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The following is a list of the top best curated Machine Learning coursera courses that are highly in demand and you can enroll for free.

TOP 18 Machine Learning Courses:

  1. Deep Learning

Core Module:

  • Neural Networks and Deep Learning
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • Structuring Machine Learning Projects
  • Convolutional Neural Networks
  • Sequence Models

Offered by: deeplearning.ai

2. Machine Learning

Core Module:

  • Introduction
  • Linear Regression with One Variable
  • Linear Algebra Review
  • Linear Regression with multiple Variables
  • Octave/Matlab Tutorial
  • Logistic Regression
  • Regularization
  • Neural Networks: Representation
  • Neural Networks: Learning
  • Advice for Applying Machine Learning
  • Machine Learning System Design
  • Support Vector Machines
  • Unsupervised Learning
  • Dimensionality Reduction
  • Anomaly Detection
  • Recommender Systems
  • Large Scale Machine Learning
  • Application Examples: Photo OCR

Offered by: Stanford University

3. Mathematics for Machine Learning

Core Module:

  • Mathematics for Machine Learning: Linear Algebra
  • Mathematics for Machine Learning: Multivariate Calculus
  • Mathematics for Machine Learning: PCA

Offered by: Imperial College London

4. Advanced Machine Learning

Core Module:

  • Introduction to Deep Learning
  • How to Win a Data Science Competition: Learn from Top Kagglers
  • Bayesian Methods for Machine Learning
  • Practical Reinforcement Learning
  • Deep Learning in Computer Vision
  • Natural Language Processing
  • Addressing Large Hadron Collider Challenge by Machine Learning

Offered by: National Research University Higher School of Economics

5. Machine Learning with TensorFlow on Google Cloud Platform

Core Module:

  • How Google does Machine Learning
  • Launching into Machine Learning
  • Intro to TensorFlow
  • Feature Engineering
  • Art and Science of Machine Learning

Offered by: Google Cloud

6. Machine Learning and Reinforcement Learning in Finance

Core Module:

  • Guided Tour of Machine Learning in Finance
  • Fundamentals of Machine Learning in Finance
  • Reinforcement Learning in Finance
  • Overview of Advanced Methods of Reinforcement Learning in Finance

Offered by: NYU | Tandon School of Engineering

7. Probabilistic Graphical Models

Core Module:

  • Probabilistic Graphical Models 1: Representation
  • Probabilistic Graphical Models 2: Inference
  • Probabilistic Graphical Models 3: Learning

Offered by: Stanford University

8. Advanced Data Science with IBM

Core Module:

  • Fundamentals of Scalable Data Science
  • Advanced Machine Learning and Signal Processing
  • Applied AI with Deep Learning

Offered by: IBM

9. Applied Data Science Specialization

Core Module:

  • Python for Data Science and Ai
  • Data Analysis with Python
  • Data Visualization with Python
  • Applied Data Science Capstone

Offered by: IBM

10. Data Mining

Core Module:

  • Data Visualization
  • Text Retrieval and Search Engines
  • Text Mining and Analytics
  • Pattern Discovery in Data Mining
  • Cluster Analysis in Data Mining
  • Data Mining Projects

Offered by: Illinois

11. Recommender Systems

Core Module:

  • Introduction to Recommender Systems: Non-Personalized and Content-Based
  • Nearest Neighbor Collaborative Filtering
  • Recommender Systems: Evaluation and Metrics
  • Matrix Factorization and Advanced Techniques
  • Recommender Systems Capstone

Offered by: University of Minnesota

12. Introduction to Machine Learning

Core Module:

  • Simple Introduction to Machine Learning
  • Basic of Model learning
  • Image Analysis with Convolutional Neural Network
  • Introduction to Natural Language Processing
  • Introduction to Reinforcement Learning

Offered by: Duke University

13. Convolutional Neural Networks in TensorFlow

Core Module:

  • Exploring a Larger Dataset
  • Augmentation: A Technique to Avoid Overfitting
  • Transfer Learning
  • Multiclass Classifications

Offered by: deeplearning.ai

14. Computational Neuroscience

Core Module:

  • Introduction & Basic Neurobiology
  • What do Neurons Encode? Neural Encoding Models
  • Extracting Information from Neurons: Neural Decoding
  • Information Theory & Neural Coding
  • Computing in Carbon
  • Computing with Networks
  • Networks that Learn: Plasticity in the Brain & Learning
  • Learning form Supervision and Rewards

Offered by: University of Washington

15. Machine Learning

Core Module:

  • Machine Learning Foundations: A Case Study Approach
  • Machine Learning: Regression
  • Machine Learning: Classification
  • Machine Learning: Clustering and Retrieval

Offered by: University of Washington

16. Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization

Core Module:

  • End-to-End Machine Learning with TensorFlow on GCP
  • Production Machine Learning Systems
  • Image Understanding with TensorFlow on GCP
  • Sequence Models for Time Series and Natural Language Processing
  • Recommendation Systems with TensorFlow on GCP

Offered by: Google Cloud

17. Sequences, Time Series and Prediction

Core Module:

  • Sequences and Prediction
  • Deep Neural Networks for Time Series
  • Recurrent Neural Networks for Time Series
  • Real-World Time Series Data

Offered by: deeplearning.ai

18. Natural Language Processing in TensorFlow

Core Module:

  • Sentiment in Text
  • Word Embeddings
  • Sequence Models
  • Sequence Models and Literature

Offered by: deeplearning.ai

Many of the courses that are available in Coursera have the audit option, which allows you to access course materials for free.

Below are steps for you to enroll for a specific course for free:

  • Open the Coursera homepage of the course that you wants to Enroll in
  • Click on the Enroll for free option button
  • Fill in the given subscription form
  • In the next screen, click the Start Free Trial button.
  • Enter the details of your Credit card or Paypal details
  • Start Free Trial button at the bottom of the screen
  • You will get a 1-week free trial for the course you want to enroll in

All the best and do let us know if you have any questions.