Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS

Explore the theory of machine learning—how data and algorithms imitate human learning to improve predictions—through examples like stock market prediction, recommendation systems, customer segmentation, and fraud detection.

Explore descriptive statistics, probability, and distributions to infer proportions from samples. Learn to distinguish numerical and categorical data, including discrete, continuous, structured, and unstructured forms.

Explore descriptive statistics to summarize data, focusing on measures of central tendency and how the mean represents the average, while identifying outliers that can affect the dataset.

Analyze how outliers pull the mean, compare mean, median, and mode through data examples, and describe distributions with skewness and bimodal patterns.

Explore measures of spread by comparing range, variance, and standard deviation to show how data differ from the mean, with population versus sample considerations.

explains the basics of probability, including fractions, decimals, and percentages, and how to compute event likelihoods as favorable outcomes over total outcomes with union and intersection.

Learn how conditional probability extends probability with unions and intersections, and apply the formula P(A|B)=P(A∩B)/P(B) to dice sums and real-world scenarios like brand choices.

Explore how probability distributions map discrete and continuous random variables to probability mass and density functions, and summarize binomial, Poisson, and normal distributions with mean, standard deviation, and standardization.

Explore hypothesis testing by contrasting null and alternative hypotheses, sampling distributions, and standard error; assess significance with p-values and one-sided or two-sided tests, while understanding type I and II errors.

Learn to install python development environments, including online Colab and Kaggle, and offline Anaconda with Jupiter notebooks and Spyder.

Explore the Python integrated development environments and editors, learn how to write and run Python print statements, handle input, save files, and view output in IDEs and Jupyter notebooks.

Explore Python basics by examining data types like integers, floats, and booleans, and master input handling, type casting, and core operators with formatting tips.

Explore Python basics, including variable naming constraints, the print end parameter, conditionals, loops, and key functions like max and user defined functions.

Explore Python data structures by mastering indexing, strings, lists, and dictionaries, including slicing, mutability, common methods (lower, upper, strip, split), nested lists, and simple sorting techniques.

Explore numpy, the python library for numerical computations and multi-dimensional arrays, learn array creation, dtype rules, indexing, slicing, reshaping, and basic operations from zeros to random matrices.

Explore the Pandas library in Python, covering Series and DataFrame, creating and indexing data, loading CSV files, and performing basic data inspection and selection.

Explore data visualization techniques for machine learning, using heart disease data to reveal distributions, relationships, and insights with seaborn, matplotlib, and pandas.

Explore data transformation techniques in Python using pandas to convert raw data into structured formats, perform feature engineering, and handle currency, dates, age, and skew through log scaling and normalization.

Import, clean, and transform data to prepare it for modeling. Explore supervised, unsupervised, semi-supervised, and reinforcement learning with regression, classification, clustering, and association.

Master linear regression for supervised tasks, optimize the best fit via gradient descent, and evaluate with MSE and R-squared, then apply multivariable regression with preprocessing and feature selection on datasets.

Learn logistic regression for binary and finite-category classification, focusing on probability outputs, and evaluate with confusion matrices, accuracy, stratified sampling, and mutual-information feature selection.

Explore k nearest neighbors for classification, compare distance metrics like Euclidean and Manhattan, and learn hyperparameter tuning via grid search and cross-validation, plus confusion matrix, precision, recall, and F1 score.

Explore Naïve Bayes, a classifier that assumes predictors are conditionally independent and uses Bayes' rule to compute P(B|A) from probabilities, with X1 and X2 features and cross-validation evaluation.

Explore how support vector machines build optimal hyperplanes using support vectors to classify data, handle linear and nonlinear separations with kernel tricks, and tune hard and soft margins for accuracy.

Learn how decision trees handle classification and regression, using information gain (entropy or Gini), prune with max depth, and evaluate training versus testing accuracy with stratified sampling and oversampling.

Explore how k-means clustering assigns data to centroids by distance, uses the elbow graph to select the optimal number of clusters, and demonstrates preprocessing and visualization on a three-class dataset.

Explore hierarchical clustering, contrasting top-down and bottom-up approaches. Learn how single, complete, and average linkage use Euclidean distance to form and validate clusters.

Learn the DBScan clustering method, detailing epsilon, minimum points, core and border points, and how noise is identified, with practical validation using the adjusted Rand index.

Learn bagging techniques like bootstrap sampling, random forests, and voting classifiers to combine multiple weak learners for improved accuracy and robust predictions.

Explore boosting and gradient boosting, where misclassified samples gain higher weights to reduce error across successive learners, compare with bagging and other models, and assess training versus test performance.

Learn how principal component analysis reduces dimensionality while preserving information, with practical steps on data scaling, min-max and standard scaling, and building PCA pipelines with two components.

Explore how recommendation systems power platforms like YouTube and Spotify, compare collaborative filtering with content-based filtering, and learn cosine similarity to generate relevant suggestions.