Coursera – Machine Learning: Theory and Hands-on Practice with Python Specialization 2025-4

Coursera – Machine Learning: Theory and Hands-on Practice with Python Specialization 2025-4 Downloadly IRSpace

Apr 18, 2025 - 19:50 Updated: Aug 17, 2025 - 19:11

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Coursera – Machine Learning: Theory and Hands-on Practice with Python Specialization 2025-4

Machine Learning: Theory and Hands-on Practice with Python Specialization, In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems.

We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs. This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. In this specialization, you will build a movie recommendation system, identify cancer types based on RNA sequences, utilize CNNs for digital pathology, practice NLP techniques on disaster tweets, and even generate your images of dogs with GANs. You will complete a final supervised, unsupervised, and deep learning project to demonstrate course mastery.

What you’ll learn

Explore several classic Supervised and Unsupervised Learning algorithms and introductory Deep Learning topics.
Build and evaluate Machine Learning models utilizing popular Python libraries and compare each algorithm’s strengths and weaknesses.
Explain which Machine Learning models would be best to apply to a Machine Learning task based on the data’s properties.
Improve model performance by tuning hyperparameters and applying various techniques such as sampling and regularization.