Coursera & Standford University Free Machine Learning Specialization Course


Hello folks! A fantastic update for all learners who are searching for certified Machine Learning Course as DeepLearning.AI ,Coursera and Standford University collaborate to launch Machine Learning Specialization Course with certification so stay connected to know in detail about this course until the end of the post

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About Coursera

Coursera Inc. is a U.S.-based massive open online course provider founded in 2012 by Stanford University computer science professors Andrew Ng and Daphne Koller. Coursera works with universities and other organizations to offer online courses, certifications, and degrees in a variety of subjects.

About DeepLearning.AI

DeepLearning.AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community which was founded in 2017 by machine learning and education pioneer Andrew Ng to fill a need for world-class AI education.

About Stanford University

Stanford University, officially Leland Stanford Junior University, is a private research university located in the census-designated place of Stanford, California, near the city of Palo Alto. The campus occupies 8,180 acres, among the largest in the United States, and enrolls over 17,000 students.

Coursera & Standford University


  • 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


  • 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

Machine Learning Specialization Courses

  1. In the first course of the Machine Learning Specialization, you will learn: 1. Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. 2. Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
  2. SKILLS YOU WILL GAIN– Regularization to Avoid Overfitting ,Gradient Descent, Supervised Learning, Linear Regression, & Logistic Regression for Classification.
  1. In the second course of the Machine Learning Specialization, you will learn:- 1. Build and train a neural network with TensorFlow to perform multi-class classification 2. Apply best practices for machine learning development so that your models generalize to data and tasks in the real world 3. Build and use decision trees and tree ensemble methods, including random forests and boosted trees
  2. SKILLS YOU WILL GAIN-Artificial Neural Network, Xgboost, Tensorflow, Tree Ensembles , & Advice for Model Development.
  1. In the third course of the Machine Learning Specialization, you will: 1.Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. 2. Build recommender systems with a collaborative filtering approach and a content-based deep learning method. 3. Build a deep reinforcement learning model.
  2. SKILLS YOU WILL GAIN-Collaborative Filtering, Unsupervised Learning, Recommender Systems, Reinforcement Learning, & Anomaly Detection.

Benefits of Coursera & Standford University Course

  • Hands-on Project-Every Specialization includes a hands-on project. You’ll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you’ll need to finish each of the other courses before you can start it.
  • Learners will get course completion certificate after completing hands-on project

How to Apply?

To join official Machine Learning Specialization, CLICK HERE

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