Machine Learning

Machine Learning(ML) – Introduction to Basics

Machine Learning (ML) – It is the hottest subject of today’s time. DataScientist is the sexiest job of today but implementation of these buzz words in real business is missing big time. The real need is to clarify, demonstrate, extract real values and reap rewards. “Machine Learning” sounds as gold mine to many businesses especially for the companies which are actually data factories i.e social media platforms.


Demystifying The Jargons

Artificial Intelligence (AI) and Machine learning (ML) are new buzzwords, which are commonly used by professionals and organizations. It is believed that these new technologies are going to impact every aspect of our lives and will open endless possibilities. AI has gone far more ahead then old school thought of “AI is bunch of if else statements”.

So its very important to understand, how these new technologies/techniques are going to change our world. People who will not adopt with this fast changing technologies will be left behind. You all are welcome to learn with me at a speed and comfort of your own. Welcome to the machine learning journey lets learn together. Sadly machine learning methods of today are mostly based on supervised machine learning (type of machine learning), this means we still have long long way to go.

Make a note of fact, if you really want to learn and have good, strong and commanding attitude over machine learning then you cant run away from mathematics, probability, linear algebra, statistics, decision theory, algorithms flow and bit of neuroscience understanding at very high.

Machine Learning and Artificial Intelligence use them as underline foundations below 3 pillars i.e. computing power, data and algorithms. ML is one of technology/technique under the umbrella of AI.

AI is using in many fields today some of them are Auto Pilot in Airplanes, Natural Language Processing, Robotics, Computer Vision, info-security and fraud detections.

Artificial Neural Networks - Everything You Need To Know

AILabPage’s Deep Learning Series

So Whats This Funny Term Machine Learning IS?

Before we embark our journey on defining machine learning, lets understand its superset i.e. Artificial Learning (AI) first. Some experts define Artificial Intelligence as any technology, which enables to demonstrate human like intelligence.

We can call AI as a colourful bundle of new techniques & emerging technologies. These technologies are for every one and anyone. No one has any proprietary claim of intellectual property etc on it. Machine learning is a subset of AI or one of the colourful and useful technique/technology out of AI bundle.

The current state for AI (Narrow AI) is based on 3 foundation pillars i.e.

  • Data (Labels, knowledge and information)
  • ComputingPower (Brain or neural network)
  • Algorithms (Logic and Experience)

Machine Learning (ML) term got coined in 1950’s but it remained largely confined to academia then. Even as on date disappointingly machine learning is still treated as complex subject and more accessible to developers only. The need of today is a simple & easily accessible machine learning cloud based services i.e. “Machine Learning as a ServiceMLaaS” for every individual and business at low cost.

AILabPage defines machine learning in one simple line as

A focal point where business, data, emerging technologies meets experience, and decides to work together.

The primary goal of machine learning (ml) is to build automated data model for analytical reasons. The objective behind the goal is to build a system that learns from the data based on applied algorithm. The output can be obtained by mapping output to input or detecting patterns / structure or learning by reward/punishment method.

For more details on time lines & history of machine learning, please refer to the post below.

The whole game revolve around building data models for analytical reasons and to find / see, what human eyes cant see in its state then.


Types of Machine Learning

Machine Learning is classified into four categories at high level depending on the nature of the learning and learning system.  Some how I find difficult to accept Semi-supervised learning as a separate class . Type of machine learnings are as below.

  1. Supervised learning: Supervised learning gets labelled inputs and their desired outputs. The goal is to learn a general rule to map inputs to the output.
  2. Unsupervised learning: Machine gets inputs without desired outputs, the goal is to find structure or hidden patterns for useful information in inputs.
  3. Reinforcement learning: In this algorithm interacts with a dynamic environment, and it must perform a certain goal without guide or teacher.
  4. Semi-supervised Learning: This type of ml i.e. semi-supervised algorithms are the best candidates for the model building in the absence of labels for some data. So if data is mix of labels and non-labels then this can be the answer. Typically a small amount of labeled data with a large amount of unlabelled data is used here.

