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Coursera Free Machine learning Specialization Course by Standford University | Coursera Free Certificate

Coursera Free Machine learning Specialization Course by Standford University | Coursera Free Certificate

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Hi everyone! Here is a fantastic update for all learners who are searching for certified Machine Learning Course. Coursera have collaborated with standford university to launch Machine Learning Specialization Course with certification. So stay connected to know in detail about this course until the end of the post and find out how to get coursera certificate for free.

About Coursera :

The massive open online course provider Coursera Inc. was established in the United States in 2012 by computer science academics Andrew Ng and Daphne Koller from Stanford University. Coursera offers online courses, certifications, and degrees in a range of areas in collaboration with universities and other organisations.

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Regarding DeepLearning. AI :

DeepLearning Founded in 2017 by machine learning and education pioneer Andrew Ng to meet the demand for world-class AI education, AI is an education technology company that is enabling the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community.

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Stanford University :

In the census-designated place of Stanford, California, close to the city of Palo Alto, is where Stanford University, formally Leland Stanford Junior University, is situated. Over 17,000 students attend the campus, which is one of the biggest in the country and covers 8,180 acres.

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What you will discover :

  • Create supervised models for prediction and binary classification tasks using NumPy and scikit-learn (linear, logistic regression)
  • Create a neural network using TensorFlow and train it to perform multi-class classification. Create decision trees and apply tree ensemble methods.
  • Apply standard practises for machine learning development and leverage unsupervised learning methods, such as clustering and anomaly detection

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  • Choice Trees
  • Virtual neural network
  • Rational Regression
  • Advisory Systems
  • Regular Regression
  • Regulating Behavior to Prevent Overfitting
  • Descent in Gradient
  • Xgboost
  • Tensorflow

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Machine Learning Specialization Courses

  • Regression and classification under supervision of machine learning
  1. 1. You will learn the following in the first course of the machine learning specialisation: 1. Create machine learning models in Python using the well-known NumPy and scikit-learn modules. 2. Create and train supervised machine learning models for binary classification and prediction problems, including logistic regression.
    2. SKILLS YOU WILL GET: Gradient Descent, Supervised Learning, Linear Regression, and Logistic Regression for Classification. Regularization to Avoid Overfitting.
  • Advanced Learning Methodologies
  1. 1. You will learn the following in the second course of the Machine Learning Specialization: 1. Create and train a neural network using TensorFlow for multi-class classification 2. Use best practises for machine learning development to make your models more generic to data and tasks in the real world. 3. Construct and use tree ensemble methods, such as random forests and boosted trees, for decision trees.
  2. 2. SKILLS YOU WILL GET: Artificial Neural Network, Xgboost, Tensorflow, Tree Ensembles, and Model Development Advice are among the skills you’ll acquire.
  • Reinforcement learning, unsupervised learning, and recommenders
  1. 1. Use unsupervised learning methods for unsupervised learning, such as clustering and anomaly detection, in the third course of the machine learning specialisation. 2. Create recommender systems using deep learning techniques based on content and collaborative filtering. 3. Create a model for deep reinforcement learning.
  2. 2. SKILLS YOU WILL GET: Collaborative filtering, unsupervised learning, recommender systems, reinforcement learning, and anomaly detection are among the skills you will acquire.

Coursera and Stanford University Course Benefits

  • Practical Project – Each Specialization comes with a practical project. The project(s) must be completed successfully in order for you to complete the Specialization and receive your certificate. You must complete all of the other courses before beginning the hands-on project course, if one is included in the Specialization.
  • After completing the practical project, students will receive a certificate of completion

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