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Big Data/ Data Science/ML Foundation in Kuala Lumpur

We offer multiple courses on Data Science. The 3-day Big Foundation course, 3-day Data cleaning advanced course, 3-day Machine Learning advanced course and 3-day Artificial Intelligence Neural Networks advanced course. Programming pre-requisite in the foundation course is optional. For participants with no programming background in Python, you will start with the 3-day Big Data/Data Science/ML Foundation course. This course will teach you Python programming for analytics. For all other data science courses except the foundation course, Python programming experience is required.

All our data science related courses are taught by working practitioners, not academicians. The goal is to get you well versed in applying techniques to solve real world problems in the most efficient manner.

3-days Foundation Course 3-days Data Cleaning 3-days Machine Learning 3-days Al-Neural Networks Download AI and Machine learning

 
 

Big Data/ Data Science/ML Foundation in Kuala Lumpur

The Big Data/Data Science/ML Foundation course in Kuala Lumpur teaches you the basic skills and expertise needed to dissect large volumes of data leading, detect patterns and enable intelligent decision-making. The foundation course is non-technical and is open to managers, professionals and decision makers.

One day 1, we cover the basics of Big data, on day 2, we cover data science aspects and on day 3, we cover machine learning basics

Participants will get practical knowledge of Data Acquisition, Data Cleaning, Data Analysis, Data Visualization and Machine Learning. The course covers business, computer science and math and provides insights into successful data science projects.

This course has been designed from ground up to cater to people with no prior coding experience. Participants will be introduced into the world of Python programming through easy to grasp exercises. Our instructors will work with individuals one to one throughout the class to ensure each participant grasps the fundamentals.

After completion of the 3 days classroom training, participants can take up our online tutorials that covers advanced topics. No additional charges for the online tutorials . The online tutorials includes watching a video and completing an exercise. Instructors will then provide feedback on the completed exercises. Instructor support is available for 6 months after classroom training.

Big Data is a process to deliver decision-making insights. The process uses people and technology to quickly analyze large amounts of data of different types (traditional table structured data and unstructured data, such as pictures, video, email, transaction data, and social media interactions) from a variety of sources to produce a stream of actionable knowledge. Organizations increasingly need to analyze information to make decisions for achieving greater efficiency, profits, and productivity.

As relational databases have grown in size to satisfy these requirements, organizations have also looked at other technologies for storing vast amounts of information. These new systems are often referred to under the umbrella term “Big Data.” Gartner has identified three key characteristics for big data: Volume, Velocity, and Variety. Traditional structured systems are efficient at dealing with high volumes and velocity of data; however, traditional systems are not the most efficient solution for handling a variety of unstructured data sources or semi structured data sources.

Big Data solutions can enable the processing of many different types of formats beyond traditional transactional systems. Definitions for Volume, Velocity, and Variety vary, but most big data definitions are concerned with amounts of information that are too difficult for traditional systems to handle—either the volume is too much, the velocity is too fast, or the variety is too complex.


 

iKompass Big Data/Data Science/ML Course Sample Content

 
 
 
 

Big Data/ Data Science/ML Foundation

3 days Classroom Training

Our Big Data/ Data Science/ML Foundation course is a good place to start in case you do not have any experience with Big Data or data science. It provides information on the best practices in devising a Big Data/data science/ML solution for your organization. The course teaches you the basic skills and expertise needed to dissect large volumes of data leading to intelligent decision-making.

Course features:

  • 3 days classroom training
  • Business and manager focused
  • 6 months of online learning with weekly assignments and feedback
  • Post course Video tutorials with support
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Classroom Training Outline

