MS in Machine Learning - Types of Machine Learning

Machine learning is regarded as an application of artificial intelligence (AI) intelligence (AI) which provides a system with an ability to learn automatically and improve from experience without any programming.

Machine learning usually talks about the development of computer programs to access data and later use it to learn for themselves.

The entire process of learning starts with observations of data, which include examples, instruction, direct experience so that data patterns can be figured out and better decisions can be taken in the future based on the samples provided.

The main aim is to allow the systems to learn automatically without any human intervention or guidance and adjust the actions accordingly.

Artificial intelligence and machine learning are considered to be popular topics that have become the buzzword in any industry. Both these topics are often talked about when it comes to Big data, analytics, and any other broad technological change that is taking the world by storm.

Artificial intelligence can be considered as a more in-depth concept of machines being able to perform tasks in a way that is deemed to be smart.

Whereas, machine learning is a current application of AI revolving around the idea that data access should be given to machines and let machines take the responsibility of learning it all by themselves.

In machine learning, you can generate a program by integrating the input and output of the program.

Machine learning is regarded as an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without the need of being programmed explicitly. Machine learning stresses the development of computer programs that can access data and use it to learn for themselves.

The terms of machine learning, data science, and artificial intelligence fall in the same domain and are connected. But these terms have their meaning and specific applications.

Data science is a concept that is used to handle big data. It also includes data cleansing, preparation, and analysis. Whereas, machine learning is defined as the practice of using algorithms to use data, learn from it, and then forecast the future trends on that topic.

Data science is a broader term for multiple disciplines, and machine learning fits within data science.

Natural Language Processing is an area which is a perfect confluence of artificial intelligence and linguistics. This involves intelligent analysis of a written language. If you have a large volume of data that needs to get some insight, you need to use NLP techniques. These insights can be information extraction, information retrieval, sentiment analysis, search, and others.

Machine Learning is an area of artificial intelligence where a set of statistical techniques are used for problem-solving. Machine language techniques can be applied to solve NLP problems.

A hypothesis is a specific type of function that we believe is similar to the actual function, which is the target function that we want to model.

Entropy, when related to machine learning, is a measure of the randomness in the information that is being processed. It is difficult to draw any conclusion if the entropy is higher.

A machine learning model is a mathematical representation of a real-world process. To generate a machine learning model, you will need to provide training data to a machine-learning algorithm to learn.

If you need to generate a machine learning model, you need to provide training data such that the input parameters correspond to the target. The output of the training process is a machine learning model which you can then use to make predictions.

Predictive analytics comprises a variety of statistical techniques from data mining, predictive modeling, and machine learning, that analyzes current and historical statistics to make predictions or otherwise unknown events.

Predictive intelligence is a particular method of delivering experiences which is unique to every individual. This enables marketers to observe customer behavior, and with every action taken, build a profile of customer preferences.

Naïve Bayes Classifier Algorithm (Supervised Learning – Classification)

The Naïve Bayes classifier algorithm is based on Bayes’ theorem. It classifies every value independent of any other value. It allows us to predict a class or a particular category which is based on a given set of features, using probability.

This particular algorithm is used to build machine learning models, particularly for disease prediction and document classification.

If you have a moderate or extensive training dataset If the instances comprise of several attributes. The classification parameter, attributes which describe the instances should be conditionally independent.

In machine learning and statistics, classification is regarded as a supervised learning approach in which the computer program learns from the data input given to it. The program then uses this learning to classify new observation.

Regression and classification are categorized under supervised machine learning. The significant difference between regression and classification is that the output variable in the regression is numerical (or continuous) while in case of classification is categorical (or discrete).

There are four main types of machine learning:

Supervised Learning

This algorithm comprises an outcome variable which is to be predicted from a provided set of independent variables). By using these set of variables, a function is generated that maps an input to the desired output.

The training process goes on until the model achieves the desired level of accuracy on the training data. Examples of Supervised Learning are Regression, Decision Tree, Random Forest, KNN, Logistic Regression,

Unsupervised learning

This machine-learning algorithm studies data to identify patterns. The algorithm does not have any target or outcome variable to predict. Also, there is no answer key or human operator to provide any instruction. Instead, the machine determines the correlations and relationships by analyzing all the available data.

In an unsupervised learning process, the machine learning algorithm is left to interpret large data sets and address that data accordingly. The algorithm tries to organize that data in some way to describe its structure. This might mean grouping the data into clusters or arranging them in a way that looks more organized.