Machine Learning (ML) - Everything You Need To Know

The approach of developing ML model is to learn from data inputs with idea as “What has happened”. Evaluating and optimising different model results remains focus here. As on date Machine Learning is widely used in data analytics as a method to develop algorithms for making predictions on data as output. It has high dependency on probability, statistics, and linear algebra.


Difference Between AI and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are commonly used interchangeably.  Both the words do not mean the same thing although they are related. Machine Learning is one of the ways/method to achieve AI / near AI. As mentioned before AI is exceptionally wide umbrella kind of scenario.

Slide-6- Data Analytics BasicsAI is one of the biggest transformation measure of technology in todays time. What we know machine learning is only one of item in this AI bundle. We use these technologies knowingly or unknowingly on almost daily basis. Example are many for this including intelligent assistants making us more productive. AI definition is still not fixed or stable its like a moving target and keep changing / evolving. As we achieve little more advancement we change the definition.  Surprisingly today’s AI might become calculator technology of past, remember what people use say when calculator came out for mathematical calculation.

It is very important to understand the difference between the two words and they necessarily do not solve the same problem. Depending upon the problem we are trying to solve we can decide to use ML or some other technique out of AI bundle. Machine Learning is technique out of AI bundle to achieve artificial intelligence. Lets just say AI generally defines & demonstrate creativity in the following traits:

  • Planning & Predicting
  • Learning & Adopting
  • Reasoning & Logic Building
  • Problem Solving & Avoiding
  • Knowledge Representation
  • Perception & Reasoning
  • Motion Detection and Manipulation

Machine Learning helps to implement all those above by using correct methods and algorithms with correct data sets. In earlier times it was believed that human intelligence can be precisely described, and machines can simulate it with AI. Before machine start attempting simulation it needs to do learning with los of data.

The real fact is artificial intelligence is more about people, not just machines. Technology and non technology companies are now investing and brining out the real and materialistic values of Artificial Intelligence to the real world. Machine Learning is (mostly) a mathematics specific AI technique for classification, regression and clustering.

Now AI has started delivering values already. Using the contemporary view of computing exemplified by recent models and results from non-uniform complexity theory has proven the fact.


Algorithms in Machine Learning

Algorithms in Machine Learning (ML) borrows principles from computer science. There are many algorithms for same purpose because no single algorithm can work best for every problem,

  • How does youtube suggest you videos ?
  • Whether an email is a spam or not ?
  • Sentiment analytics to tell if a person is happy or sad ?
  • Which face has what name;  How facebook knows ?
  • How google map is able find best & fastest route between office and home?

All above question has one common answer “Algorithms”. Different problem will have different algorithm (solution). ML instructs an algorithm to learn for itself by analysing data. The more data any algorithm processes, the smarter it become. Some of the popular machine learning algorithms are as below.

  • Linear Regression – Simple Linear Regression.
  •  Logistic Regression – Outcome variable is binary or dichotomous.
  • Linear Discriminant Analysis –  A generalization of Fisher’s linear discriminant.
  • Classification and Regression  Tree – Algorithm for predictive modeling
  • Naive Bayes’A family of simple probabilistic classifiers.
  •  K-Nearest Neighbours – Predict the numerical target based on a similarity measure.
  • Learning Vector Quantization-  Representation of large amounts of data by (few) prototype vectors.
  • Support Vector Machines- Employed for both classification and regression purposes.
  • Bagging and Random Forest- Takes a bunch of bootstrapped samples of from original dataset.
  • Boosting and AdaBoost-  Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias.
  • Qlearning is a model-free reinforcement learning technique.

