Data Analytics, Data Science, ML, DL & NLP - All in 1 Course

Explore expressions and variables in Python, including operands, operators, and how Python evaluates expressions to results, and learn assignment to store and reuse values with examples like five plus three.

Practice expressions and variables in Python to calculate total eggs bought, total eggs used, and remaining eggs across three days using addition, subtraction, and print statements.

Explore the definition, purpose, and features of Python data types: integers, floats, strings, and booleans. Learn typecasting with int, float, str, and bool to convert between data types.

Practice Python data type conversion through a hands-on lab that converts string to integer, float to string, and boolean to integer, while verifying types with the type function.

Master Python string handling by exploring quotes, word strings, indexing from zero and negative indexing, slicing, len, concatenation, escape sequences, and common methods like upper, lower, and replace.

Explore hands-on practice with various string operators in Python, including indexing, slicing, negative indexing, substrings, step-based slicing, length, concatenation, escape sequences, case conversion, and replacement.

Explore Python data structures by learning tuples, their immutability and order, along with list operations, slicing, concatenation, and nested structures such as lists of lists.

Explore hands-on Python techniques for tuples and lists, including nested tuple and nested list slicing, negative indexing, and stepwise extraction, along with append and remove operations.

Explore how Python sets ensure unique elements and remove duplicates, are unordered and mutable, and how to convert lists to sets, add and update items, and perform union and intersection.

Explore working with sets in Python via a hands-on lab in Jupyter Notebook, converting lists to sets, adding and removing elements, and performing union and intersection.

Learn how Python dictionaries store data as unique and immutable key–value pairs, access values by keys, and add or remove entries with keys, values, and items.

Explore working with dictionaries in Python via a hands-on lab in a Jupyter notebook. Practice slicing and extracting values, adding keys, deleting entries, and listing keys, values, and items.

Explore how conditions and branching control Python programs with if, else, and elif. Master boolean logic, comparison operators, and the or and not operators, with examples such as voting eligibility.

Apply Python conditionals and branching to solve real problems by classifying animals by speed, evaluating discounted ticket eligibility, and determining eco friendly car race eligibility with nested if statements.

Explore loops for iteration in Python, including for loops, while loops, range, and enumerate, to repeat tasks over lists, strings, and tuples.

Explore hands-on loop concepts with for and while loops, enumerating tasks, printing welcome messages, generating even numbers, and classifying numbers as positive, negative, or zero.

Develop and use functions with def, arguments, and return, and distinguish between return and print. Explore global and local variables, and examples like temperature conversion and circle area.

Develop Python functions to calculate compound interest, EMI, and BMI, applying the defined principles and formulas to real-world savings, loan payments, and health assessments.

Learn how Python uses a class as a blueprint to create objects with attributes and methods, using a car example to show brand and wheels, and start_engine.

Develop an object-oriented library system in Python by building a book class with title, author, and availability, and adding borrow, return, and check availability methods.

Explore how application programming interfaces enable communication between apps and servers, use Rest principles, and perform create, read, update, delete operations via http methods, endpoints, and api keys.

Learn HTML fundamentals and how to apply BeautifulSoup for web scraping, including parsing and navigating HTML, using requests to fetch pages, and extracting links with find_all and href.

Learn web scraping with BeautifulSoup and pandas to collect data, parse HTML, extract headings, paragraphs, links, and tables, convert to a dataframe, and export to Excel.

Explore how the Python open function reads and writes text files, using r, w, and a modes, read with read, readline, and for loops, then close the file.

Practice using the Python open function to read and write text files. Learn to read full content and the first line, create samsung.txt, and append to apple incorporation.txt.

Learn to read and write data with the pandas library in Python, using read_csv and read_excel, manipulate dataframes, and preview datasets with head and tail.

Use pandas to read the Excel file, load February sheet into a dataframe, print the first ten rows, convert a dict to a dataframe, and export as CSV without indices.

Learn to read and write JSON and XML data using Python, including json.load and json.loads for files and strings, and xml.etree.ElementTree for parsing and accessing tags.

Learn to read JSON and XML files in Python by loading JSON data and printing student names and grades, then parsing XML to print employee names and positions.

Apply Python's try, except, else to handle runtime errors such as syntax, value, zero division, and file not found, improving robustness for user input, file I/O, and API use.

Practice exception handling in python with two problems: handle data dot txt not found via try and accept, then manage zero division and value errors on user input.

Learn to set up a Jupyter notebook for data analysis by installing and importing pandas and numpy, loading data with read_csv or read_excel, and viewing the first five rows.