Semi-supervised learning

Semi-supervised learning is more or less similar to supervised learning. This algorithm uses both labeled and unlabelled data. Labeled data is typically the information that has meaningful tags so that the algorithm can understand the data, while unlabelled data lacks that information.

By using this combination, machine learning algorithms can learn to label the unlabelled data.

Reinforcement Learning

The machine is usually trained using this algorithm to make particular decisions. The machine then is exposed to an environment where it prepares itself continuously using trial and error method.

This machine usually learns from experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process.

A machine learning (ML) developer or an engineer is considered an expert who uses data on training models. The models are then used to automate specific processes like image classification, speech recognition, and market forecasting.

To become a machine learning developer, the skill sets that are required are as follows -

• Computer Science Fundamentals and Programming
• Data Modeling and Evaluation
• Probability and Statistics
• Applying Machine Learning Algorithms and Libraries
• Software Engineering and System Design
• Machine Learning Courses and Topics

What is the prerequisite for machine learning?

The main prerequisite for machine learning is data analysis. For beginning practitioners (i.e., hackers, coders, software engineers, and people working as data scientists in business and industry) you don’t need to know that much calculus, linear algebra, or other college-level math to get things done.

Machine learning unfolds from artificial intelligence, and it involves the study of pattern recognition. Nowadays, when vast amounts of data are being dealt with in our everyday lives, pattern recognition is something which helps large corporations and websites work accurately with the users. Artificial intelligence has become very popular with the consumers, esp. intelligent personal assistants like Apple’s Siri, Microsoft Cortana, etc.

Python language is considered to be one of the most flexible languages for machine learning. Python does comprise of individual libraries for machine learning, namely SciPy and NumPy, which are ideal for linear algebra and getting to know kernel methods of machine learning.

R is regarded as one of the most powerful machine learning platforms, which is used by the top data scientists in the world. R is regarded as one of the most used machine learning tool. When professional machine learning experts were surveyed in 2015, again, the most popular machine learning tool was R.

R offers an array of techniques like data analysis, sampling, visualization, supervised learning and model evaluation are provided in R.The academics that develop new algorithms are now emerging in R platform and then releasing as packages.

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being programmed. Machine learning gives importance to the development of Computer Programs that can change when exposed to new data.

Python is a popular open-source programming language. It is one of the most-used languages in artificial intelligence and other related scientific fields. Machine learning is the field of artificial intelligence that uses algorithms to learn from data and then make predictions.

Python does contain special libraries that can apply to machine learning, particularly SciPy and NumPy, which are ideal for linear algebra and also for getting acquainted with kernel methods of machine learning. The language is perfect to use when working with machine learning algorithms and has relatively easy syntax.

R is said to have a better community for data exploration and learning. R also has extensive visualization capabilities. Python, on the other hand, has become better at data handling because of the introduction of pandas. Learning and development time is very less in Python when compared to R being a low-level language.

Machine learning prioritizes predictive accuracy over model interpretability, while data analysis emphasizes interpretability and statistical inference. Python, being more concerned with predictive accuracy, has developed a positive reputation in machine learning. R, as a language for statistical inference, has made its name in data analysis. Python is always better for data manipulation and repeated tasks.

On the other hand, R is good for ad hoc analysis and exploring datasets. R has a steep learning curve, and people without programming experience find it overwhelming. Python is generally considered easy to pick up.

Python, R, MAT LAB have excellent packages for ML Algorithm and Visualization. The support of such packages is behind when it comes to JavaScript. JavaScript can be a functional language for machine learning when it allows using machine learning everywhere concretely, and this fact will become increasingly important soon.

Preliminary knowledge of math is required to learn about machine learning. The kind of math needed for math are as follows-

• Linear algebra
• Calculus
• Multivariate calculus
• Statistics
• Coding the matrix
• Probability and Statistics

Some of the famous thesis and research topics in Machine Learning are as follows:

• Supervised Machine Learning
• Machine Learning Algorithms
• Computer Vision
• Deep Learning
• Unsupervised Machine Learning
• Data Mining
• Neural Networks
• Bayesian Network
• Reinforcement Learning
• Predictive Learning

The Machine Learning Certification by Stanford University (Coursera) is the single highest-rated course on Machine Learning on the entire internet. Created by Andrew Ng, Co-Founder of Coursera and Professor at Stanford University, the program has been taken up by more than 1,678,000 students & professionals globally, who have given it an average rating of a whopping 4.9 out of 5.

