Machine Learning: Theory and Hands-on Practice with Python

In the Machine Learning specialization, you will delve into the fundamentals of supervised and unsupervised learning, as well as introductory deep learning topics using Python. This course equips you with the skills to apply machine learning algorithms to real-world data, understand when to use different models, and enhance model performance. With a strong emphasis on practical application, you will gain hands-on experience with popular Python libraries and develop the ability to evaluate and compare the strengths and weaknesses of various machine learning models.

Throughout the course, you will explore classic supervised learning algorithms such as logistic regression, decision trees, KNN, and ensembling methods like Random Forest and Boosting. Additionally, you will delve into unsupervised methods, including dimensionality reduction techniques, clustering, and recommender systems. The specialization culminates with an introduction to deep learning basics, encompassing model architectures, neural network building and training using libraries like Keras, and practical examples of CNNs and RNNs.

By the end of the course, you will be adept at selecting the most suitable machine learning models based on data properties, tuning hyperparameters to enhance model performance, and applying various techniques such as sampling and regularization to refine your models.