities.

Learn how self-attention drives transformers by computing attention weights from queries, keys, and values via softmax, then apply masked multi-head attention with concatenated heads in the decoder.

Explore how transformer decoder blocks process a prompt with embeddings and positional encoding, using masked multi-head attention, a ReLU feed-forward network with dropout, and add-and-normalization with skip connections.

Explore GPT training from basic self-supervised training to fine tuning, using pre-trained models and backpropagation to tailor language generation and sentiment analysis tasks.

Explains the context window in large language models, including tokenization, input/output token limits for GPT 3.5 and GPT 4, and how Rag enables retrieval augmented generation.

Explore prompts in large language models, how prompts are tokenized and processed by decoder architectures like GPT. Learn how attention, token limits, and potential hallucinations shape generated outputs.

Prompt engineering designs inputs for large language models to maximize outputs by shaping context, task, persona, format, executor, and tone, using cooking-like stepwise prompts.

Learn prompt tuning to improve language models by refining a prompt parameter set. Compare with model fine tuning and hyperparameter optimization, and cover nlp basics, transformers, rags, and prompt engineering.

Explore prompt structures for LLMs, including action verbs, topics, constraints, background context, and challenges, with examples like writing a story and generating Python EDA code.

Explore the concept of retrieval augmented generation (RAG), its uses in custom data, chatbots, and PDF-based models, and its role in knowledge intensive NLP tasks with practical implementation.

Explore retrieval augmented generation (rag) and how it combines retrieval, augmentation, and generation to improve output quality from LLMs using custom data, embeddings, and vector databases.

Discover practical use cases of retrieval augmented generation (RAGs) for customer support, document tutoring, resume guidance, and AI-powered claims processing using custom data from PDFs and websites.

Explore retrieval augmented generation (rag) for knowledge intensive natural language processing tasks. Learn to integrate external knowledge from a custom dataset using embeddings and a hybrid parametric–nonparametric memory.

Explore rag architectures for turning PDFs into text with large language models, chunking data, creating embeddings, and storing them in a vector database for context-based answers.

Explore the detailed retrieval augmented generation architecture for documents, outlining pre-processing, chunking, embedding creation, vector databases, and similarity-based retrieval to drive LLM outputs.

Explore LangChain, a framework to build apps powered by language models, connecting LLMs to data sources, embeddings, and vector stores for retrieval augmented generation.

Master prompt engineering for non-technical learners to interact with large language models like ChatGPT, using tokens, vectors, and memory across four components—instruction, context, input data, and output indicator.

Explore zero shot, one shot, and few shot prompting, with examples across translation and script writing, and learn how in-context demonstrations steer large language models.

Explore chain of thought prompting, a technique where large language models explain reasoning step by step by breaking problems into subproblems.

Explore how vector databases store text as embeddings, use cosine similarity and locality sensitive hashing to enable fast semantic search for large language models.

Compare vector index and vector database, highlighting embeddings with faiss, metadata storage, scalability, real-time updates, backups, and data security to guide when to use a database.

Explore how vector databases use similarity search to retrieve nearest neighbor vectors, covering indexing, querying, post processing, and trade-offs between speed and accuracy.

Explore using a vector database like Pinecone to store document embeddings, perform cosine similarity searches, and enable retrieval augmented generation for knowledge-intensive NLP tasks with chat completion.

Learn how locality sensitive hashing enables fast vector database searches by bucketing similar document vectors through shingling, min-hashing, and Jaccard similarity to reduce comparisons.

explore Ollama, an open source tool to run large language models on local hardware, enabling private, cost-efficient, low-latency ai with rag-based retrieval and flexible customization.

Install Ollama, launch Tiny Llama on your Windows or mac. Learn to run models with pull or run, and review prerequisites, quantization, and context length for local Gen AI experiments.

Test and compare models with Ollama, installing alternatives to Tiny Llama and running test cases for translation, sentiment analysis, and classification across local and AWS environments.

Import Ollama in Python, initialize a tiny llama model, and generate text in a Jupyter notebook to demonstrate practical usage and common debugging pitfalls.

Explore retrieval augmented generation with Ollama to answer from your own documents and databases, reducing hallucinations through embeddings and a vector store.

Learn to build a rag system with ollama to answer questions from a research paper using pdf loading, chunking, Hugging Face embeddings, and a conversational retrieval chain.

Explore how major LLM API providers like OpenAI and grok power text, image, and video models; learn to obtain API keys and build RAG pipelines with grok and OpenAI.

Explore RAG systems with xAI and LLM APIs by building a retrieval augmented generation workflow using chunking, embeddings, and a vector database to answer questions from research papers.

Explore practical RAG with xAI using LLM APIs, implementing a conversational retrieval chain for custom data, and evaluating models with human validation, using embeddings from Hugging Face or OpenAI.