Learn to derive column data types with Python using dtypes, fix misclassified fields such as object lifespans and dates, and assign correct int, float, and datetime types.

Perform exploratory data analysis in Python by using value_counts to compute frequencies of categorical variables, then convert them to percentages to compare channels like online and in-store.

Explore cross-tabulation to turn raw categorical data into a clear count-based summary and reveal how two categories relate; use the cross-table to compare counts across region and product type.

Explore five fundamental continuous distributions—normal, exponential, Student's t-distribution, chi-square, and gamma—and learn when to use each for data shape, waiting times, small samples, hypothesis testing, and waiting for multiple events.

Define the population and the sample, explain why sampling matters, and outline random, representative, adequately sized methods like simple random, stratified, systematic, and cluster sampling.

Explore the types of statistical data, including qualitative and quantitative forms—nominal, ordinal, discrete, and continuous—and their use in descriptive, inferential, predictive, and trend analyses with Python.

Explore levels of measurement, or scales, defining how data can be categorized, ordered, or measured, including nominal, ordinal, interval, and ratio, guiding appropriate analyses.

Explore how data science blends statistics, computer science, and machine learning to derive insights, using data cleaning, analysis, visualization, and predictive analytics across industries.

Explore data science fundamentals: linear algebra, probability, statistics, and calculus. Learn Python or R and key libraries—numpy, pandas, matplotlib, seaborn, scikit-learn—for data manipulation, visualization, and supervised or unsupervised learning.

Explore the path to becoming a data scientist by mastering linear algebra, probability, statistics, data manipulation, visualization, and machine learning with Python or R.

Explore the data analysis process: cleaning, transforming, and uncovering patterns to inform decisions. Master descriptive, diagnostic, predictive, and prescriptive analysis using Excel, SPSS, R, Python, Tableau, Power BI, and SQL.

Explore business intelligence as tools and practices that gather, integrate, analyze, and present data via dashboards and KPIs to guide data-driven decisions.

Explore how statistical modeling uses mathematical relationships to analyze and predict outcomes from data, employing techniques like linear regression and time series to support data-driven business decisions.

Explore machine learning as a data-driven subset of artificial intelligence, covering supervised, unsupervised, and reinforcement learning, with data collection, preprocessing, training, evaluation, and deployment for scalable predictions and personalization.

Dive into deep learning, a multi-layer neural network approach that automatically extracts features from raw data to handle complex tasks like image recognition, speech recognition, and autonomous driving.

Explore artificial intelligence, its differences from machine learning and deep learning, and how AI powers supply chain optimization, personalized recommendations, chatbots, voice assistants, and autonomous vehicles.

Compare traditional data and big data, highlighting structured data in relational databases and five V's—volume, velocity, variety, veracity, value—and introduce sources and tools like Hadoop, Spark, NoSQL, and data lakes.

Explore big data concepts, including distributed storage with Hadoop, data lakes, and scalable cloud storage, and learn batch, real-time, and stream processing, data mining, predictive analytics, ETL, and data governance.

Explore database management tools such as Apache Hadoop, Microsoft SQL Server, Oracle, and MongoDB for handling big data, unstructured data, and distributed processing with scalable, fault-tolerant analytics.

Explore Python’s simple syntax, its NumPy and pandas data analysis libraries, and its scikit-learn, TensorFlow, and PyTorch machine learning capabilities, alongside R’s statistics and ggplot2 visualizations for data science.

Explore 360 data analytics tools, from Excel to IBM SPSS, for data analysis and visualization, with insights on when to use each and their limitations.

Compare Power BI and Tableau as data visualization tools, highlighting Microsoft integration, live data dashboards, real-time analytics, cost considerations, and suitability for small to large enterprises.

Explore Jupyter notebook as an open source, interactive platform for live code and visualizations, and note its language flexibility (Python, R, Julia) and cloud options (Azure, Google Colab) for collaboration.

Identify the business problem as the first step in data science to guide data collection, methods, and success metrics. Apply the five whys to uncover root causes.

Collect relevant data through online survey tools (Google Forms, SurveyMonkey) and offline methods to inform decision making, ensuring data accuracy, reliability, and validity with appropriate sampling, time, and cost.

Prepare raw data by cleaning, manipulating, and transforming it for analysis, ensuring accuracy, consistency, and readiness for machine learning.

Build a structured data model that maps relationships and constraints for reliable analysis. Emphasize data quality, feature relevance, and the choice between statistical and machine learning approaches.

Evaluate your model on unseen data with regression and classification metrics: MAE, MSE, RMSE, R-squared, precision, recall, F1, AUC, ROC; identify overfitting or underfitting, then tune hyperparameters for deployment.

