Machine Learning in Python for Absolute Beginners

Explore four major types of machine learning, including supervised, unsupervised, semi supervised, and reinforcement learning. See how labeled data guides predictions, how clustering forms groups, and how rewards shape learning.

Explore machine learning applications, including traffic prediction, medical diagnosis, fraud and fake news detection, virtual assistants, email filtering, product recommendations, language translation, speech and image recognition, and self-driving car research.

Learn how to integrate, transform, normalize, reduce, and clean data from multiple sources to prepare text data for efficient, accurate machine learning.

Load, clean, and preprocess data with Pandas; assess quality, handle missing values and noise, encode and reduce dimensionality, and split data into training (70–80%), testing (20%), and optional validation (10%).

Explore hyperparameter tuning with grid search and random search on the iris dataset, training logistic regression and decision tree models, evaluating best parameters and scores.

We covered validation techniques, overfitting and finding a good fit, hyperparameter tuning and learning rate, bias vs variance, and visualizing model results to improve accuracy, preparing for supervised learning.

Explore non linear regression, a form of polynomial regression that simulates non linear relationships. See how curved data trends favor polynomial approaches over linear regression, with quadratic and cubic examples.

Explore supervised learning through classification algorithms, their applications, and strengths and weaknesses. Delve into decision tree, night based random forest, and SVM, with practical insights for beginners.

Explore logistic regression for binary outcomes and logit function-based probability, and compare Bayes, k-nearest neighbors, support vector machines, and decision trees for classification with applications like email spam.

Implement a support vector machine in Python, load and validate data, perform feature engineering, split 75% training and 25% testing, fit the model, predict, and evaluate accuracy on test set.

Explore k-nearest neighbor and support vector machines with practical code snippets, examining multiclass classification, advantages, and disadvantages. Apply these concepts through case studies, including fake news detection.