ML & MLOps Masters 2026 - Build, Train, Evaluate, Deployment

Master Python installation and environment setup with options like python.org downloads, Anaconda Navigator, Jupyter Notebook, VS Code or Spyder, and Google Colab for practice.

Learn to use Google Colab for python practice, avoiding setup issues with ipynb notebooks. Create notebooks, run code with shift-enter, text blocks, and upload files while exploring lists and tuples.

Learn to debug without an instructor using ChatGPT and other AI tools to diagnose and fix common Python errors in data analytics and data science.

Explore Python basics by understanding variables and keywords, learn installation options via python.org, Anaconda Navigator, or Colab, and explore types with print and type.

Master data types, operators, and operands in Python, including numeric, dictionaries, booleans, and sequences; explore type casting, input, string immutability, and operator precedence.

Learn Python data structures—lists, tuples, sets, dictionaries—with emphasis on lists mutability, indexing, slicing, and nested lists, plus extend, append, pop, remove, sort, and shallow copy.

Master tuples in Python: immutable sequences, unlike lists, defined with round brackets or comma notation. Learn indexing, slicing, concatenation, nesting, and practical uses, including length, access, and sorting workarounds.

Master Python loops and iterations, covering for and while loops, iterables (lists, strings, dictionaries, sets), iterators, and list comprehensions for practical examples from odd/even checks to dictionary iteration.

Master Python functions, from built-ins to user-defined and lambda forms, and apply map, filter, and reduce with practical examples like BMI calculations and even/odd checks.

Explore the fundamentals of object oriented programming in Python, including classes, objects, constructors, and instance and class variables. Learn about inheritance, polymorphism, encapsulation, and practical examples with rectangles and circles.

Explore NumPy, the numerical Python library for fast multi-dimensional ndarrays, including creating zeros, ones, full, identity, and random arrays, with indexing, shaping, and basic arithmetic using the np alias.

Explore pandas basics for Python data analysis: create and inspect dataframes, read files, use head, info, describe, select columns, filter with conditions, handle nulls, and perform joins and merges.

Learn to plot diverse charts with matplotlib in Python, using pandas data frames, from histograms and bar charts to pie charts and scatter plots, with code practices.

Explore the basics of statistics and its role in data analytics and data science, using charts and scatter plots to compare age and salary, and distinguish population from sample.

Explore descriptive statistics in the ML & MLOps masters 2026 course, focusing on central tendency and variability, including mean, median, mode, variance, standard deviation, and frequency distribution for data sets.

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

Explore quantitative data, defined as numerical values, and distinguish discrete data from continuous data with examples like class size and height, including plotting approaches.

Explore population versus samples and learn how data from a subset estimates the whole, using random and nonprobability sampling techniques.

Define population as the entire group and a sample as a subset; show why researchers use samples to estimate the population mean from data chunks.

Use sampling to analyze large populations when full data collection is impractical, inferring population metrics from a representative sample and speeding analysis while reducing data collection errors.

Explore probability and non-probability sampling, learn how to create samples from a population, and examine techniques like simple random, stratified, cluster, systematic, purposive, voluntary response, snowball, and convenience-based methods.

Explore cluster random sampling, dividing the population into clusters and randomly selecting some, ensuring clusters reflect the population and cover it, with less statistical certainty than simple random sampling.

Explore probability sampling techniques such as random, systematic, stratified, and cluster, and compare with non-probability methods like convenient, purposive, voluntary response, and snowball sampling.

Learn population sampling, the use of a representative sample, and how population variance, sample variance, and standard deviation (sigma) are calculated from mu (mean).

Explore descriptive analytics and statistics by detailing central tendency measures—mean, median, and mode—and dispersion measures like range, interquartile range, variance, standard deviation, and mean deviation.

Examine measures of central tendency, including mean, median, and mode, to identify the center of data distributions with descriptive statistics guidance and examples of when to use each.

Compute the mean, or average, as the sum of observations divided by their count. Use mean to impute missing values and identify central tendency for discrete and continuous data.

Identify the mode as the most frequent value in data, including unimodal, bimodal, and multimodal cases, and learn practical imputation strategies for categorical data.

Identify the range as the difference between the highest and lowest values, a simple dispersion measure. Use it to highlight extreme values and guide the quality control chart.

