Delivered by Stanford University’s renowned Andrew Ng. This course provides a broad introduction to Machine Learning, Data Mining & Statistical pattern recognition. 4.9 (124604 Ratings) & 27,50,130 already enrolled.
Offered by top researchers at the University of Washington, This course covers Clustering, Information Retrieval, Prediction, Classification of all other relevant topics. 4.7 (18,514 ratings) & 71,550 already enrolled.
Offered by Columbia University's Prof. John W. Paisley, Course topics include classification and regression, clustering methods, sequential models, matrix factorization, topic modeling & model selection. Avg Rating 4.8
Offered by the University of Minnesota, This course will help you to Master recommender systems, Learn to design, build and Evaluate recommender systems for commerce, content. 4.3 (1034 ratings) & 7781 already enrolled.
Developed by Andrew Ng in union with Stanford Professors, NVIDIA & deeplearning.ai. Course offers Deep Learning Specialization. Master Deep Learning, and Break into AI. 4.8 (190,603 ratings) & 272,639 already enrolled.
In this course, you'll Learn to create Machine Learning Algorithms in Python, R from two Data Science experts. As a BONUS the course includes Python, R code templates. 4.5 (104,149 ratings) & 528,822 already enrolled.
Created by Frank Kane (Ex Amazon, IMDb Employee ) This course covers Machine learning tutorial with data science, Tensorflow, artificial intelligence & Neural networks. 4.5 (18,270 ratings) & 114,214 students enrolled.
This Course will help you to learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn, Tensorflow, Python to analyze data, powerful Machine Learning algorithms and more. 4.5 (60,834 ratings) & 287,674 students...
This course will cover Neural Network, TensorFlow, Artificial Neural Networks, Convolutional & Recurrent Neural Networks, AutoEncoders Reinforcement Learning and more. 4.4 (13,266 ratings) & 72,349 students enrolled.
You'll learn how to create amazing data visualizations with R, Advanced R Features, R Data Frames, R to handle Excel Files, Web scraping with R, Connecting R to SQL, etc. 4.6 (9,173 ratings) & 49,333 students enrolled.
In this course, you'll get to know to understand Artificial Neural Networks, Recurrent Neural Networks, Self Organizing Maps, Boltzmann Machines, and Auto-Encoders. 4.5 (26,702 ratings) & 2.02,184 students enrolled.
If Python or R isn’t your cup of tea, then this course will help you to learn the latest Big Data technology - Spark and Scala, including Spark 2.0 Data Frames. 4.3 (3,875 ratings) & 22,471 students enrolled.
This course provides a Guide to AWS Certified Machine Learning, Specialty and Practice Test. You'll learn AWS Machine Learning algorithms, Predictive Quality assessment. 4.4 (1273 ratings) & 11142 students enrolled.
Created by Lazy Programmer Inc, This Course provides a Complete Guide to Artificial Intelligence, prep for Deep Reinforcement Learning with Stock Trading Applications. 4.5 (5,800 ratings) & 31,464 students enrolled.
If you want to master Artificial Intelligence using Deep Learning and Neural Networks, then this is the right choice for you. Created and taught by Lazy Programmer Inc. 4.6 (2,613 ratings) & 27,881 students enrolled.
Offered by IBM, This course focuses on models in production at a hypothetical streaming media company. There is an introduction to IBM Watson Machine Learning.
In this project, you’re going to learn what an autoencoder is, use Keras with Tensorflow as its backend to train your own autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images.
Offered by Uni. of London, This course is designed to introduce you to Machine Learning without needing any programming. That means that we don't cover the programming based machine learning tools like python and Tensor Flow.
Offered by IBM, By the end of this course you will be able to: Employ the tools that help address class and class imbalance issues. Alss able to Explain the ethical considerations regarding bias in data
This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods.
This course is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models.