Deploy your trained model to a real-world environment, delivering real-time predictions via cloud, edge, or container-based deployments while ensuring security, latency, and ongoing maintenance.

Explore primary, secondary, and tertiary data sources, weighing advantages and challenges to guide reliable data collection for research and analytics.

Discover the basics of statistical data analysis, from cleaning and transforming data to modeling, using tools like R, Python, and SPSS for descriptive statistics, regression, and multivariate analysis.

Explore descriptive and inferential statistics, summarize data with metrics and charts, and infer population trends from samples using tests like the t test.

Explore inferential statistics methods such as one sample t test, independent and paired t tests, and one way ANOVA to assess mean differences in populations and groups.

Explore chi-square tests for independence to assess associations between categorical variables via observed and expected frequencies, and apply Pearson correlation to quantify linear relationships between numeric or ordinal variables.

Learn how linear regression links a dependent variable to one or more independent variables, using simple and multiple models, the regression equation, R square, and beta values to predict outcomes.

Explore how hypothesis testing guides decision making in inferential statistics by evaluating sample data against null and alternative hypotheses. Apply test statistics and p-values to determine conclusions and quantify evidence.

Learn to select the appropriate statistical test for a scenario and hypothesis, from t tests to ANOVA, chi square, correlation, and regression, and check normality, linearity, and homoscedasticity.

Explore confidence level, significance level, and p value in hypothesis testing, and learn how these measures guide decisions and conclusions.

Make informed decisions and conclusions by comparing a calculated p value to a 5% significance level, deciding between null and alternative hypotheses, and stating the conclusion.

Compare two independent classes to test a new teaching method with a step-by-step hypothesis testing workflow, including H0, H1, alpha 0.05, Shapiro-Wilk, and t test.

Transform data to improve quality and model readiness by normalizing scales, mitigating skew with log or Box-Cox transforms, handling outliers, creating features, reducing dimensionality, and encoding categorical variables.

Master data transformation techniques to reduce skewness and improve model performance, including logarithmic and Box-Cox transformations, binding, and one-hot encoding.

Explore creating new features from revenue and cost to form profit, extract day, month, and year from dates, and apply standardization, normalization, and PCA to prepare data for analysis.

Explore how machine learning from data analysis informs decision making across industries. Learn about predictive analytics, real time analysis, and customer personalization for business impact.

Explore supervised and unsupervised machine learning in data analytics, including classification, regression, logistic regression, decision trees, and random forests, with practical examples.

Define the core problem with inputs and outputs, then collect, clean, and engineer features. Choose a model, train and evaluate with accuracy, MSE, and MAPE, deploy, and monitor.

Explore how machine learning learns from training data to predict and decide, using supervised, unsupervised, and reinforcement learning, with real-world applications like spam filtering, recommendations, and fraud detection.

Explore supervised regression models, including linear regression, svr, random forest regressor, ridge regression, and polynomial regression, highlighting robustness, least squares minimization, and when to apply each.

Explore supervised classification models such as logistic regression, SVM, random forest, Naive Bayes, and KNN, and learn how they assign class labels from probabilities using thresholds like 0.5.

Explore unsupervised clustering models such as k-means and DBSCAN to discover natural data groupings without predefined labels, evaluate with the elbow method, and handle outliers.

Assess model performance using classification and regression metrics, including accuracy, precision, RMSE, ROC AUC, and confusion matrices, to compare models on unseen data.

Identify how overfitting and underfitting hinder generalization, and apply fixes like regularization, early stopping, more data, and adjusting model complexity to balance bias and variance.

Explore imbalanced data, where the majority class dominates, and learn F1 and ROC AUC metrics plus SMOTE and weighted loss functions.

Explore matrices as grids of numbers with rows and columns, cover operations like addition, subtraction, scalar and matrix multiplication, and review forms such as square, diagonal, zero, and identity matrices.

Explore scalars and vectors, where scalars have magnitude only and vectors have magnitude with direction, represented as arrows, with vector addition and scalar multiplication.

Explore linear algebra foundations with vectors, matrices, and linear transformations, and learn how these tools solve systems and power data science and machine learning through eigenvalues and eigenvectors.

Explore tensors in linear algebra, from scalars, vectors, and matrices to multi-dimensional data like color images, and learn how order, shape, and axes enable deep learning and complex simulations.

Explore the transpose of a matrix by flipping rows into columns, note dimension changes, and apply (A+B)^T = A^T + B^T and (AB)^T = B^T A^T.

Explore the dot product as matrix multiplication, multiplying rows by columns and summing results. Learn dimensional rules, key properties, and practical takeaways for matrix operations.

