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 the implementation of these buzzwords in real business is missing big time. The real need is to clarify, demonstrate, extract real values and reap rewards. “Machine Learning” sounds like a gold mine to many businesses, especially for the companies which are actually data factories i.e. social media platforms.

Machine Learning involves feature selection and feature engineering and encompasses a variety of algorithmic techniques. Techniques like deep learning and neural network etc. The deeper it gets, it starts hinting at the simulation of the human brain though and might get complicated for some.

Machine Learning(ML) – Basic Terminologies in Context

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

What is Machine Learning?

Machine learning, the beating heart of the vibrant realms of artificial intelligence and computer science, pulses with the fervent desire to harness data and algorithms, mirroring the intricate dance of human learning, forever striving to refine its accuracy with each passing moment. Before we embark on our journey of defining machine learning, let’s understand it a bit. Machine Learning term got coined in the 1950s but it remained largely confined to academia then.

• Foundation Pillars of Machine Learning: The current state of machine learning is built upon five foundational pillars: data (including labels, knowledge, and information), storage, computing power (akin to the brain or neural network), algorithms (comprising logic and experience), and business revenue hunger – reflecting the pressing need for new businesses and revenue streams in today’s competitive landscape.

AILabPage defines Machine Learning in one simple line as below

A focal point where business, data, and emerging technologies meet experience and decide to work together.

Why ML is so good today; for this, there are a couple of reasons below but not limited to though.

• Big Data – The explosion and availability of big data
• Storage capacity – Availability of high-speed and huge-capacity storage
• Computing power – Development of extremely powerful machines with high memory & faster computing ability
• Algorithms – Advancements in machine learning algorithms
• Business Revenue Hunger – There is a huge hunger for new businesses and revenue streams in this business shrinking times

In recent decades, monumental strides in storage and processing capabilities have unfurled pathways to awe-inspiring innovations propelled by the raw emotion of machine learning. Think of the enchanting recommendation system of Netflix, weaving threads of connection between hearts, or the breathtaking evolution of autonomous vehicles, daring to dream beyond the limits of imagination.

• Machine Learning as a Passionate Guardian: Nestled within the embrace of data science, machine learning assumes a vital role, utilizing statistical methodologies to nurture algorithms.
• Tender Care of Algorithms: Through meticulous attention, machine learning algorithms are trained to categorize, predict, and unearth profound insights from vast data mines.
• Guiding Lights of Insights: These precious insights serve as guiding lights, illuminating decision-making processes across diverse landscapes with the energy of growth and possibility.
• The Cry for Skilled Data Scientists: As the realm of big data expands, the demand for skilled data scientists reverberates throughout industry valleys.
• Valiant Pursuers of Discovery: Driven by an unquenchable thirst for discovery, these valiant souls conquer business challenges and gather precious data jewels to shape a brighter future.

Even to date, disappointingly machine learning is still treated as a complex subject and more accessible to developers only.  For more details on timelines & history of machine learning, please refer to this post, The Exciting Evolution of Machine Learning

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

The need for today is a simple & easily accessible machine learning cloud-based service i.e. “MLaaS – Machine Learning as a Service” for every individual and business at a low cost. The whole game revolves around building data models for analytical reasons and to find/see, what human eyes can’t see in its state then.

Types of Machine Learning

Machine Learning is classified into four categories at a high level depending on the nature of the learning and learning system.  Somehow I find it difficult to accept Semi-supervised learning as a separate class. The type of machine learning are

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: The machine gets inputs without desired outputs, the goal is to find structure or hidden patterns for useful information in inputs. Extremely useful when we have a very large amount of unlabeled data.
3. Reinforcement learning: In this algorithm interacts with a dynamic environment, and it must perform a certain goal without a guide or teacher.
4. Semi-supervised Learning: This type of ml i.e. semi-supervised algorithms are the best candidates for model building in the absence of labels for some data. So if data is a mix of labels and non-labels then this can be the answer. Typically a small amount of labelled data with a large amount of unlabeled data is used here.

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

Machine Learning and AI

Artificial intelligence and machine learning are used interchangeably often but they are not the same. Machine learning is one of the most active areas and a way to achieve AI.

• AI & ML Advancements: In today’s landscape, AI & ML are pushing the boundaries of what machines can achieve, from making nuanced judgments to mastering complex games.
• Scarcity of Skilled Professionals: Despite these advancements, a critical challenge remains: the shortage of individuals capable of translating AI & ML theories into actionable business outcomes.
• Practical Expertise Needed: Mere academic knowledge isn’t sufficient; practical expertise in real-world scenarios is essential for success in AI & ML applications.
• Cultural Integration of AI & ML: To fully embrace AI & ML, organizations must prioritize education for all levels, including business teams, managers, and executives.
• Continuous Learning Opportunities: This educational journey involves ongoing sessions and tailored roadshows to equip professionals with the skills necessary to leverage AI & ML effectively in their respective domains.