Calculate the interquartile range to measure the middle 50% of data, using Q3 minus Q1 to assess dispersion and detect outliers.

Learn how variance and standard deviation measure dispersion around the mean, and how standard deviation is square root of variance, with population (n) and sample (n-1) using sigma notation.

Explore mean deviation as the average of absolute deviations from the mean, and note the median minimizes this value, illustrated by a 0.88 deviation.

Explore the fundamentals of probability, its theories and formulas, and why we use it. Learn the additional rule, independent events, cumulative probability, conditional probability, and Bayes theorem in upcoming videos.

Learn probability as the measure of how likely an event is, using favorable outcomes over total outcomes with coin toss and die examples, and explore trials, events, and sample space.

Explore the addition rule of probability for mutually exclusive and non mutually exclusive events. Apply P(A or B) = P(A) + P(B) − P(A ∩ B) with dice examples.

Explore independent events in probability, where one event does not affect another, and apply P(A and B) = P(A)P(B) with examples like rolling a die and tossing a coin.

Explore cumulative probability, defined as the probability that a random variable falls within a given range, illustrated with two-coin and two-die examples.

Apply Bayes theorem to compare the probability a black ball came from bag x versus bag y, using conditional probabilities and two-bag scenarios.

Explore probability distributions, from uniform to normal, and learn how to describe outcomes, apply scaling techniques, and convert to standard normal.

Explore uniform distribution, including discrete and continuous cases, with dice examples. Learn that discrete probability is 1/n, area represents probability, and the mean is (a+b)/2 with std dev sqrt((b-a)^2/2).

Learn the binomial distribution in statistics, a probability model for independent trials with fixed success probability. Use the formula nCx p^x (1-p)^{n-x} on coin-toss examples.

Master Poisson distribution, a discrete probability model for number of events in a time period, using lambda as the average rate. Include a call center example with formula P(X=x)=(lambda^x e^{-lambda})/x!.

Explore the normal distribution, also known as the Gaussian or bell curve, and apply the empirical rule to interpret data within mu plus minus one, two, and three sigma.

Explore skewness and how it reveals data asymmetry, with right and left skew versus symmetric distributions. Learn logarithmic transformation, square root transformation, cube root transformation, and reciprocal to approach normality.

Kurtosis measures the thickness of data distribution and its deviation from normality, distinguishing mesokurtic and platykurtic shapes, handling skewness, and guiding transformations like log, square root, and cube root.

Explore normal and standard normal distributions and how z-scores measure deviation from the mean using mu and sigma. Learn feature scaling with standardization and normalization for machine learning readiness.

Calculate a z score using x, mu, and sigma to standardize a test score. Consult the z table to determine how the score ranks among peers.

Explore covariance and correlation, and examine how these two statistics relate to each other in this module.

Learn how covariance reveals the directional relationship between two variables, with positive or negative covariance, using the formula and a stock example to illustrate means and deviations.

Explore correlation as how variables move together linearly, measured by Pearson's coefficient from -1 to 1, with negative and zero correlation, differentiating it from covariance and using heatmaps and python.

Compare covariance and correlation, noting how they signal direction and the magnitude of relationships in data. Covariance is unbounded, while correlation lies between -1 and 1.

Explore hypothesis testing, a method using sample data to evaluate two competing population statements—null and alternate hypotheses—using the normal distribution and z-scores to assess significance.

Compare one-tailed and two-tailed tests in hypothesis testing, explain rejection regions on the sampling distribution, and illustrate null versus alternate hypotheses with practical examples.

Explore the p value and significance level, and how null and alternative hypotheses guide statistical testing. Illustrate with a coin-toss example how observed results affect rejection decisions.

Explore the main statistical tests—t-test, z-test, ANOVA, chi-square, and correlation—and map them to categorical and numerical data with visualizations like bar charts, box plots, and scatter plots.

Demonstrates performing t tests—one-sample and two-sample—using female and male ages to test hypotheses with p-values and 0.05 significance, via data analysis steps.

Apply chi square test to categorical data by comparing observed and expected counts via pivot table, and test the null hypothesis of no association between gender and location with p-value.

Explore the ANOVA (analysis of variance) parametric test to determine if means differ across three dose groups, covering sum of squares, df, F statistic, and p-values.