Explore linear regression, a supervised model, fitting a best line by minimizing the residual sum of squares to predict the target Y from input features X, using slope and intercept.

Explore how logistic regression performs classification by mapping features to probabilities with the sigmoid function, applying a 0.5 threshold to assign classes, and training with cross-entropy loss via gradient-based optimization.

Master k-fold cross validation, a resampling method that trains on k-1 folds and validates on the remaining fold to assess model performance on unseen data.

Explore L1 and L2 regularization to prevent overfitting, compare feature selection and coefficient shrinkage, and learn how lambda controls penalties in regression and neural models.

Explore oversampling methods to address class imbalance in supervised learning by increasing minority class examples. Study random oversampling, SMOTE, and ADASYN, and prevent overfitting.

Balance imbalanced datasets by undersampling the majority class to match the minority, boosting model fairness and recall with methods like random undersampling, Tomek links, and near-miss.

K-means clustering uses centroids and Euclidean distance to minimize intra-cluster variance and maximize inter-cluster separation, with elbow method guidance for choosing k.

Explore how decision tree regression predicts continuous outcomes by greedily splitting feature space into rectangular regions and assigning a mean value per leaf, using MSE as the impurity measure.

Explore how decision tree classification builds a feature-based decision tree to assign data to classes. Learn how splits use gini impurity or information gain and how depth controls overfitting.

Explore how random forest regression uses an ensemble of decision trees, bootstrap sampling, and random feature selection to predict continuous targets with improved accuracy and reduced overfitting.

Explore how random forest classification builds an ensemble of decision trees trained on bootstrap samples with random feature selection, then uses majority voting to boost accuracy and generalization.

Explore how AdaBoost builds a strong classifier by sequentially training weak learners, weighting misclassified samples, and using a weighted vote to boost accuracy, highlighting its adaptiveness and sensitivity to noise.

Explore how traditional gradient boosting builds an additive ensemble of shallow decision trees, using gradient descent to minimize loss and correct residuals with each new tree.

Explore how CatBoost uses ordered target statistics to encode categorical features and build symmetric trees with gradient boosting for fast, accurate, and robust overfitting control.

Explore how Lightgbm, a fast, memory-efficient gradient boosting framework using leaf wise tree growth, optimizes classification, regression, and ranking on large datasets with gradient-based sampling, histograms, and cross-entropy loss.

Explore how XGBoost, a fast, scalable gradient boosting library, optimizes classification, regression, and ranking through parallelized trees, regularization, sparsity handling, and custom loss functions.

Explore hyperparameter tuning, including grid search, random search, and Bayesian optimization, to select learning rate, batch size, regularization, and other settings that prevent underfitting and overfitting while enabling cross-validation.

Deep learning uses multilayer neural networks to automatically learn hierarchical features from large data, with architectures like CNNs, RNNs, Transformers, and autoencoders, trained via forward propagation, backpropagation, and gradient descent.

Discover how neural networks transform inputs into outputs through weighted sums with biases and non-linear activations, learned by forward and backward propagation, with CNNs, RNNs, and transformers.

Explore TensorFlow's open source framework that uses data flow graphs and tensors to build, train, and deploy scalable machine learning models across devices, with a rich ecosystem and Keras integration.

Explore how TensorFlow 2.0 works, emphasizing eager execution by default, the unified tf.keras API, and streamlined deployment with TensorFlow Lite, TensorFlow Serving, and TFX.

Explore initialization in deep learning, setting starting weights and biases to guide training, prevent vanishing or exploding gradients, break symmetry, and speed convergence with Xavier/Glorot methods in TensorFlow models.

Glorot initialization, also called Xavier initialization, sets initial weights to keep activations and gradients balanced across layers, preventing vanishing or exploding gradients and favoring tanh or sigmoid activations.

Explore how stochastic gradient descent trains deep learning models by updating weights after each example using the loss gradient, offering fast, memory-efficient optimization that can help escape local minima.

Explore the history, definition, and workflow of artificial intelligence, from early computing to deep learning, neural networks, and current AI applications like natural language processing and computer vision.

Explore the three AI types by strength—weak (narrow), strong (generalized), and super (conscious)—and see how ethics, psychology, and computer science shape AI across fields.

Explore how human intelligence, artificial intelligence, and augmented intelligence collaborate to enhance decision making and a safer commute, highlighting the synergy between machine data processing and human insight.

Explore generative AI and its diverse use cases across sectors, powered by deep learning and large language models to generate text, images, music, and video.