The most renowned graduate schools for studying machine learning are as follows:

• Carnegie Mellon University
• Cornell
• University of Michigan Ann Arbor
• Berkeley
• Columbia University
• Stanford
• University of California San Diego
• University of Washington
• Georgia Tech
• University of Massachusetts Amherst

Some of the famous universities for data science are as follows:

• MS in Data Science, Columbia University
• MS in Data Science, New York University
• MS in Computational Data Science, Carnegie Mellon University
• MS in Data Science, Washington University

Georgia Tech

• Tuition fee- $27,872

Columbia University

• Tuition fee- $44,592

University of North Carolina Chapel Hill

• Tuition fee - $26,963

Carnegie Mellon University

• Tuition Fees- $47,470

Kaggle is considered to be a renowned platform for predictive modeling and analytics competition. Several companies and different researchers usually post data on this platform. Statisticians and data miners compete here to develop the best models for predicting and describing the data that is given.

This approach of crowdsourcing depends on the fact that there are many strategies which can be applied to any predictive modeling task. It is impossible to know at the outset the technique that will be most effective.

Kagglers usually come from varied backgrounds which include fields like computer science, biology, medicine, computer vision, and also fields like glaciology. It also includes many of the world’s reputed researchers. Many of these researchers usually publish papers in peer-reviewed journals, which is based on their performance in Kaggle competitions.

• Companies work on the data and a detailed description of the problem.
• Next, Kaggle frames the competition, anonymize the data, and integrates the winning model into their business operations.
• Participants then experiment with different techniques and compete against each other to produce the best models.
• The work done is shared publicly through Kaggle Scripts. This is done to achieve a better benchmark and to inspire new ideas.
• The submissions are usually made through scripts or a private manual upload.
• For most competitions, submissions are scored immediately. This is based on their predictive accuracy that is relative to a hidden solution file) and then is summarized on a live leaderboard.
• After the deadline is over, the host company pays the prize money for the solution that won the competition.
• Many companies recruit participants that are based on their place on the leaderboard, final score, and scripts that are submitted.
• Alongside its public competitions, Kaggle also provides private competitions which are limited to Kaggle’s top participants.

What does a machine learning engineer do?

Machine learning experts make use of existing algorithms and other statistical methods to figure out how one set of data is going to look. Algorithms are also used to classify existing big data sets (classification). An expert in machine learning picks what algorithm is to be used for a task.

Feature engineering is another essential part of the job. The raw data that is used cannot be directly fed to the algorithms. It is required to analyze the data, preprocess it, and also make dimensionality reduction before it is being fed to the algorithms.

The job role of a machine learning expert is very sophisticated. One needs to have exceptional mathematical skills to perform complex computations and also work with the algorithms involved in this type of programming.

Communication skills are also crucial for a machine learning expert. Machine learning engineers will need to explain their process to individuals who are not programming experts. Also, some positions require machine learning engineers to publish articles on their work that they are doing.

One needs to have strong analytical skills as the job role involves projecting outcomes and isolating issues which need to be resolved to make programs more effective.

The average pay for a Machine Learning Engineer is approximately $40.45 per hour. The average salary for a Machine Learning Engineer is roughly $111,448 per year.

Some of the best job prospects for a student of machine learning include:

• Machine Learning Engineer
• Data Architect
• Data Scientist
• Data Mining Specialists
• Cloud Architects
• Cyber Security Analysts

Machine Learning Engineer

A Machine Learning Engineer develops amazing algorithms to help decipher meaningful patterns from humongous amounts of data. Machine learning engineers should also be able to work with programming languages like Python, Java, Scala, C++, and JavaScript.

The person must also be capable of building distributed systems that are highly scalable and work in teams which focus on personalization.

Data Architect

Data Engineers are responsible for maintaining the big data ecosystem in an organization. They must have substantial in-depth knowledge about programming.

They also must be familiar with Hadoop, Hive, MapReduce, MySQL, MongoDB, NoSQL, Cassandra, SQL, Data streaming, and programming. Additionally, they must also be proficient in R, Python, Ruby, C++, Perl, Java, SAS, SPSS, and Matlab.

Data Analyst

The job role of data analysts involves familiar with data retrieval and storing systems, data visualization, and data warehousing using ETL tools, Hadoop-based analytics, and business intelligence concepts.

These persistent and passionate data miners usually have a strong background in Mathematics, Statistics, Machine Learning, and Programming.

Data scientist

Considered as one of the most popular profession, Data Scientists are experts in R, SAS, Python, SQL, MatLab, Hive, Pig, and Spark. They are proficient in Big Data technologies and analytical tools.