Python for Absolute Beginners Learn Programming from scratch

Explore Python sets and operations like union, intersection, difference, and subset relations, and relate them to join concepts such as left, right, outer, and inner joins for data handling.

Discover how to use dictionaries in Python to store key-value pairs, update and delete by key, and handle hashable keys while supporting frequency, stop words, lemmatization, stemming, and sorting.

Master Python string manipulation with zero-based indexing, slicing, and case handling, explore substring searches, palindrome checks, and tokenization for NLP preprocessing and text analysis.

Learn how to handle Python date time features by converting raw strings to date time objects, and extracting day of the week, month, and hour, plus formatting data for modeling.

Explore churn prediction using a bank dataset with preprocessing, imputation, feature engineering, and logistic regression. Align the model with business constraints, evaluate with metrics, and discuss productionization.

Explore lambda functions in Python, learn to define one-line functions with lambda, apply filters and simple iterations, and use lambda and filter for data cleaning and pre-processing.

Explore lambda expressions in Python to filter and transform data with filter, map, and reduce; apply to lists and strings for preprocessing, tokenization, and basic NLP tasks.

Learn how to declare local and global variables inside Python functions, call functions, and use return statements to produce values, enabling custom, reusable code, and passing arguments for dynamic results.

Learn how Python passes parameters with positional, keyword, and variable-length arguments, and how return statements produce results. Explore local and global scope and the role of key-value pairs.

Master global keyword usage and recursion in Python, compare iterative and recursive approaches, and apply map and for loops to tasks like handshakes and Fibonacci.

Master Python date and time handling by converting diverse formats and extracting hour, minute, day of week, month, quarter, and week from your data frame.

Explore confusion matrices and metrics like precision, recall, f1 score, specificity, and sensitivity for binary and multi-class classification. Interpret ROC curves and AUC to evaluate model performance.

Develop data visualization and analysis skills in Python by using the math and random libraries, and master date time formatting with strftime directives for plotting and data cleaning.

Perform exploratory data analysis in Python using NumPy and pandas, load datasets from Kaggle, and describe them with mean, standard deviation, and percentiles; visualize with Matplotlib and Seaborn.

Explore basic data exploration in Python by creating heatmaps and pair plots to uncover feature correlations, handle mixed data types, and guide feature selection in small datasets.

Explore outlier handling and visualizing distributions with distplot and joint plots, using box, strip, and region plots in pandas and numpy.

Remove outliers to refine data and use joint plots, pair plots, strip plots, region plots, and box plots to reveal distributions with pandas and numpy.

Learn how to extract month from timestamps, one-hot encode month and country, and build a python logistic regression model to predict ad clicks with a 70/30 train-test split.

Explore logistic regression as a supervised learning method for classification and regression with sigmoid probabilities and a 0.5 threshold. Evaluate precision, recall, F1, and ROC curves, noting overfitting risks.