Explore how correlation and covariance reveal linear relationships between variables, and learn to compute and interpret correlation values using scatter plots and Excel data analysis.

Explore the data analytics and data science process from data collection to model building, highlighting data processing, cleaning, iterative EDA, and visualization that drive insights and decisions.

Explore exploratory data analysis (EDA) as a visual, data-driven approach to understand data characteristics, identify errors and anomalies, and build quick baseline models for business insights.

Learn how data cleaning turns raw data into high-quality data for analysis and modeling, covering missing values, imputation or deletion, feature scaling, outliers, and invalid data handling.

Master techniques to handle missing values during data analysis and data cleaning, including deleting rows or columns, imputing with mean, median, or mode, and exploring algorithmic and advanced imputation methods.

Explore practical techniques to handle missing values in a churn modeling dataset using pandas and numpy. Impute with mean, median, or mode, apply forward or backward filling, or drop rows.

Learn feature scaling, a data cleaning technique that uses standardization (z-score) for normally distributed data and normalization to 0–1 for data that is not normally distributed to prepare predictive models.

Explains standardization with z scores based on mu and sigma, centering data at zero and typically ranging from -3 to 3, using an income example.

Identify outliers with box plots, histograms, scatter plots, and normal distribution cues; decide to remove, keep, or replace them using quantile or interquartile range, and apply robust models.

Identify and handle invalid values across dates, formats, and logical ranges; convert types, correct encoding, and remove structurally invalid data for robust data cleaning.

Explore the types of analysis, focusing on univariate analysis that uses a single variable to understand data and its characteristics, mainly for categorical data, with summary statistics for numerical columns.

Explore bivariate analysis to measure relationships between two variables, using scatter plots for numerical pairs and charts for categorical combinations, including correlation and covariance illustrated with churn data.

Explore numerical analysis of data, from single-variable summaries (mean, median, 25th/75th percentiles, min, max) to multivariate correlations, using scatter plots, histograms, kernel density estimation, and heat maps with pandas.

Explore how derived metrics create new features from existing data using domain knowledge, binning, and encoding techniques to prepare categorical data for machine learning models.

Learn how feature binning converts continuous variables into categorical bins to reveal patterns, identify outliers, and handle missing values, using equal width and equal frequency methods.

Learn how to perform feature binning with age using pandas cut, defining bins and labels, handling missing values, and visualizing counts with a bar chart in a practical churn dataset.

Explore feature encoding and convert categorical variables to numerical formats to feed models in predictive analytics. Learn label encoding, one hot encoding, and dummy encoding, with examples and practical guidance.

Explore practical feature encoding techniques on a churn dataset, including missing-value handling, label encoding, one-hot and dummy encoding, target encoding, and hashing encoding.

Explore exploratory data analysis for telecom churn case study, from data cleaning and missing value imputation to univariate, bivariate, and multivariate analysis, leading to insights and predictive modeling.

Back up the data frame, convert total charges to numeric, and address missing values by dropping low-percentage nulls. Bin tenure for clearer insights and remove irrelevant columns like customer ID.

Perform univariate analysis on a cleaned telecom dataset using an automated loop to plot all features, revealing churn patterns by senior citizens, contract type, and payment method.

Explore numerical and bivariate analysis for churn prediction: convert churn to numeric, encode features with dummies, and analyze monthly charges, total charges, tenure, and their correlations.

Build an end-to-end EDA report for churn analysis, detailing business understanding, data understanding, and findings. Present graphs and insights from the churn study, including final thoughts on churn drivers.

Install MySQL by setting up MySQL Workbench and MySQL Installer, create a root username and password, and connect to the local database; learn about SQL, databases, and DBMS basics.

Compare file server and client server architectures to see how multi-user access, in-memory edits, file locking, and versioning influence data integrity in production databases.

Define constraints in SQL, including unique, not null, and primary key rules. Create and manage tables in MySQL Workbench, applying primary keys and foreign keys.

Explore table basics and data definition language (ddl) essentials, including create, alter, and drop table commands, primary keys and constraints, data types, and practical sql table examples.

Explore data query language (dql) fundamentals with select queries, filtering using where, like, and in, and learn to create tables, insert data, and apply aliases and counts for clear results.

Explore data manipulation language (DML) basics in SQL, learn to insert, update, delete, and select data, create and query tables, and practice with employee datasets.

