Coursera – Machine Learning Specialization (by DeepLearning AI)

Coursera – Machine Learning Specialization (by DeepLearning AI)
English | Tutorial | Size: 3.46 GB

#BreakIntoAI with Machine Learning Specialization. Master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng

What you’ll learn
• Build ML models with NumPy & scikit-learn, build & train supervised models for prediction &
binary classification tasks (linear, logistic regression)
• Build & train a neural network with TensorFlow to perform multi-class classification, & build
& use decision trees & tree ensemble methods
• Apply best practices for ML development & use unsupervised learning techniques for
unsupervised learning including clustering & anomaly detection
• Build recommender systems with a collaborative filtering approach & a content-based deep
learning method & build a deep reinforcement learning model

Skill You Will Gain:
• Decision Trees
• Artificial Neural Network
• Logistic Regression
• Recommender Systems
• Linear Regression
• Regularization to Avoid Overfitting
• Gradient Descent
• Supervised Learning
• Logistic Regression for Classification
• Xgboost
• Tensorflow
• Tree Ensembles

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

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