Let’s sow the seed of Artificial Intelligence first.
I’m a human being, capable of doing terrible things. –Awolnation
AI the father!
Just like the human intelligence the machines are too getting equipped with thinking power, intelligence, and feelings. Machine Learning is clinging at the jet speed. Cognitive accomplice makes Machine Learning more and more widespread.
Machine Learning is spreading like fire in the jungle. The Kindle has entered into every field of IT. It’s one way or the other, but this phrase is turning into the cliche’ in the niche of Software world.
There are numerous courses, offering the teachings as well as skills required to propagate the daily problems along with the techniques to tackle Big Data. Thus, looking at the present technical demands it is advisable to learn Machine Learning rather than hitting your head into thick walls of the internet.
The aura after completing the course spreads like the wings of albatross. More hands on some essential ML libraries as Scikit Learn, pandas, numpy, Seaborn, etc. to mine the data, analyze it and visualize whether one is heading in the right direction or not.
Machine Learning: The Big Technical Name
A plethora of words is available with Google to define the phrase. From my point of view, machine learning is hype to create expert systems. The mechanisms adopted in computer science while developing intelligence in machines or applications is machine learning.
The four walls of coding do not bound the machines/software. Instead the particular adapts itself according to the data or surroundings around it. Coding gets flexible from time to time. Explicit programming is involved in developing machine learning applications.
Machine Learning has its roots everywhere; it’s deep-seated. Throwing some light on its applications we have: Medicine, Healthcare, Gaming, Defence, Security, Image Recognition, Analysis, speech synthesis, NLP and the seashore is not under the radar yet.
On a daily basis, the new research paper is being published with a new set of mathematical equations and physics involved in the explanation.
Merely Machine Learning is further subdivided into three phases viz. (a) Supervised Learning, (b) Unsupervised Learning and third in the race is (c) Reinforcement Learning.
We have the labeled dataset, accounting which the outcome is calculated or guessed following the principle of the best line fit. This is Supervised Learning.
Y = F(X)
To predict the patterns in data unsupervised learning is punched. The data fed to the models here is completely random. The machine has to do work in labeling the data.
It’s better to discuss an example to have a better understanding of the fear. Storytime! With a sprinkle of poetry a bit.
A short time ago, there was a guy named ‘Din.’ He placed an undeniable offer by the big lad ‘Gin.’ The latter said that the former could have a Kohinoor from the container made of Tin. Hearing this the first one wondered and said Am I onto to commit a Sin? On asking the question ‘How?’ To Win. The procedure of getting the priceless hardest substance, replied the Gin, is up to Him. He is to figure it out the answer to this Bin. On this Win, he had a wide Grin.
The unknown steps that Din took to win that Bluestone bypassing the hurdles on the way; The learnings he applied to his previous experience to reach the goal, is called Reinforcement Learning.
What does Machine Learning Covers?
A number of algorithms with their fundamental mathematics involved are explained in very few MOOC (Massive Open Online Courses). Mapping , Least Squares, Matrix Completion, Rank Parameterization, Sparse Inverse Covariance, Sparse Principal Covariance(PCA). Few algorithms that are mostly used in industry are Linear Regression, Logistic Regression, SVM, Bayesian algorithm, etc.
The Prerequisite for this is Linear Algebra, Statistics, and Calculus.
Machine Learning is driving crazy the data science prospect. Data Science is another domain of data problems which is profoundly being dealt with machine learning techniques or algorithms presently to achieve the insights.
The processes indulged in machine learning are quite similar to data mining, predictive modeling. Machine Learning emphasis more precisely on pattern search. The hidden patterns are looked through the data. Machine Learning techniques are usually applied to Supervised Learning.
Unsupervised Learning uses Neural Networks.
Some of the majorly used ML algorithms in every data enthusiastic laps are regressions, SVMs, Bayesian algorithms.
The focus and primary purpose of applying these algorithms is to find the correlations between variables and hence to determine the predictions.
The optimal path is found out to arrive at the outcome using observations about specific actions.
The grouping of the data is done here on the basis of alike or similar characteristics.
Huge chunks of data are collected and correlations amid variables to know the processing of incoming data in the future.
The Mind-Boggling ML Use-Case
Machine Learning: Boost the Smart Satellite Data
Observing the Earth is a phenomenal phenomenon and that too from the space. Machine Learning is being implemented in the satellite to hover the hidden patterns in the satellite data. Various use cases are being formulated from the images generates from above. The extraterrestrial space is creating a new landscape in the scientists’ brains. The efforts fulcrum at understanding the natural disasters to carry out the espionage efforts.
Machine Learning just provides an Oculus for Earth Observation (EO) by conferring scale, automated image tagging and arranging, and, mapping the correlations. EO is a classic approach to use for a completely new set of applications like getting the glimpse of a third-world nation.
Radiant Solutions’ contract with NGA (U.S. National Geospatial-Intelligence Agency) to provide more than a billion labeled objects with high resolution sat images which will accelerate the Machine Learning algorithms mechanism to extract resourceful and valuable information from the imagery. To support the national security and humanitarian missions data is put under the supervision of ML algorithms; is the ultimatum.
The Vision of Machine Learning
The Machine Learning algorithms have been present since the 1950s, but now they have gained a huge hype, in every industry, with another name of Artificial Intelligence (AI), and has its dominance plus prominence both.
Speaking more of which the deep learning algorithms are powering every new thought and idea of an individual or a firm. So machine learning has a very wide span in upcoming years. The study is embracing the most Hi-Tech companies at first. The big mouths Google, Amazon, Microsoft, Huawei, Sony hire the customers for platform services which cover the machine learning visible spectrum along with the infrared and ultraviolet zone of white light.
With every day’s new ray of sun new concept evolves and so does new techniques are figured to formulate the fresh brains. Just like the plague of pollution, the demand for optimizing machine learning algorithms is also on the hike. Researchers’ researches are more focusing on exploring the ways to develop the formulae for flexible models.
Witnessing all above the best and all-rounder tool to dwell with machine learning is Python so far. But in this race R is emerging like Su-35(Sukhoi-35) in the data race. Both are very fine granules to mix in any liquid to prepare the colloidal sol. Python is a general-purpose while R is a specific purpose and is more drawn towards the Statistics perspective.
Machine Learning with a fist of Mathematics is a mesmerizing field. While working on this desk most of the time, you’ll astonish yourself.