VuBacktracking / Coursera-Machine-Learning-Specialization

Contains Optional Labs and Solutions of Programming Assignment for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2023) by Prof. Andrew NG

Machine Learning Specialization

Contains Optional Labs and Solutions for Programming Assignments for the Machine Learning Specialization (Updated) by Prof. Andrew NG

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Skill you'll gain:

• Python
• Regression
• Classification
• Recommendation System
• Artificial Neural Network
• ... And more!!!

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What will you 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

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Applied Learning Project

By the end of this Specialization, you will be ready to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
• Build and train a neural network with TensorFlow to perform multi-class classification.
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.

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Outline of Machine Learning Specialization Course

Course 1 - Supervised Machine Learning: Regression and Classification:

In the first course of the specialization, you'll:

• Have a good understanding of the concepts of Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Gradient Descent,...
• Build simple machine learning models in Python using popular machine learning libraries NumPy & scikit-learn.
• Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression.

Course 2 - Advanced Learning Algorithms:

In the second course of the specialization, you'll able to:

• Build and train a neural network with TensorFlow to perform multi-class classification.
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.

Course 3 - Unsupervised Learning, Recommenders, Reinforcement Learning

In the last course of the specialization, you'll be able to:

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection
• Build a deep reinforcement learning model
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method

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Certificates

1. Machine Learning Specialization
2. Supervised Machine Learning: Regression and Classification
3. Advanced Learning Algorithms
4. Unsupervised Learning, Recommenders, Reinforcement Learning