 
CCC Big Data Foundation

Big Data/ Data Science/ML Foundation Course Outline

DAY 1 TIME TOPIC DELIVERY DISCRIPTION TOOLS
9:00 - 10:00 Machine Learning Lifecycle Theory Training and testing data. The machine learning life cycle is the cyclical process that data science projects follow. It defines each step that an organization needs to take in order to take advantage of machine learning and artificial intelligence (AI) to derive practical business value.
10:00 - 10:30 BI Versus Data Science Theory Business intelligence is the use of data to help make business decisions. Data analytics is a data science. If business intelligence is the decision making phase, then data analytics is the process of asking questions.
10:30 - 10:45 Tea break
10:45 - 12:00 Big Data Characteristics Theory Volume, Velocity, Value
12:00 - 13:00 Lunch
13:00 - 14:00 Python Functional Programming Practical Lists, Dictionary, Strings, Tuples, Functions. Python is a general - purpose programming language that is becoming more and more popular for doing data science. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. Python
14:00 - 14:45 Data Science tools Practical With over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python and R data science and machine learning on Linux, Windows, and Mac OS X. It's the industry standard for developing, testing, and training on a single machine. Anaconda
14:45 -15:00 Tea break
15:00 - 17:00 Python Data Structures Practical The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter Notebook
 
DAY 2 TIME TOPIC DELIVERY DISCRIPTION TOOLS
9:00 - 10:00 Big Data Engineering Theory Clusters Hadoop
10:00 - 10:45 Distributed Databases Theory NoSQL. NoSQL encompasses a wide variety of different database technologies that were developed in response to the demands presented in building modern applications MongoDB
10:30 - 10:45 Tea break
10:45 - 11:15 Distributed Processing Theory Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Spark
11:15 - 12:00 Data Lakes Theory A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Hadoop or S3
12:00 - 13:00 Lunch
13:00 - 14:00 NumPy Practical Mathematical Operations on matrices
14:00 - 14:45 Data Acquisition Practical Data Collection. API stands for Application Programming Interface. An API is a software intermediary that allows two applications to talk to each other. In other words, an API is the messenger that delivers your request to the provider that you're requesting it from and then delivers the response back to you. Beautiful Soup. API’s
14:00 - 14:45 Data Cleaning Practical Wrangling. Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas
14:45 -15:00 Tea break
15:00 - 17:00 Data Visualization Practical Charts. Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software. Seaborn, Bokeh
 
DAY 3 TIME TOPIC DELIVERY DISCRIPTION TOOLS
9:00 - 10:45 Machine Learning Algorithms Practical Supervised. Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language.It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means. Scikit Learn
10:30 - 10:45 Tea break
10:45 - 11:00 Linear Regression Practical In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). Regressor
11:00 - 11:30 K Nearest Neighbors Practical In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression Classifer
11:30 - 12:00 Naïve Bayes Practical In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Classifer
12:00 - 13:00 Lunch
13:00 - 17:00 Data Science project Practical In this project, we are going to use a very simple Titanic passenger survival dataset to show you how to start and finish a simple data science project using Python and Pandas; from exploratory data analysis, to feature selection and feature engineering, to model building and evaluation. Pandas

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Master Nodes

Job Tracker
 
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Data Cleaning

Our 3 days data cleaning course teaches you techniques to scrub or process big data with the goal of making it ready for building models. Most algorithms require data that is cleaned and normalized. Data scientists typically end of spending more than 70% of their effort in data cleaning/wrangling. Knowledge of techniques to work with with unstructured data is essential in data science.

Real-world data is frequently dirty and unstructured, and must be reworked before it is usable. Data may contain errors, have duplicate entries, exist in the wrong format, or be inconsistent. The process of addressing these types of issues is called data cleaning. Data cleaning is also referred to as data wrangling, massaging, reshaping , or munging. Data merging, where data from multiple sources is combined, is often considered to be a data cleaning activity.

We need to clean data because any analysis based on inaccurate data can produce misleading results. We want to ensure that the data we work with is quality data

Data quality involves:

  • Validity: Ensuring that the data possesses the correct form or structure
  • Accuracy:The values within the data are truly representative of the dataset
  • Completeness:There are no missing elements
  • Consistency: Changes to data are in sync
  • Uniformity: The same units of measurement are used

There are several techniques and tools used to clean data. We will examine the following approaches: Handling different types of data

  • Cleaning and manipulating text data
  • Filling in missing data
  • Validating data

We will be using Python libraries. These libraries often are more expressive and efficient. However, there are times when using a simple string function is more than adequate to address the problem. Showing complimentary techniques will improve the student’s skill set.

