I have a great interest in big data techniques and machine leaning algorithms. For that reason I took the Stanford University online machine learning course taught by Prof. Andrew Ng on Coursera.org (the biggest MOOC on the web). I passed the course with a final grade of 98/100 (see certificate below). Here you can find links to videos I made explaining some of the applications and algorithms which I learned from the course. Those are based on assignments during the class. The class introduced different regression models and advanced machine learning algorithms to such problems as anti-spam, image recognition, clustering, building recommender systems, and many other problems. I highly recommend the course for those who want to get some hands on advanced machine learning techniques.
Linear regression using gradient descent
Logistic regression with two possible outcomes
Logistic regression with complex data distribution
Neural network with One-Vs-All algorithm
The problem of under-fitting and over-fitting
Support Vector Machines (SVMs)
Unsupervised learning: Clustering via K-Means algorithm
Anomaly detection in machine learning
I also took the "Hadoop Platform and Application Framework" course offered by the University of California at San Diego (see certificate below). The course offers in-depth knowledge of Hadoop Platform and associated applications, and how to get the most powerful Big Data techniques to work on applications. The course introduces hands-on techniques for Hadoop ecosystem including HDFS, MapReduce, YARN, HBase, Spark, Hive, PIG, Impala, Hue, Solr, and Oozie. It is a great course to learn about parallelization and partitioning data for a more efficient computing.
In addition I'm enrolled in the "Applied Data Science in Python" specialization offered by the University of Michigan in coursera.org. So far I finished, "Data Science in Python Pandas", "Applied Plotting, Charting and Data Represeantation in Python" and "Applied Machine Learning in Python" courses. See the Certificates below. I have learned some hands on techniques and models for building and testing hypotheses based on linear and non-linear statistical regression models, constructing and implementing algorithmic solutions, and predictive analysis based on behaviour methods. This is done using the "Pandas" library in python for data collection and cleaning, "Matplotlib" library for plotting, charting, and visualization, and "Sciket-Learn" library for machine learning.
Cloud computing is of great interest to me too. Thus I'm enrolled in the "Data Engineering on Google Cloud Platform" specilization offered on Coursera by Google Cloud. So far I have taken "Google Cloud Platform Big Data and Machine Learning Fundamentals" course (see certificate below). It is a introduction of using Google BigQuery, Dataproc, Datalab, DataStore, BigTable, TensorFlow, Cloud ML, CLoud APIs, etc.. I highly recommend the course if you will be dealing with Big Data and want to migrate all your work into the cloud.