Traditional AI relies on an organization's repository, analytics platform, and application layer with a feedback loop to refine predictions; generative AI uses vast external data and prompting to tailor models.

AI use cases in life appear in voice assistants, smart home devices, and personalized recommendations. The lecture also covers security, health monitoring with wearables, camera enhancements, and real time navigation.

Explore ai chatbots and smart assistants that use natural language processing, dialogue management, deep learning, and machine learning to interpret input, detect intent, and deliver personalized responses.

Explore generative AI tools and applications across diverse fields. Learn how industry leaders integrate this technology and leverage multimodal llms that handle text, images, audio, and video.

Explore generative ai models and their types, including variational autoencoders, generative adversarial networks, autoregressive models, and transformers, and see how they generate text, art, music, and videos.

Explore natural language processing, speech technology, and computer vision to understand applications in industries and how NLP analyzes language, CT converts speech to text, and TTS generates speech for interaction.

Learn how AI, cloud, edge computing, and IoT create intelligent, real-time applications by processing data from devices like fitness trackers and smart thermostats.

Discover tools for text generation powered by generative AI. Learn how large language models like GPT and Palm enable coherent, context-aware text and multimodal capabilities across chat and research tasks.

Discover the core capabilities of generative AI for image creation, including image to image translation, inpainting, outpainting, and style transfer, with tools like Dall-E, Stable Diffusion, and Midjourney.

Explore how generative AI powers code generation, completion, optimization, language translation, and documentation through tools like GPT, ChatGPT, Copilot, Polly Coder, IBM Watson, and more.

Explore generative AI tools for audio and video, including speech generation, music creation, and audio enhancement. Use text-to-speech, video tools, and avatar creation to boost accessibility and visuals.

Define a prompt and its core components, and show how enriched prompts with context, input data, and output indicators guide generative AI models to precise, desirable outputs.

Explore prompt engineering as the art of crafting precise prompts and system prompts to guide generative AI, defining goals, context, and expectations through an iterative process that yields accurate outputs.

learn best practices for crafting prompts using four dimensions—clarity, context, precision, and role play—to unlock generative ai potential and control style, tone, and relevance.

Explain the interview pattern approach to prompt engineering and apply it to craft prompts that guide generative AI with detailed, tailored responses through structured follow-up questions.

Learn the chain-of-thought prompt technique to guide AI reasoning through step-by-step prompts, using related questions and solved examples to improve accuracy.

Master the tree of thought prompting to structure prompts hierarchically for multiple reasoning paths, guiding prompt engineering toward tailored, contextually accurate ai outputs.

Explore inferential statistics that generalize from sample to population, test hypotheses, estimate unknown values, and forecast future trends using sampling distributions, standard errors, and confidence intervals.

explore the central limit theorem by showing how sample means from any population form a normal distribution as sample size grows, enabling confidence intervals and hypothesis testing.

Explore the standard error: a measure of uncertainty in sample means that informs confidence intervals and hypothesis tests, with se = sigma / sqrt(n) and larger samples reducing error.

Identify estimators as formulas applied to sample data to guess population parameters, and estimates as the resulting values, noting they should be unbiased, efficient, and consistent, with confidence intervals.

Explore how a confidence interval uses a point estimate, standard error, and z-score with a margin of error to bound the true population parameter from sample data.

Compare z scores and t scores to standardize data, using known population deviation for large samples and unknown deviation with small samples, enabling hypothesis testing and confidence interval construction.

Explore how margin of error bounds a sample statistic and how z-score or t-score, standard error, sample size, and variability shape the interpretation.

Explore how to test population claims using null and alternative hypotheses, one-tailed and two-tailed tests, and p value to decide whether to reject or retain H0.

Learn about type one and type two errors in hypothesis testing, including alpha and beta, false positives and false negatives, and how sample size and power analysis reduce errors.

Translate real questions into precise, testable hypotheses by defining the parameter, writing the null and alternative hypotheses, and selecting one-tailed or two-tailed tests for means, proportions, or differences.

Learn to identify the testing goal and data structure to select the right hypothesis test—t tests, chi-square, ANOVA, correlation, and regression—considering one- or two-tailed options and sample size.

Learn to perform assumption tests for parametric analysis, including normality, homogeneity of variance, independence, and linearity. When assumptions fail, choose nonparametric alternatives and visualize data with histograms and scatter plots.

Learn how to select the significance level, or alpha, in hypothesis testing, balancing type I error, sample size, and stakes, with 0.1, 0.05, and 0.01 as examples.

Make a decision and conclude after a hypothesis test by defining the null and alternative hypotheses, choosing alpha, and using the p value to reject or fail to reject.