The basic text based tasks include:

  • Data transformation
  • Data imputation (handling missing data)
  • Subsetting data
  • Sorting data
  • Validating data

Learning Objectives

After completing this course, you should have the skills and be familiar with the following topic

  • Handling various kind of data importing scenarios that is importing various kind of datasets (.csv, .txt), different kind of delimiters (comma, tab, pipe), and different methods (read_csv, read_table)
  • Getting basic information, such as dimensions, column names, and statistics summary
  • Getting basic data cleaning done that is removing NAs and blank spaces, imputing values to missing data points, changing a variable type, and so on
  • Creating dummy variables in various scenarios to aid modelling
  • Generating plots like scatter plots, bar charts, histograms, box plots, and so on

Who should attend

Data Analysts, Data Engineers, Data Science Enthusiasts, Business Analysts, Project Managers

Prerequisite

Foundational certificate in Big Data/Data Science This course is meant for anyone who are comfortable developing applications in Python, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Python programming language will also find this course to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing python stack, this course is for you

Delivery Method

Mix of Instructor-led, case study driven and hands-on for select phases

H/w, S/w Reqd

Python, Pandas, Numpy, , Spark, Elasticsearch, MongoDbSystem with at least 8GB RAM and a Windows /Ubuntu/Mac OS X operating system

Tools covered

  • Pandas
  • Numpy
  • MongoDb
  • Apache Spark
  • Elasticsearch
  • Kafka
  • Jupyter notebook
  • Ipython
  • EC2
  • S3
 

Data Cleaning – Working with Data Lakes

DataLake

Data Cleaning – Process

DataProcess

Data Cleaning Training Roadmap

TRAINING_ROAD_MAP_ITPACS

Data

Machine Learning course in Kuala Lumpur

Our 3 days machine learning course in kuala lumpur teaches you various algorithms to build models. The course predominantly covers supervised algorithms.

Machine Learning is a name that is gaining popularity as an umbrella for methods that have been studied and developed for many decades in different scientific communities and under different names, such as Statistical Learning, Statistical Signal Processing, Pattern Recognition, Adaptive Signal Processing, Image Processing and Analysis, System Identification and Control, Data Mining and Information Retrieval, Computer Vision, and Computational Learning. The name “Machine Learning” indicates what all these disciplines have in common, that is, to learn from data, and then make predictions. What one tries to learn from data is their underlying structure and regularities, via the development of a model, which can then be used to provide predictions.

The goal of this course is to approach the machine learning discipline in a unifying context, by presenting the major paths and approaches that have been followed over the years, without giving preference to a specific one.

This course is an introduction to the world of machine learning, a topic that is becoming more and more important, not only for IT professionals and analysts but also for all those scientists and engineers who want to exploit the enormous power of techniques such as predictive analysis, classification, clustering and natural language processing.

Learning Objectives

After completing this course, you should have the skills and be familiar with the following topics

  • Apply mathematical concepts regarding the most common machine learning problems, including the concept of learnability and some elements of information theory.
  • Explain the process of Machine Learning
  • Describe the most important techniques used to preprocess a dataset, select the most informative features, and reduce the original dimensionality.
  • Describe the structure of a continuous linear model, focusing on the linear regression algorithm. Explain Ridge, Lasso, and ElasticNet optimizations, and other advanced techniques.
  • Describe the concept of linear classification, focusing on logistic regression and stochastic gradient descent algorithms.
  • Describe the concept of classification algorithms including Decision Trees, Support Vector Machines, Random Forests, Naive Bayes and K Nearest Neighbors
  • Demonstrate knowledge of evaluation metrics

Who should attend

Data Analysts, Data Engineers, Data Science Enthusiasts, Business Analysts, Project Managers

Prerequisite

Foundational certificate in Big Data/Data Science This course is meant for anyone who are comfortable developing applications in Python, and now want to enter the world of data science or wish to build intelligent applications. Aspiring data scientists with some understanding of the Python programming language will also find this course to be very helpful. If you are willing to build efficient data science applications and bring them in the enterprise environment without changing your existing python stack, this course is for you

