Python Machine Learning: From Beginner to Pro

Begin your Python machine learning journey with a beginner-friendly introduction to supervised, unsupervised, and reinforcement learning, key algorithms, Jupyter, and a hands-on iris dataset project.

Explore how Python enables machine learning with libraries like scikit-learn, TensorFlow, Keras, PyTorch, pandas, numpy, matplotlib, seaborn, covering data collection, data preprocessing, model selection, training, evaluation, hyperparameter tuning, and deployment.

Explore the differences between AI, ML, and deep learning, and learn how Python enables training models across supervised, unsupervised, and reinforcement learning with practical examples.

Learn how supervised learning uses labeled data to map inputs to outputs and predict new data. See examples like image classification, spam detection, and sentiment analysis.

Discover unsupervised learning techniques, including clustering with k means, hierarchical clustering and DBscan, and dimensionality reduction with PCA and t-SNE, plus applications like anomaly detection, market basket analysis, and LDA.

Explore reinforcement learning, where an agent learns to maximize rewards by interacting with an environment through a policy and Q-learning, with applications in games like AlphaGo, robotics, and autonomous vehicles.

Explore a range of AI systems with brief descriptions, from conversational agents to autonomous tools, including ChatGPT, Siri, Google Assistant, IBM Watson, Tesla Autopilot, AlphaGo, Amazon Alexa, and Dell E.

Explore deep learning, neural networks, backpropagation and gradient descent, with real-world examples in image and speech recognition and natural language processing.

Install and set up Jupyter using Anaconda Navigator or Conda, launch the notebook, and explore interactive, multi-language support with markdown, visualization, and Python machine learning basics.

Set up Anaconda Navigator and Jupyter Notebook, then run machine learning tutorials. Explore code mirror extensions and iris data sets, and run other languages in Jupyter.

Explore a hands-on iris dataset demo in python with a jupyter notebook, loading data from url, inspecting shape and headers, and visualizing with pandas, matplotlib, and sklearn.

Explore linear regression fundamentals in Python, including simple and multiple regression, coefficients and intercept, R-squared interpretation, SSR and SST, p-values, and practical coding with NumPy, Pandas, and Matplotlib.

Explore descriptive statistics and probability concepts for machine learning, including distributions, hypothesis testing, regression, PMF/PDF/CDF, Bayes, and uncertainty estimation using Python and Jupyter.

Explore data categorized by type (numerical, text, time series), structure (structured, unstructured, semi-structured), learning problem, source, and domain to inform preprocessing and feature engineering.

Explore qualitative and quantitative data in machine learning, including encoding methods like one-hot and label encoding, normalization, standardization, and PCA in supervised and unsupervised learning.

Learn how information gain and entropy measure uncertainty and guide feature selection in decision trees. Use confusion matrices to evaluate classification performance and understand errors.

Explore the types of events and probability distributions, including simple, dependent, independent, mutually exclusive, non-mutually exclusive, discrete and continuous distributions, and their applications in machine learning.

Learn how to import datasets in Jupyter using pandas to read CSV files. The lesson guides you from downloading iris CSV from GitHub to loading it with pandas.

Dive into data analysis with Python by loading Titanic data set in Jupyter using pandas, numpy, seaborn, matplotlib. Explore survival patterns with plots such as count plots and histograms.

Train and test data in python using jupyter with Titanic dataset, splitting into x and y, then fit a logistic regression model and evaluate with classification report and confusion matrix.

Explore the logistic regression curve by applying the sigmoid function to features, estimate parameters via maximum likelihood or gradient descent, and interpret probabilities with thresholds and ROC curves.

Learn how a decision tree classifies and regresses data by splitting at nodes using criteria like Gini impurity and entropy, with pruning and ensemble extensions like random forest.

Apply the k-nearest neighbors classifier with scikit-learn to build, train, and evaluate a simple dataset in Jupyter, and measure accuracy on the test set.

Learn how to perform k-means clustering in python by generating a sample dataset, fitting a three-cluster model, and visualizing colored data points with red centroids in a jupyter notebook.