Learn how SQL joins connect data across tables using a common column, covering inner, left, right, and full (outer) joins, self joins, and cross joins with practical examples.

Explore aggregation functions in SQL, including group by, count, minimum, maximum, and average, with practical examples on gender and contract distributions.

Learn how to manipulate SQL string data using concat, trim, substr, mid, upper and lower, and character length to produce readable text and well-structured names.

Explore SQL date and time functions, from date diff and date format to date add and sub date, with practical examples on a transaction details table.

Explore window functions in SQL that compute values across related rows using over and partition by, featuring row_number, rank, and first_value in sales and product examples.

Connect SQL databases with Python to extract data from MySQL, load into a Pandas data frame, and perform EDA in notebooks. Connect SQL databases from Power BI.

Explore the fundamentals of machine learning, its scope and use cases, then master core techniques—regression, classification, clustering, association rule learning, time series analysis—and key topics like feature engineering and deployment.

Machine learning is a subset of artificial intelligence that learns from data. Data science blends AI, ML, deep learning, statistics, and data exploration to study data.

Examine the four types of machine learning—supervised, unsupervised, reinforcement, and semi-supervised—along with labeled data, training and testing, and use cases like fraud detection, classification, and regression.

Explore key use cases of machine learning and deep learning across supervised, unsupervised, and reinforcement learning, with examples like recommendation systems, natural language processing, and facial detection.

Explore healthcare use cases powered by machine learning and deep learning, including imaging, disease detection, personalized treatment, fraud prevention, and drug discovery, applying classification, regression, and recommendation systems.

Identify features and their role as x variables in supervised learning, and distinguish y variables as the targets. Use churn examples and Vodafone data to illustrate predictors and model building.

Master train-test split concepts using the 80-20 split, separating training and testing data with X and Y variables, and evaluate models using accuracy for classification and error metrics for regression.

Master feature scaling as a data cleaning step that standardizes or normalizes features to a common scale for predictive modeling and sometimes EDA, using standardization (z-score) or normalization (0–1 range).

Learn standardization by converting data to z-scores using the mean and standard deviation, with Python and scikit-learn, illustrating mu, sigma, and a -3 to 3 z-score range for future normalization.

Learn regression as a supervised predictive technique that predicts a continuous target from input features, covering linear, multiple linear, lasso, ridge, and polynomial methods with real-world examples.

Explore regression metrics like MAE, MSE, RMSE, R2, and adjusted R2 to evaluate performance while covering data cleaning, train-test split, feature scaling, and model comparison.

Apply practical regression metrics such as MAE, MSE, RMSE, R2 score, and adjusted R2 to evaluate models on a train-test split using sklearn.

Learn simple linear regression to model how area relates to price, fit the best line, and predict prices using y = mx + c.

Extend simple regression to multiple features, forming a plane that predicts price using y equals beta zero plus beta one x1 plus beta two x2, and interpret the beta weights.

Implement linear regression in practice with simple and multiple models on the 50 startups dataset, using one-hot encoding, feature scaling, and train-test splits to predict profit.

Create a 3D two-feature dataset using make regression, split into train and test sets, fit a linear regression model, and evaluate with MAE, MSE, RMSE, and R2 score.

Learn polynomial regression as a form of linear regression, using polynomial features and degrees to capture non-linear relationships, while managing underfitting and overfitting.

Apply polynomial regression practically by building models with polynomial features and comparing degrees from two to ten. Split, train, and evaluate using R2 scores while visualizing results.

Understand the bias-variance trade-off through intuitive examples. Distinguish overfitting and underfitting and identify the sweet spot for model performance, with notes on regularization, bagging, and boosting.

Explore ridge regression as a regularization technique for linear regression, penalizing large slopes with lambda to reduce overfitting and improve generalization, alongside basic cost function updates.

Explore lasso regression and its L1 regularization, which combats overfitting and enables feature selection by adding a lambda times the absolute value of coefficients to the cost function, unlike ridge.

Explore practical ridge and lasso regression by building a linear model with the Boston housing dataset, comparing R2 scores and tuning alpha via manual and grid/randomized search, plus feature selection.

Explore how supervised learning uses labeled training data to predict category membership, with spam filtering and fraud detection as key examples.

