At present, the technology is evolving with focused on convenience; machines are being designed in a way that they can communicate with human beings meaningfully. Moreover, they are expected to help not only to household common tasks but also with a more complicated and sophisticated problem. So it is pretty intuitive to assume that there are lots of scope in Artificial Intelligence (AI) yet to explore. So, here is a thoughtful paradigm to start with machine learning.
Programming languages
First and foremost we need to have knowledge of Machine Learning (ML) compatible programing language. Here are some programming languages I enlisted in one of the previous article (Programming languages for Machine learning: Julia) and these are quite popular and suitable for ML. If you know any of the languages from the article that’s awesome. Otherwise, if you don’t know of any those programming languages, I would highly recommend learning “Python.” The main advantage with the Python is that it easy to learn and nearly all ML model is available in Python.
Statistics and Calculus
ML is an interdisciplinary science which compromises some concepts of statistical and Calculus which can be used to build intelligent applications with the help of computer science. There are a wide variety of statistic learning resources across the web.
Some online resource Statistical and Calculus topics needed for Machine Learning are:
- Khan Academy’s Linear Algebra, Probability & Statistics, Multivariable Calculus, and Optimization.
- Coding the Matrix: Linear Algebra through Computer Science Applications by Philip Klein, Brown University.
- Linear Algebra — Foundations to Frontiers by Robert van de Geijn, University of Texas.
- Applications of Linear Algebra, Part 1 and Part 2. A newer course by Tim Chartier, Davidson College.
- Joseph Blitzstein — Harvard Stat 110 lectures.
- Larry Wasserman’s book — All of statistics: A Concise Course in Statistical Inference.
- Boyd and Vandenberghe’s course on Convex optimization from Stanford.
- Linear Algebra — Foundations to Frontiers on edX.
- Udacity’s Introduction to Statistics.
Statistical concepts are also important for Data Pre-Processing and Exploratory Data analysis.
Big Data Technologies
Understanding how to access large dataset, store them and process them efficiently is very crucial to be able to process big data analysis. Technologies essential for Big data analysis are Hadoop, Spark, and Scala. Along with these online cloud platforms such as Google cloud, AWS, etc. are alternatively capable of managing big data and are easy to handle. Again lots of web resource are there to help with these techniques.
- Simplilearn: https://www.simplilearn.com/big-data-and-analytics/
- Cloudera: http://www.cloudera.com/training/certification/cca-spark.html
- Big Data University: https://bigdatauniversity.com/
- Hortonworks: http://hortonworks.com/training/certification/
- Coursera: https://www.coursera.org/specializations/big-data?action=enroll
Some of the interesting big data resources are enlisted by Bernard Marr, Big Data: 33 Brilliant And Free Data Sources Anyone Can Use.
Note: This is a guest post, and opinion in this article is of the guest writer. If you have any issues with any of the articles posted at www.marktechpost.com please contact at asif@marktechpost.com
I am Nilesh Kumar, a graduate student at the Department of Biology, UAB under the mentorship of Dr. Shahid Mukhtar. I joined UAB in Spring 2018 and working on Network Biology. My research interests are Network modeling, Mathematical modeling, Game theory, Artificial Intelligence and their application in Systems Biology.
I graduated with master’s degree “Master of Technology, Information Technology (Specialization in Bioinformatics)” in 2015 from Indian Institute of Information Technology Allahabad, India with GATE scholarship. My Master’s thesis was entitled “Mirtron Prediction through machine learning approach”. I worked as a research fellow at The International Centre for Genetic Engineering and Biotechnology, New Delhi for two years.
