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 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 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 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.

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.

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 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.

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 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 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.

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.

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 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.

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 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.

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

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

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.

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 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.

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

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 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.

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 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.

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

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

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.

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 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 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 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.

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 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.

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.

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 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 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.

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.

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.

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 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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.