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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

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A-sad-ali/Machine-Learning-Specialization

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The title

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

Skill you'll gain:

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

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

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.

Outline of Machine Learning Specialization Course

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.

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.

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

Certificates

  1. Machine Learning Specialization