Explore binary and multi-class classification in machine learning, with fraud detection and churn examples, and learn how models handle two versus multiple classes.

Explore log loss as a key classification metric, understand prediction probability, thresholds, and model evaluation for binary problems like fraud detection and spam filtering.

Explore area under the ROC curve (AUC ROC) as a key binary classifier metric, measuring sensitivity and specificity across thresholds.

Master k nearest neighbors, a supervised classifier using Euclidean distance to vote among k neighbors for churn or active. See binary and multi-class cases with age and salary.

Explore a knn classifier using age and gender to predict cricket or football preference, with dummy encoding and euclidean distance. Neighbor voting determines the predicted class for a new player.

Implement the first KNN classification model by preparing data, handling missing values and numeric conversion, applying one-hot encoding, performing train-test split, and standardizing features.

Learn to implement a kNN classifier with scikit-learn, perform data preparation, encoding, scaling, train-test split, and evaluate accuracy for churn prediction, plus new data and probability predictions.

Understand how a decision tree uses a root node, internal nodes, leaf nodes, and branches with features like age, gender, or smoker status to predict disease.

Explore entropy-based decision trees for classification, identify the root node using information gain, and implement the model in Python with an illustrative profit or loss example.

Explore the gini-based criterion for decision trees, compare it with entropy, and build a root node from age, competition, and type to predict profit.

Explore decision tree classifiers, compare Gini and entropy criteria, visualize root, internal, and leaf nodes with plot_tree or Graphviz, and tune max depth for churn data.

Learn how random forest, an ensemble classification method, builds multiple decision trees with random data and feature selections, and uses voting to predict churn more accurately than individual trees.

Discover how support vector machines classify two classes by maximizing the margin between parallel hyperplanes, identify support vectors, and extend to non-linear data by moving to higher dimensions.

Compare practical classifiers—KNN, decision trees, random forest, Naive Bayes, SVM, and logistic regression—using a churn dataset. Includes end-to-end workflow with EDA, encoding, scaling, and evaluation metrics like accuracy and recall.

Explore underfitting and overfitting in classification, compare training and testing performance, and examine imbalance issues through practical examples to improve model generalization.

Discover practical strategies for imbalanced data in classification, using oversampling and undersampling, smote variants, and threshold tuning to improve precision, recall, and F1 in real-world churn and fraud problems.

Bagging blends bootstrapping and aggregation to turn low-bias, high-variance models into a stronger predictor via majority voting, as seen in random forest and bagged decision trees.

Explore bagging ensembles by implementing bagging classifiers with decision trees on synthetic data, compare accuracy with random forests, and study bootstrap sampling and key parameters.

Explore AdaBoost and how boosting combines weak learners, especially decision stumps, into a strong learner. Learn how the method updates sample weights to emphasize wrong predictions.

Learn cross entropy as a loss function for classification, contrasted with cost functions, through a weather example and the distinction between binary and multi-class (categorical) cross entropy.

Discover XGBoost, the extreme gradient boosting algorithm, a powerful ensemble method with parallel processing, optimized data structures, cache awareness, and gpu support for fast, accurate predictions.

Learn k-means clustering, an unsupervised method that groups data by nearest centroids using euclidean distance, updates centers, and uses the elbow method with wcss to choose k.

Explore hierarchical clustering, a bottom-up agglomerative method that builds a dendrogram to reveal a hierarchy of clusters. Compare it with k-means and note pros and cons for large data.

Explore hierarchical clustering in practice by building dendrograms with SciPy, choosing cluster numbers, and comparing agglomerative clustering to k-means using annual income and spending score.

Explore the critical role of feature engineering and dimensionality reduction in preparing data, selecting features, and improving model performance using PCA, LDA, and related techniques.

Explore recursive feature elimination in practice, selecting five features from a 30-feature dataset, compare with a full-model baseline, and learn preprocessing steps like one-hot encoding and handling missing values.

Explore chi-square based feature selection, a statistical test for association between categorical features and the target, enabling significance testing, reduced dimensionality, improved interpretability, handling categorical data, and avoiding overfitting.

Learn chi square test based feature selection to identify the top five features related to the Y variable and compare logistic regression models with and without chi square selection.