Off-course each algorithm is used in correct environment and usually don’t change the parent i.e type of algorithm. Refer to post “Machine Learning Algorithms for more details on machine learning algorithms and their use. Machine Learning entirely depend upon algorithms of two kinds i.e. Learning style and Symmetry & similarity.


Machine Learning Process

The process starts from data collections, cleaning and then training. Once training is complete by the data model it now needs to be tested with remaining data lying unused. The performance metrics show case any gauge with real world. This process can be iterative and in each iteration performance parameters can be fine tuned.

ML instructs an algorithm to learn for itself by analysing data. Algorithms here learn a mapping of input to output, detection of patterns or by reward. The more data it processes, the smarter the algorithm gets.

In other words Machine learning algorithms “learns” from the observations. When exposed to more observations, the algorithm improves its predictive performance. The most basic rule and meaningful point to understand anything about machine learning is, quality of data it gets served with. So this means its purely driven by data sets to provide answers. It follows GIGO method. The set of algorithms are as important as underlying data sets.


Statistics and Machine Learning

The summation of Data, Mathematics, Statistics, Algorithms and Computing power can be called as Machine Learning. It can be equate to magic but can be compared with futurology, which is the study of postulating possible, probable, and preferable futures. The world views and myths that underlie machine learning.

We have classic example from linear regression (LR) which was developed in the field of statistics. Linear regression is studied as a model for understanding the relationship between input and output numerical variables. Now it has been borrowed by machine learning algorithms. It is both a statistical algorithm and a machine learning algorithm.

There are some methodological differences between machine learning and statistics but those really don’t divorce them. The difference between the two is that machine learning emphasizes optimization and performance, while other is concerned about sample, population and hypothesis. Machine learning is more concerned with making predictions, even if the prediction can not be explained well.

How to Lie with Statistics is a book written by Darrell Huff in 1954 presenting an introduction to statistics for the general reader. He was not a statistician, but was a journalist who wrote many “how to” articles as a freelancer.

It is not a magic, using familiar tools such as MS Excel, Python, R and machine learning cloud services from Azure and Amazon web services can clear this perception. This is a super cool and simplest subject you can ever encounter nothing to remember by heart.


Some of the uses for Machine Learning

Supervised Learning is becoming a good friend for marketing business in particular. For example how much money will we make by spending more dollars on digital advertising? Or even making small predictions for stock markets i.e. What’s going to happen to the stock market tomorrow?

ML Uses Cases.png

Machine learning techniques are accelerating almost on a daily basis with intentions to bring good values to the businesses of today. It is revolutionising the way we do our business and what should be done to improve upon. ML develop its own encompassing strategy from the experience it come across  over the period of time.

As sir Andrew Ng says AI is a new form of electricity so in my opinion ML is no different; This has been one of the most active and rewarding areas of research due to its widespread use in many areas. Some of major break through in ML includes Natural Language Processing, and DeepLearning (GAN’s the most recent one).


Points to Note:

All credits if any remains on the original contributor only. We have covered all basics around Machine Learning. Machine Learning is all about data, computing power and algorithms to look for information. In the previous post we covered Generative Adversarial Networks.A family of artificial neural networks.


Books + Other readings Referred

  • Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
  • Lab and hands on experience of  @AILabPage (Self taught learners group) members.
  • Our new member of AILabPage – Navdeep Kapur has contributed to this blog post.

Feedback & Further Question

Do you have any questions about Supervised Learning or Machine Learning ? Leave a comment or ask your question via email . Will try my best to answer it.


Machine Learning (ML) - Everything You Need To KnowConclusion –  I particularly think that getting to know the types of machine learning algorithm actually helps to see somewhat clear picture. The answer to the question “What machine learning algorithm should I use?” is always “It depends.” It depends on the size, quality, and nature of the data. Also what is the objective / motive data torturing. As more we torture data more useful information comes out. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. To us at AILabPage  we say machine learning is crystal clear and ice cream eating job. It is not only for PhDs  aspirants but its for you, us and every one.


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