Delivery Method

Mix of Instructor-led, case study driven and hands-on for select phases

H/w, S/w Reqd

Python, Pandas, Numpy, System with at least 8GB RAM and a Windows /Ubuntu/Mac OS X operating system

Duration

3 days

 

Sample concepts covered as part of the Machine Learning course in Kuala Lumpur

The course will cover in detail both the mathematical aspects as well as the business application aspect of algorithms

Training data and test data

The observations in the training set comprise the experience that the algorithm uses to learn. In supervised learning problems, each observation consists of an observed response variable and one or more observed explanatory variables. The test set is a similar collection of observations that is used to evaluate the performance of the model using some performance metric. It is important that no observations from the training set are included in the test set.

training_test_accuracy

Memorizing the training set is called over-fitting. A program that memorizes its observations may not perform its task well, as it could memorize relations and structures that are noise or coincidence. Balancing memorization and generalization, or over-fitting and under-fitting, is a problem common to many machine learning algorithms. In this course we will discuss regularization, which can be applied to many models to reduce over-fitting.

Random Forests – Ensemble Voting

Ensembling by voting can be used efficiently for classification problems. We now have a set of classifiers, and we need to use them to predict the class of an unknown case. The combining of the predictions of the classifiers can proceed in multiple ways. The two options that we will consider are majority voting, and weighted voting. Ideas related to voting will be illustrated through an ensemble based on the homogeneous base learners of decision trees, as used in the development of bagging and random forests.

Voting

Bias Variance Trade-off

Many metrics can be used to measure whether or not a program is learning to perform its task more effectively. For supervised learning problems, many performance metrics measure the amount of prediction error. There are two fundamental causes of prediction error: a model’s bias, and its variance. Assume that you have many training sets that are all unique, but equally representative of the population.

A model with high bias will produce similar errors for an input regardless of the training set it used to learn; the model biases its own assumptions about the real relationship over the relationship demonstrated in the training data. A model with high variance, conversely, will produce different errors for an input depending on the training set that it used to learn. A model with high bias is inflexible, but a model with high variance may be so flexible that it models the noise in the training set. That is, a model with high variance over-fits the training data, while a model with high bias under-fits the training data. It can be helpful to visualize bias and variance as darts thrown at a dartboard.

Darts

Decision Trees

Decision trees are one of the simplest techniques for classification. They can be compared with a game of 20 questions, where each node in the tree is either a leaf node or a question node. Decision tree learning is a predictive machine learning technique that uses decision trees. Decision trees make use of decision analysis and predicts the value of the target. Decision trees are simple implementations of classification problems and popular in operations research. Decisions are made by the output value predicted by the conditional variable.

Decision_tree

Entropy

In statistics, entropy is the measure of the unpredictability of the information contained within a distribution. The entropy technique takes cues from information theory. The premise is that more homogeneous or pure nodes require less information to be represented.

Entropy_Graphic

Support Vector Machines

Support vector machines (SVMs) are supervised learning methods that analyze data and recognize patterns. SVMs are primarily used for classification, regression analysis, and novelty detection. Given a set of training data in a two-class learning task, an SVM training algorithm constructs a model or classification function that assigns new observations to one of the two classes on either side of a hyperplane, making it a nonprobabilistic binary linear classifier

linear_separability

Hyperplane

A support vector machine (SVM) is a supervised machine learning model that works by identifying a hyperplane between represented data. The data can be represented in a multidimensional space. Thus, SVMs are widely used in classification models. In an SVM, the hyperplane that best separates the different classes will be used.

geometric

Need for Applied Machine Learning

Graph

Source of Data for Machine Learning




There is obvious visible information, which one is conscious of and there is information that comes off you. Example, from your phone one can determine which website you visited, who you called, who your friends are, what apps you use. Data science takes it further to reveal how close you are to someone, are you an introvert or an extrovert, when during the day are you most productive, how often do you crave for ice cream, what genre of movies you like, what aspects of social issues interest you the most etc.,

Sensors everywhere

With the possibility of adding sensors to everything, now there is deeper insight into what is going on inside your body. Spending 10 minutes with a doctor who gives you a diagnosis based on stated or observed symptom is less useful than a system that has data about everything going on inside your body. Your health diagnosis is likely to be more accurate with analysis of data collected through devices such as fitbits and implantables.