Learn how kernel density estimate (KDE) plots visualize distribution shapes, compare to histograms, and reveal normality, skewness, or multimodal patterns in data.

Apply the Shapiro Wilk test to assess normality of data, interpret the W statistic and p value, and decide if parametric methods are appropriate.

Explore data transformation methods to stabilize variance and reduce skewness using square root, log, and Box-Cox, enabling positive data to meet parametric test assumptions.

Explore the independent sample t test to compare means of two independent groups, check assumptions of normality and equal variances, and interpret results via p-values.

Master one-way analysis of variance (ANOVA) to compare means of three or more independent groups. Apply hypotheses, p-values, and key assumptions, with a Python f_oneway example.

Explore the chi square test for independence, a non-parametric method for testing relationships between two categorical variables, with hypotheses and p-values, and compute expected counts using Python's chi2_contingency.

Explore Pearson's correlation, which quantifies the strength and direction of a linear relationship between two continuous variables using the correlation coefficient r and p-values.

Explore linear regression, predicting a dependent variable from one or more predictors, with intercept and slopes. Learn simple vs multiple models, key assumptions, residuals, and R squared for model fit.

Learn to generate new features from existing data to boost model performance, capturing non-linear patterns through transformations, ratios, aggregations, date decompositions, interactions, and categorical encodings like frequency and target encoding.

Extract date elements to unlock time-based patterns and boost model accuracy by decomposing dates into year, month, day, weekday, quarter, and hour for revealing seasonality and cycles.

Explore feature encoding as a crucial step that converts categorical variables into numeric form for machine learning, using label, one hot, ordinal, and binary encodings.

Explore feature binding, or discretization, by grouping continuous values into bins, including equal width, equal frequency, or custom binding, to simplify data, capture non-linear patterns, and improve robustness and interpretability.

Explore feature mapping to transform raw features into model-friendly representations, speeding learning, improving accuracy, and enabling complex relationships through value mapping, ordinal encoding, polynomial features, interaction mapping, and domain-based mappings.

Convert categorical features to numbers with dummy variables through one-hot encoding, enabling models to learn from presence or absence of categories while dropping a baseline to avoid multicollinearity.

Define the target variable and select relevant features using domain knowledge and statistical tests. Avoid leakage and multicollinearity for robust, accurate predictions.

Explore feature scaling with min max scalar and standard scaler to ensure fair feature contribution, accelerate gradient descent, and improve distance-based models like KNN and SVM.

PCA reduces dimensionality by transforming many variables into uncorrelated principal components that capture the most variance. It standardizes data, computes covariance, derives eigenvalues and eigenvectors, and selects top components.

Master the train test split to separate data into training and testing sets, apply split ratios and a random state, ensure generalization to unseen data, and guard against data leakage.

Apply Python to compute descriptive statistics for numeric variables using the describe method, obtaining mean, standard deviation, variance, min, max, and percentiles.

Learn to perform the Shapiro-Wilk test in Python by importing Shapiro from scipy.stats, assessing normality of numeric variables, and interpreting p-values at 0.05.

Apply the Shapiro-Wilk test to numeric columns to assess normality. Interpret p-values for age and average purchase amount, and note normality violations or acceptances.

Learn how to apply square root, log, and Box-Cox transformations in Python to normalize skewed data, assess normality with the Shapiro-Wilk test, and visualize results with KDE plots.

Apply square root, logarithmic, and box-cox transformations, assess normality with Shapiro-Wilk tests, and visualize results with KDE plots to identify the best method.

Conduct an independent sample t test in Python to compare average purchase amount between churned and existing customers, using 0.05 significance and SciPy's ttest_ind after filtering churn_status yes or no.

Conduct an independent sample t-test on average purchase amount to compare churned and existing customers using scipy's ttest_ind, interpret the p-value, and note the significant difference with higher churned averages.

Apply one way analysis of variance to compare the average frequency of purchases across cities, test hypotheses with a 0.05 level, and interpret the p value using Python's SciPy f_oneway.

Apply Shapiro-Wilk test to assess normality of frequency of purchases, then perform a one-way ANOVA across Chicago, New York, Houston, and Los Angeles, using Levene’s test to conclude no differences.

Apply the chi-square test for independence to assess the null and alternative hypotheses about region and purchase channel using a cross tab and a 0.05 significance level.

Perform a chi square test for independence using SciPy's chi2_contingency on a cross tab of region and purchase channel, then interpret the p-value to conclude no significant association.

Perform a Pearson correlation between purchase frequency and average purchase amount to test for a significant relationship at 5%, after verifying normality and linearity with a scatter plot.

