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

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Last updated 9/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + subtitle | Duration: 71 Lessons ( 14h 34m ) | Size: 1.4 GB

Develop Foundational Machine Learning Skills. Add Supervised, Unsupervised, and Deep Learning techniques to your Data Science toolkit.

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.

Skills you'll gain
Unsupervised Learning
Python Programming
Deep Learning
hyperparameter tuning
Supervised Learning
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. Learn more:
MS in Data Science:

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MS in Computer Science:

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Applied Learning Project
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.

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