Machine Learning Zero to Hero: Step by Step with Python

Explore a Python refresher for machine learning, covering core concepts, data structures, control flow, functions, and object-oriented programming, with essential libraries like numpy, pandas, matplotlib, and seaborn.

Explore the basics of numpy, pandas, matplotlib, and seaborn for data analysis and visualization, with hands-on examples of arrays, data frames, bar and line charts, and statistical plots.

Learn how vectors and matrices underpin linear algebra for machine learning, including dot products, cosine similarity, and linear transformations with NumPy in Python, with applications to linear regression.

Explore statistics and probability for machine learning with descriptive statistics, mean, median, and standard deviation to understand data, examine normal distribution, correlations, covariance, using study hours and test scores.

Explore derivatives and gradients in machine learning, and visualize how input changes affect model performance using Python with sympy, numpy, and matplotlib, including gradient descent intuition.

Learn data preprocessing and cleaning with pandas and numpy, detect and fill missing values, handle duplicates, and prepare clean, bias-free data for machine learning models.

Explore supervised learning by defining training data with input features and outputs, and compare classification and regression algorithms, while mastering data pre-processing, cleaning, transformation, and reduction techniques.

Introduce simple linear regression to relate variables with y = m x + a, and demonstrate fitting and predicting house prices from area using NumPy and pandas.

Explore polynomial regression as an extension of linear regression to model non-linear relationships, using python along with numpy and matplotlib to create degree-based polynomial features and visualize results.

Discover logistic regression for binary classification, using a sigmoid output, train-test split, and accuracy evaluation to understand model performance with single and multiple features.

Explore evaluating machine learning models with confusion matrix, accuracy, precision, recall, f1-score, and roc auc using a python logistic regression example on the breast cancer dataset.

Learn how to implement k-means clustering in Python to group similar data points, visualize unclustered data, and interpret cluster centers using a three-cluster synthetic dataset.

Explore hierarchical clustering, visualize dendrograms, and implement agglomerative clustering in Python using synthetic data to discover how data points form clusters without predefining a number of clusters.

Explore dbscan, a density-based clustering method that shapes clusters and detects noise, outperforming k-means on non-linear data; tune eps and min_samples on moons data set and iris data set.

Explore dimensionality reduction techniques like PCA and t-SNE to simplify high-dimensional data, visualize clusters, and improve model performance in Python using real datasets such as iris and digits.

Clean data by handling missing values with median imputation and removing rows in the Titanic data set, then detect and remove outliers with IQR in a housing price example.

Apply feature scaling to the iris data, using standardization to zero mean and unit variance and normalization to a 0–1 range with min‑max scaling.

Learn feature selection techniques to improve model performance and reduce overfitting, applying filter methods like correlation and wrapper methods such as recursive feature elimination with linear regression.

Scale hyperparameter tuning with grid search cv and randomized search cv to optimize models, using iris data set, random forest classifier, and support vector classifier, guided by cross validation.

Learn cross-validation techniques to evaluate model generalization beyond a single train-test split. Apply k-fold and stratified k-fold cross-validation on the iris dataset with logistic regression to obtain robust scores.

Explore bagging and boosting, two ensemble methods that improve model performance. Bagging reduces variance with random forest classifiers; boosting reduces bias by sequentially improving weak learners.

Master random forest in depth by learning how multiple decision trees reduce overfitting and boost accuracy, with classification on iris and regression on Boston data.

Explore XGBoost, an optimized extreme gradient boosting algorithm that delivers high performance with regularization, parallel processing, and missing value handling, demonstrated on iris classification and regression tasks.

Implement classification with LightGBM on the iris dataset using a LightGBM classifier, and perform regression on synthetic data with a LightGBM regressor. Explore speed, efficiency, and mean squared error optimization.

Learn what neural networks are and how training tunes input, hidden, and output layers to recognize patterns, with types like feed-forward, convolutional, recurrent, modular, and deconvolutional networks.

Confront overfitting in neural networks using dropout and L2 regularization. The lecture shows deactivating neurons during training and penalizing large weights with practical Keras examples.

Explore text preprocessing for natural language processing by performing tokenization, stopwords removal, and lemmatization to clean and standardize text for modeling.

Explore word embeddings and convert words into numerical vectors using Word2Vec and GloVe; learn to measure contextual similarity and train a simple vector model in Python.

Learn what time series data is: a sequence of data points over time where time and order matter. Visualize stock price and daily temperatures using Python pandas and matplotlib.

Master moving averages and exponential smoothing to smooth time series data and produce short-term forecasts, with practical stock price examples in pandas.

learn how stationary time series have constant mean, variance, and autocorrelation, and how first differencing with adf tests can stabilize data for forecasting in python.