Master principal component analysis to reduce high-dimensional data for feature engineering, visualization, and noise filtering; learn how to choose n components and retain 80% variance to combat overfitting.

Explore the practical application of principal component analysis to streamline features, assess explained variance, and upgrade a logistic regression model with PCA-transformed data.

Explore practical linear discriminant analysis alongside PCA, reuse PCA code for LDA, and compare performance, explained variance, and end-to-end modeling with kernel PCA and QDA.

Explore kernel PCA and quadratic discriminant analysis in a hands-on session, compare with PCA and LDA, and discuss when non-linear feature engineering improves or matches linear methods.

Boost model accuracy by tuning hyperparameters across classifiers and algorithms, using manual and automated methods like grid search, random search, and bayesian optimization guided by a validation set.

Explore manual hyperparameter optimization, and demonstrate hit-and-trial tuning for a decision tree, while contrasting manual hpo with grid search, random search, and bayesian approaches, highlighting drawbacks.

Practice session demonstrates building a breast cancer classification model with a random forest and manual hyperparameter optimization to improve accuracy, then compares grid and randomized search approaches.

Leverage randomized search CV to tune random forest hyperparameters, sampling 100 iterations from a random grid, using 3-fold cross-validation, and compare gains against a base model.

Explore grid search cross-validation and hyperparameter optimization for random forest, tuning bootstrap, max depth, max features, and min samples to achieve about 0.94% accuracy boost.

Explore time series analysis and forecasting, defining time series, identifying trends and seasonality, and predicting future values from past data using ARIMA, SARIMA, Prophet, and LSTM.

Explore the distinction between time series and regression, including when to use each with or without a date column, and learn key algorithms like ARIMA, SARIMA, LSTM, and Prophet.

Explore time series analysis as a specialized forecast on data with a date column at equal intervals, and learn its importance in statistics, plus upcoming anomaly detection (outlier detection).

Explore anomaly detection in time series, identifying data points that deviate from expected patterns as outliers, and discuss handling or removing anomalies with ARIMA, Prophet, or LSTMs.

Explore the four time series components: trend, seasonality, irregularity, and cyclic, and learn how decomposition and autocorrelation reveal them for accurate forecasting.

Explore time series decomposition with statsmodels, comparing additive and multiplicative models, and visualize trend, seasonality, and residuals using the air passengers dataset.

examine time series stationarity using plots, summary statistics, and augmented dickey-fuller test; interpret p-values to decide unit roots, noting ARIMA and exponential smoothing require stationarity while prophet variants may not.

Pre-process time series data by cleaning, handling missing values, removing duplicates, and addressing outliers to ensure accurate forecasting. Apply feature scaling, encoding, and feature engineering for robust analysis.

Handle missing values in data cleaning for EDA by deleting rows or columns with missing values, imputing with mean, median, or mode in Python, and comparing effects on model accuracy.

Master techniques to handle missing values in real time data. Apply mean, median, and mode imputation, and use forward or backward filling, or drop rows or columns on churn dataset.

identify and treat outliers using box plots, histograms, and scatter plots. decide when to remove or keep outliers in time series forecasting, predictive analytics, using quantile or interquartile range methods.

Learn how feature scaling standardizes and normalizes numerical features to improve predictive modeling and sometimes data exploration, using standardization and normalization.

Explore feature scaling in Python using sklearn, performing normalization with min max scalar and standard scalar on a churn dataset.

Explore practical feature encoding techniques for churn modeling data, including handling missing values, label encoding, and one-hot or dummy encoding using pandas and sklearn.

Explore time series forecasting algorithms like ARIMA, seasonal ARIMA variants, Prophet, LSTMs, Holt-Winters, GARCH, and VAR/ARIMA models, and understand autoregressive integrated moving average concepts.

Learn how Arima, the autoregressive integrated moving average model, analyzes time series with trend, seasonality, and autocorrelation, and how p, d, and q define autoregressive, differencing, and moving average components.

Explore the mathematics behind Arima, detailing autoregressive and moving average components, differencing effects, how past values drive forecasts, and how pacf/pcf guide choosing p and q.

Identify optimal p, d, q values for ARIMA models using grid search, comparing manual ACF/PACF plotting with Auto.arima automation and rmse evaluation in R and Python.