The amount of data available with wearables and other devices provides for rich insight about how you live, work with others and have fun.

Digital Breadcrumbs

Big Data and analytics is made possible due to the digital breadcrumbs we leave. Digital breadcrumbs include things like location data, browsing habits, information from health apps, credit card transactions etc.,

The data lets us create mathematical models of how people interact, what motivates us, what influences our decision making process and how we learn from each other.

Big Data versus Information

One can think of Big Data as the raw data available in sufficient volume, variety and velocity. Volumes here refer to terabytes of data. Variety refers to the different dimensions of data. Velocity refers to the rate of change.

A bank can use credit card information to develop models that’s more predictive about future credit behavior. This provides better financial access. What you purchased, frequency of purchase, how often do you pay back, where do you spend money are better predictors of payment credibility than a simple one dimensional credit score.


Machine Learning Process

FAQs


Foundation Course: Data Science is a combination of business, technical and statistical worlds. We will be covering the foundational aspects of all three in class. As such, we don’t require participants to have a background in all three. Background in any one of the three will be sufficient. We will teach Python functional programming in class along with how to use the data science libraries for data acquisition, visualisation, machine learning.
No. The optional technical modules don’t have additional costs. However, to work through the optional technical modules, you need to have a background in either statistics or programming.
Foundation: ITPACS Data Cleaning: ITPACS Certified Associate in Data Science – Data Cleaning Machine Learning: ITPACS Certified Associate in Data Science – Machine Learning
The course does not have an academic minimum requirement. However, you need to be familiar with basic data analysis and have an understanding of school/ college statistics.
The difficulty level of the concepts depends on your background. If your job involves analyzing trends from data, you are likely to find the course easy. Before joining the class, we will send you some data and you need to send us some insights about the data. Your insights about the data will determine if you will be able to get the most value from attending the class.
Technology is one part of the data science world. The course covers business, statistical and technology. For example, the business side of the course covers figuring out the factors that influence sales. The statistical aspects involves uncovering the correlation between various factors that affect sales. The technology aspect involves writing code to elicit predictions. We spend about 2 hours at the end of the day in writing code in Python for those interested in the programming aspects.
Foundation: No. This is a 3 day introductory course. Data science is an extensive field and can take years to be an expert. Many data scientists specialize in one particular domain. This course provides you with an overview of what is involved in data science.
Foundation: The course covers the theoretical aspects of a Big Data Solution. The technical aspects of building a big data solution is not covered because there are so many different architectures and technologies. Data Cleaning: Yes, we will cover Spark, EC2, Kafka and MongoDB Machine Learning: Yes, we will cover Spark, EC2, Kafka and MongoDB
Most of the participants are managers in companies across different industries who are evaluating opportunities for using analytics to make decisions. These managers are either exploring the application of data science within their own domain or are already working with data scientists and analysts. Upon completion of the course, these managers are in a better position to drive data science projects in their context.  Most of these managers represent the business side of data science.
Gartner said there would be a shortage of 100,000 data scientists (US) by 2020. McKinsey put the national gap (US) in data scientists and others with deep analytical expertise at 140,000 to 190,000 people by 2017, resulting in demand that’s 60 percent greater than supply.
Accenture found that more than 90 percent of its clients planned to hire people with data science expertise, but more than 40 percent cited a lack of talent as the number one problem.
Big Data/Data Science Foundation Course: We offer a pass guarantee for this exam. In case a participant fails the exam, they have two more attempts to clear the exam at no additional cost. The objective of the foundation course is to facilitate entry into the data science field for people with no analytics background. As such, the exam itself is not difficult. The exam does not have any coding. In the unlikely scenario wherein the participant fails the third time, we will refund the full course fees.
Yes. If you are currently in-between jobs, we provide additional discount on the course fees. During registration, let us know about your situation and we will accommodate additional discount.