In this era of boundless possibilities, fostering a collective understanding and appreciation for AI & ML isn’t just about adapting to technology—it’s about embracing a shared journey towards innovation and progress.

Demystifying The Jargons

Artificial Intelligence and Machine learning 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 ahead from the old-school thought of “AI is a bunch of if-else statements”.

• Understanding the Impact of Emerging Technologies: It’s crucial to grasp how new technologies and techniques will reshape our world, as those who fail to adapt may risk falling behind in the rapidly changing landscape.
• Invitation to Learn Together: Everyone is encouraged to embark on the learning journey at their own pace and convenience, as we explore the fascinating realm of machine learning together.
• Current State of Machine Learning: Presently, machine learning methods predominantly rely on supervised learning, indicating that there’s still considerable progress to be made in this field.
• Integration with Artificial Intelligence: Machine learning and artificial intelligence utilize these foundational pillars, particularly computing power, data, and algorithms, to drive innovation and advancement.
• Machine Learning as a Subset of AI: Machine learning stands as a technology/technique under the broader umbrella of AI, showcasing its significance in advancing technological capabilities and driving progress.

Make a note of the fact, that if you really want to learn and have a good, strong and commanding attitude over machine learning then you can’t run away from mathematics, probability, linear algebra, statistics, decision theory, algorithms flow and a bit of neuroscience understanding at very high.

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.

Difference Between AI and Machine Learning

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

The real fact is artificial intelligence is more about people, not just machines. Technology and non-technology companies are now investing and bringing 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.

Algorithms in Machine Learning

Algorithms in Machine Learning (ML) borrow principles from computer science. There are many algorithms for the 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 spam?
• Sentiment analytics to tell if a person is happy or sad?
• Which face has what name;  How facebook knows?
• How google map is able to find the best & fastest route between the office and home?

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

• Linear Regression – Simple Linear Regression.
•  Logistic Regression – The 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 from the original dataset.
• Boosting and AdaBoost-  Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias.
• Q–learning is a model-free reinforcement learning technique.

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

Machine Learning Process

The process starts with data collection, cleaning and then training. Once training is completed by the data model it now needs to be tested with remaining data lying unused.

• Performance Metrics and Iterative Refinement: Performance metrics provide insights into real-world effectiveness, with iterative processes allowing for continuous refinement of parameters to improve performance over successive iterations.
• Autonomous Learning via Data Analysis: Machine learning enables algorithms to autonomously learn by analyzing data, facilitating the understanding of input-output mappings and the identification of patterns or rewards, with algorithmic intelligence growing proportionally with the volume of processed data.
• Crucial Role of Data Quality: The quality of data served is pivotal in machine learning, determining the accuracy and reliability of the generated insights, as adherence to the “Garbage In, Garbage Out” (GIGO) principle underscores the significance of input data quality.
• Central Importance of Algorithms: The selection of appropriate algorithms is fundamental in machine learning, as they dictate the learning process and the generation of insights, with algorithmic efficacy crucial for achieving desired outcomes.
• Synergy Between Algorithms and Data Sets: Both algorithms and data sets are integral components of machine learning, with their synergy driving the effectiveness of the learning process and the accuracy of the outcomes produced.

In other words, Machine learning algorithms “learn” from the observations. When exposed to more observations, the algorithm improves its predictive performance.

Statistics and Machine Learning

The summation of Data, Mathematics, Statistics, Algorithms and Computing power can be called Machine Learning. It can be equated 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.

• Linear Regression in Machine Learning: Linear regression, originally a statistical model, has been adopted by machine learning algorithms as a means of understanding the relationship between input and output numerical variables, serving as both a statistical and machine learning algorithm.
• Methodological Differences between Machine Learning and Statistics: While there are methodological distinctions between machine learning and statistics, they are not mutually exclusive; machine learning focuses on optimization and performance, whereas statistics may prioritize considerations such as sample, population, and hypothesis testing.
• The Influence of Darrell Huff’s Work: “How to Lie with Statistics,” authored by Darrell Huff in 1954, offers a non-statistician’s introduction to statistics for the general reader, demonstrating the significant impact of individuals from diverse backgrounds, such as journalists, in shaping the discourse around statistical concepts.

It is not 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 businesses 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?

Machine learning techniques are accelerating almost daily to bring good value to the businesses of today. It is revolutionizing the way we do our business and what should be done to improve upon it. ML develops its own encompassing strategy from the experience it comes across over the period.

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 the major breakthroughs in ML include Natural Language Processing, and DeepLearning (GAN’s the most recent one).

Conclusion –  I particularly think that getting to know the types of machine learning algorithms actually helps to see a 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 task. It is not only for PhDs aspirants but it’s for you, us and everyone.

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Points to Note:

All credits if any remain on the original contributor only. We have covered all basics of 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.

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