This lecture covers why stationarity matters for time series forecasting with ARIMA, shows the augmented Dickey-Fuller test, p-value interpretation, and using transformations to achieve stationarity.

Apply transformations such as log, double log, and differencing, plus moving average and exponential weighted moving average, to achieve stationarity for ARIMA modeling, and perform inverse transformations for final forecasts.

Explain multiplicative and additive decomposition of time series using Statsmodels seasonal_decompose. Reveal trend, seasonality, and residuals from log data, show plots, and discuss acf, pacf, and inverse transformations for forecasting.

Learn how to interpret ACF and PACF plots on log-transformed time series to estimate AR and MA orders (p and q), and why grid search matters for production.

Explore end-to-end time series transformations and their inverses, including log, double log, and log differencing, using exponentiation and cumsum to recover original data.

Learn to run a grid search for arima time series, testing p, d, and q values, and use rmse to identify the best pdq for forecasting with practical code workflow.

Grid search evaluates multiple p and q combinations, revealing an optimum pdq of 1, 0, and 1, while highlighting its time and resource costs.

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

Apply a Facebook Prophet model to air passenger data, fit on transformed series, and forecast future values. Interpret y_hat, y_hat_lower, and y_hat_upper with trend and seasonality insights and offline plots.

Explore holiday effects in Facebook Prophet and how to incorporate holidays via a data frame or built-in methods, enhancing forecasts with seasonality and multivariate considerations.

Explore univariate and multivariate time series forecasting with Facebook Prophet, applying log transformations, inverse transforms, and added regressors like date, humidity, wind speed, and mean pressure to predict mean temperature.

Evaluate forecasting performance by training on the training data, predicting on the testing data, and comparing results to select the best model using MAE, MSE, RMSE, MAPE, and R2.

Explore mean absolute error, an evaluation metric that averages the absolute differences between actual values and predictions, illustrated with a simple example.

Understand root mean squared error, the square root of MSE, as a regression metric using sqrt((1/n) * sum (y - y_hat)^2) to evaluate and compare models.

Explore the mean absolute percentage error (MAPE) as a forecasting and regression evaluation metric, learn its formula, and compare models by the lowest MAPE.

Explore energy demand forecasting with ARIMA, using time series data to predict electricity demand and help utilities optimize power generation and distribution.

Load and visualize energy data with pandas, convert timestamps to datetime, and plot load with solar generation to reveal trend and seasonality; forecast arima and address missing values with ffill.

Assess stationarity using ADF tests and p-values, perform ARIMA modeling with p=2, q=2, d=0, compare RMSE across models, and discuss grid search for hyperparameter tuning in energy demand forecasting.

Predict stock prices using univariate time series with Facebook Prophet, leveraging historical Tesla data and bayesian regression modeling to generate forecasts for trading decisions.

Transfer code to Google Colab, load the Tesla stock data, and build a Prophet forecast with daily, weekly, and yearly seasonality for 365 days, including component plots.

Forecast Tesla stock prices with Prophet, analyze closing price and volume, explore trend, volatility, and seasonality, and apply moving averages.

Explore demand forecasting for e-commerce data by comparing ARIMA, Holt's Winter, and Facebook Prophet, building and evaluating models with training-test splits.

Demonstrate a practical demand forecasting workflow in Google Colab, including data upload, median imputation, weekly aggregation of unit sold, and exploratory analysis of base price and total price.

Learn to detect seasonality in weekly demand data with autocorrelation plots in pandas, interpreting the sinusoidal pattern and seasonal decomposition to guide model building.

Train Holt-Winters, ARIMA, and Prophet on a cleaned weekly time series, split data into training and test, and evaluate forecasts with RMSE to identify the best algorithm.

Compare arima, Holt-Winters, and Prophet for Facebook profit forecasting on a messy dataset; evaluate plots and rmse, prefer Prophet with potential regressors and interactive Plotly visuals.

Explore how machine learning analyzes patient data and lifestyle factors to classify cancer risk as high, medium, or low. Build algorithms and deploy Streamlit or Flask app to automate prediction.

Examine a generic multi-class classification architecture for predicting three risk levels with an imbalanced dataset. Compare models like tree, random forest, and XGBoost, with feature scaling, PCA/LDA, and hyperparameter optimization.

Understand a Kaggle healthcare dataset to identify independent features and a risk level target, explore cancer types and patient attributes, and plan to train, evaluate, and deploy models.