Demonstrate a hands-on Pearson correlation to evaluate linearity between purchase frequency and average purchase amount, visualize with rake plot and scatter plots, and report the p value and positive relationship.

Apply Python linear regression to measure how frequency of purchases influences the average purchase amount, test hypotheses with a 5% significance level, and report the model summary.

Apply a linear regression with statsmodels to predict the average purchase amount from frequency of purchases, add a constant, and interpret the ols results.

Develop new features through feature engineering using domain knowledge, such as total purchase amount and customer lifetime value, implemented with Python to enrich customer data.

Create a new feature named customer_value from pre-processed data by multiplying frequency of purchases with average purchase amount, then compute customer lifetime value (clv) by lifespan in months, demonstrating execution.

Extract date elements from a DateTime variable to create features for predictive data modeling. Derive year, month, and day values and add them as columns with Python code.

Extract year, month, and day from the date of purchase using Python, ensure datetime64 dtype, handle dot accessor errors, and drop the original date of purchase column in preprocessed data.

Apply level encoding to convert ordinal categorical variables into numeric features using scikit-learn's level encoder, transforming churn status from yes/no to 1/0 for machine learning models.

Apply feature encoding to a binary categorical variable using sklearn's label encoder, performing fit_transform on churn status and verifying results in the pre-processed data.

Learn to convert a numeric variable into a categorical feature by binning with pandas pd.cut, creating a new bind column with defined bins and levels, and adjusting include lowest.

Create an engagement_level variable from customer lifespan in months using pd.cut for feature binning, with bins 0–2, 2–3, and 3–5 labeled low, moderately engaged, and highly engaged.

Create a dictionary to map each categorical value to a numeric code, load it as mapping, and apply map to encode an ordinal categorical column.

Encode engagement levels with a Python dot map by building a string-to-number mapping for low, moderately engaged, and highly engaged, then apply it to pre-processed data and view results.

Learn how to convert non-ordinal categorical variables into numeric features using pandas get_dummies, and merge the dummy columns into your dataset with pd.concat, for machine learning readiness.

Derive all column names from the preprocessed data and generate dummy variables for gender, city, region, and purchase channel using pandas get_dummies and view first five rows.

Develop a machine learning model by separating features and the target, loading features into x and the target into y, and removing redundant columns via drop and dummy variables.

Practice feature selection by dropping identifiers and redundant columns from processed data, then prepare regression and classification datasets with x and y targets for CLV and churn status.

Scale features with Python using standard scaler and min max scaler from sklearn.preprocessing, applying fit_transform to prepare features for a machine learning model on customer data.

Scale features for regression and classification models using standard scaler and min max scaler from sklearn.preprocessing, applying fit_transform to x_reg and x_class.

Apply PCA using sklearn to reduce feature dimensionality, compute explained variance ratio, and identify the optimal component count via a plotted variance line.

Explore a hands-on principal component analysis (PCA) with sklearn, computing explained variance ratios and plotting AVR against the number of components to identify a single component that explains all variance.

Learn to split data into train and test sets with train_test_split, define X and y, and set test_size and random_state for reproducible evaluation on scaled features.

Import train_test_split and create train and test sets for regression and classification models, then scale features with a min-max scaler and set test_size to 0.2 and random_state to 42.

Develop a machine learning linear regression model to predict customer lifetime value by combining statistics and computer vision, using scikit-learn to train, predict, and evaluate with mean squared error.

Import sklearn's linear regression and mean squared error, fit the model on train features and target, then predict test outcomes; evaluate with MSE and compare via a CDF plot.

Apply logistic regression to predict churn status from features, trained on x_train and y_train and tested on x_test, with accuracy, confusion matrix evaluation, and heatmap visualization.

apply logistic regression using sklearn to load, train, predict, and evaluate a classification model; compute accuracy and visualize a confusion matrix to interpret performance.

Apply k-fold cross-validation in Python using stratified k-fold and cross_val_score with logistic regression, set max_eta to 1000, n_splits to 5, and random_state 42 for sports data.

Apply k-fold cross validation to a logistic regression model on sports data using Python. Include data preprocessing with standard scaler, feature-target split, and stratified k-fold scoring to gauge accuracy.

Learn to apply L1 and L2 regularization to regression with Python using scikit-learn's lasso and ridge models. Configure alpha and max_iter, then fit, predict, and evaluate with MSE and RMSE.

Apply L1 and L2 regularization to a linear regression model, compare lasso and ridge using mean squared error (MSE), and perform hyperparameter tuning.