Recent studies in neuroscience demonstrate that we can change our brain just by thinking. Our concept of "self" is etched in the living latticework of our 100 billion brain cells and their connections. Picking up new skills is about making new connections in the mind. By the time you complete the course, you have changed your brain permanently. If you learned even one bit of information, tiny brain cells have made new connections between them, and who you are is altered. The act of mental stimulation through learning is a powerful way you can grow and mold new circuits in your brain. Growing new circuits is vital to growth and state of being.
There is a small chance that you may be in what a growing body of knowledge point to as "survival mode". When we live in survival, we limit our growth, because the chemicals of stress will always drive our big-thinking brain to act equal to its chemical substrates. Chronic long-term stress weakens our bodies. We choose to remain in the same circumstances because we have become addicted to the emotional state they produce and the chemicals that arouse that state of being. Far too many of us remain in situations that make us unhappy, feeling as if we have no choice but to be in stress. We choose to live stuck in a particular mindset and attitude, partly because of genetics and partly because a portion of the brain (a portion that has become hardwired by our repeated thoughts and reactions) limits our vision of what’s possible. We can change (and thus, evolve) our brain, so that we no longer fall into those repetitive, habitual, and unhealthy reactions that are produced as a result of our genetic inheritance and our past experiences. Scientists call this neuroplasticity—the ability to rewire and create new neural circuits at any age—to make substantial changes in the quality of your life. Learning a new skill allows new experiences and in turn fires new circuits related to curiosity, creativity etc,
The brain is structured, both macroscopically and microscopically, to absorb and engage novel information, and then store it as routine. When we no longer learn new things or we stop changing old habits, we are left only with living in routine. When we stop upgrading the brain with new information, it becomes hardwired, riddled with automatic programs of behavior that no longer support a healthy state of being. If you are not learning anything new, your brain is constantly firing the same old neurons related to negative states such anxiety, stress and worry. We are marvels of flexibility, adaptability, and a neuroplasticity that allows us to reformulate and repattern our neural connections and produce the kinds of behaviors that we want.
Research is beginning to verify that the brain is not as hardwired as we once thought. We now know that any of us, at any age, can gain new knowledge, process it in the brain, and formulate new thoughts, and that this process will leave new footprints in the brain—that is, new synaptic connections develop. That’s what learning is. In addition to knowledge, the brain also records every new experience. When we experience something, our sensory pathways transmit enormous amounts of information to the brain regarding what we are seeing, smelling, tasting, hearing, and feeling. In response, neurons in the brain organize themselves into networks of connections that reflect the experience. feelings. Every new occurrence produces a feeling, and our feelings help us remember an experience. The process of forming memories is what sustains those new neural connections on a more long-term basis. Memory, then, is simply a process of maintaining new synaptic connections that we form via learning irrespective of age. The reality is that if you are not making new neural connections, the brain cells are decaying or firing the same old routine patterns. This leads to physically aging faster than usual and other health problems. Contrary to the myth of the hardwired brain, we now realize that the brain changes in response to every experience, every new thought, and every new thing we learn. This is called plasticity. Researchers are compiling evidence that the brain has the potential to be moldable and pliable at any age.
AI has two sides. Research and application. Research is about in depth knowledge of how something works. You could spend years in research to find out how electricity and waves works and finally create a microwave. Consumers then use these microwaves to cook various food. A consumer doesn't need to have extensive knowledge on the inner working of a microwave. They can get creative about the end result of using the microwave. This is the application side of things. Currently, as a result of extensive research, there is plethora of microwaves in the market. Attending a university courses is like creating another microwave, reinventing the wheel. You would rather focus your effort on the application side of AI. Take the already built algorithms and use it for your use cases. The way we teach our course is to apply these algorithms to solves business problems rather than go in-depth into calculus, matrices and trigonometry that make up an algorithm.

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