Perform exploratory data analysis on a cancer risk dataset, examining features from patient id and cancer type to demographics and lifestyle factors, and noting the imbalanced risk level distribution.

Create a seaborn bar plot of gender cancer counts by cancer type and patient count, grouped by gender; breast cancer dominates females, prostate in males.

Build a baseline XGBoost model with Smart inside a leakage-free pipeline, using label encoding, imblearn pipeline, and evaluation with log loss to compare results.

Explore and compare baseline XGBoost, a class-weighted XGBoost, and Optiona-tuned class-weighted XGBoost, with random forest as a baseline, finally identifying the Optiona-tuned model as the winner.

Save the tuned, class-weighted XGBoost model as a pkl file with joblib, then prepare it for deployment in a Streamlit or Flask app.

Convert an Optuna-tuned XGBoost cancer risk prediction model into a Streamlit app, enabling batch CSV predictions and manual input with preprocessing and deployment options.

Deploy a Streamlit cancer risk level detection app to AWS EC2, exploring deployment options across Azure, GCP, and on‑premises, and evaluating databases from Oracle SQL to NoSQL and vector databases.

Test the Streamlit app locally by running app.py with Anaconda, then prepare for aws deployment using the final xgb class weighted pickle and a sample prediction.

Master the data analytics lifecycle from business understanding through data understanding, collection, preparation, and exploratory data analysis, guiding BI or AI deployment paths.

Analyze telco churn using four customer segments, key loyalty drivers and churn triggers, and apply a data science led bronze-to-gold pipeline to predict churn and drive proactive retention campaigns.

Examine data types for telecom churn analysis, including customer demographics, usage, reload data, and call center records. Identify must-have (yellow) versus good-to-have data to build a churn model.

Explore a telecom churn dataset with features like tenure, monthly charges, payment method, and contract type; analyze churn distribution, identify high churners, and outline eda and predictive modeling directions.

Explore customer churn through an end-to-end eda: load the dataset, inspect shape and data types, identify total charges as a numeric issue, and visualize churn distribution to surface insights.

Clean the data by converting invalid types to numeric, handling nulls, backing up data, dropping few missing values, and binning tenure; remove noninformative columns to prepare for analysis.

Perform bivariate analysis to compare churners and non-churners by partner status, gender, and payment method, and summarize insights in an end-to-end EDA report for data storytelling.

Build a telecom churn prediction model by data prep, stratified 80-20 split, scaling, feature engineering, and multiple model trials with hyperparameter tuning, then deploy via Flask or Streamlit on AWS.

Build a base model for customer churn using a train-test split, a decision tree classifier, and one-hot encoding, then address imbalanced data and data type issues like total charges.

Clean telco data, convert total charges to numeric, scale features, bin tenure, and encode variables to train and evaluate model, while balancing data with upsampling and smart ENN or Adacene.

Explore class-imbalance handling for XGBoost, comparing smart upsampling, Adacene, and Exibust, and apply hyperparameter optimization to boost recall and F1 toward a 0.7 target.

Explains hyperparameter optimization for models like AdaBoost and XGBoost, comparing randomized search and grid search, highlighting time costs, and the role of parameter grids and tuning methods.

Save the best model to a PKL file using Joblib or pickle to back up before Google Colab session timeouts, and prepare for deployment with Flask and Streamlit.

Tune hyperparameters for churn prediction using Adaboost, LightGBM, and Cadboost, guided by Optuna Bayesian optimization. Save, compare, and deploy the best models with Streamlit or Flask.

Identify Adaboost as the top performer on F1 and recall, and plan hyperparameter optimization and saving as a PKL file for the next Streamlit app.

Learn how to turn standalone Python notebooks into deployable apps with Flask, build a front end, expose models via REST APIs, and map end-to-end deployment workflows.

Create a basic Flask web app by installing Flask, creating app.py, defining routes, returning text responses, and running with optional debug mode and port configuration.

Transform a notebook-based breast cancer model into a Flask web app with a simple html front end, handling post submissions, rendering templates, and frontend-backend integration.

Deploy your flask application on AWS EC2 to expose it publicly instead of localhost. Launch an EC2 instance, select an AMI, configure security groups, upload and deploy your code, verify.