Learn to apply smote-based oversampling on imbalanced data in python using the EMB learn oversampling module, training on xtrain with random_state 42 and printing results with a counter function.

Apply smote oversampling to balance imbalanced data, inspect class distribution with a counter, and compare distributions before and after resampling.

Apply Tomek links undersampling to imbalanced data, then optionally combine with SMOTE using the imblearn motomagx tool to balance classes and inspect with a class counter.

Balance imbalanced data by applying Tomek links undersampling and a smote-tomek combination, using atomic links for balanced fraud detection.

Learn to perform k-means clustering in Python, using scikit-learn to segment customers and apply the elbow method with wcss to choose the optimal number of clusters.

Perform k-means clustering on customer data using recency, frequency, and monetary score; use the elbow method to choose k, then label clusters as regular and loyal for targeted segmentation.

Learn to use the decision tree regressor to predict customer lifetime value, training with fit, predicting with predict, and evaluating with mean squared error in scikit-learn Python.

Build a decision tree regressor with sklearn, train on x_rate_train and y_rate_train, predict on x_rate_test, and assess with mean squared error and plots comparing predicted to actual values.

Build a decision tree classifier to predict customer churn, train with xtrain and ytrain, and evaluate using accuracy and confusion matrix, then visualize results with a heat map.

Explore building a decision tree classification model using sklearn, train on training data, predict test data, and evaluate with accuracy score and confusion matrix, comparing results to logistic regression.

Apply the random forest regressor, an ensemble of decision trees, in Python with sklearn to train, predict, and evaluate customer lifetime value using mean squared error.

Train a random forest regression model with sklearn, fit on x_train and y_train, predict on x_test, and compute mean squared error, then compare with linear regression for customer lifetime value.

Learn to build a random forest classification model in Python to predict customer churn, import RandomForestClassifier from sklearn.ensemble, fit on features and target, and evaluate with accuracy and confusion matrix.

Import and train a random forest classifier from sklearn to predict customer churn, evaluate accuracy score and confusion matrix, and compare to logistic regression, achieving about 85% accuracy.

Learn to implement AdaBoost classification and regression in Python using scikit-learn, including model setup, training with 100 estimators and random state, and evaluation with classification report and RMSE for regression.

Learn to build AdaBoost classification and regression models in Python on Google Colab, including data loading, target encoding, feature scaling, and evaluation with the classification report and mean squared error.

Explore traditional gradient boosting with scikit-learn by building a classifier and regressor, tuning nestimators and learning rate, fitting on train data, and evaluating with a classification report and rmse.

Develop a traditional gbm classification and regression pipeline on healthcare data, performing preprocessing, train-test split, feature scaling, and evaluation with the classification report and rmse.

Explore Python code for CatBoost models, including classification and regression, with import, fit, predict, evaluation via classification report and rmse, and mindful use of verbose and random state.

Practically build and evaluate CatBoost classification and regression models in Python, including preprocessing, train-test split, feature scaling, model training, and performance metrics like MSE and RMSE.

Develop gbm classifier and regressor using Lightgbm, evaluate with the classification report and rmse, and prepare for hyperparameter tuning with XGBoost in upcoming lessons.

Develop and evaluate Lightgbm classification and regression models on healthcare data through end-to-end preprocessing, feature scaling, and train-test splits, using classification reports and RMSE for assessment.

Develop XGBoost classification and regression models in Python, train with parameters like n_estimators and learning rate, and evaluate using the classification report and rmse on healthcare data.

Develop an XGBoost classification and regression model on healthcare data, covering preprocessing, train-test split, scaling, and evaluation with classification reports and RMSE.

Leverage Python and Bayesian search CV to tune Xgbregressor hyperparameters, defining a search space for n_estimators, max_depth, learning_rate, subsample, and gamma, using RMSE as the optimization metric in 3-fold cross-validation.

Hyperparameter tuning of an xgbregressor for heart rate prediction on healthcare data using Bayesian search with scikit-optimize to minimize RMSE and MSE.

Apply TensorFlow deep learning to the mnist dataset, using 28 by 28 grayscale images (784 pixels) to predict digits 0 to 9 with a softmax probability vector.

Load and preprocess the mnist data in TensorFlow, including creating train and test splits, normalizing pixel values by 255, and reshaping images to 784-length vectors for model input.

Develop a TensorFlow deep learning model with tf.keras sequential layers, 784 input shape, dense units (128, 64, 10) with sigmoid activations, softmax output for MNIST classification, trained with SGD.

Evaluate a TensorFlow deep learning model on test data using test loss and test accuracy, confirming strong generalization with test accuracy around 93% and training accuracy around 92%, avoiding overfitting.