Transformational advancements in technology in today’s world are making it possible for data scientists to develop machines that think for themselves. Based on complex algorithms that can glean information from data, today’s computers can use neural networks to mimic human brains, and make informed decisions based on the most likely scenarios. The immense possibilities that machine learning can unlock are fascinating, and with data exploding across all fields, it appears that in the near future Machine Learning will be the only viable alternative simply because there is nothing quite like it!
With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.
So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.
Our Machine learning course will help you to master this science and understand Machine Learning algorithms, which include Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:
The Machine Learning Course with Python by KnowledgeHut is a 48 hour, instructor-led live training course, with 80 hours of MCQs and assignments. It also includes 45 hours of hands-on practical session, along with 10 live projects.
Our Machine Learning course with Python will help you get hands-on experience of the following:
Machine Learning is an application of Artificial Intelligence that allows machines and computers to learn automatically to predict outcomes from examples and experiences, without there being any need for explicit programming. As the name suggests, it gives machines and computers the ability to learn, making them similar to humans.
The concept of machine learning is quite simple. Instead of writing code, data is fed to a generic algorithm. The generic algorithm/machine will build a logic which will be based on the data provided. The provided data is termed as ‘training data’ as they are used to make decisions or predictions without any program to perform the task.
1) Stanford defines Machine Learning as:
“Machine learning is the science of getting computers to act without being explicitly programmed.”
2) Nvidia defines Machine Learning as:
“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”
3) McKinsey & Co. defines Machine Learning as:
“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”
4) The University of Washington defines Machine Learning as:
“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”
5) Carnegie Mellon University defines Machine Learning as:
“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”
Today, algorithms of machine learning enable computers and machines to interact with humans, write and publish sport match reports, autonomously drive cars, and find terrorist suspects as well. Let’s peek through the origins of machine learning and its recent milestones.
Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.
The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.
Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.
The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.
IBM’s Deep Blue beat the world champion in a game of chess.
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.
The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.
IBM’s Watson beat its human competitors at Jeopardy.
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.
An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.
The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.
The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.
There are various other factors and steps involved as well. This is just an example of the process.
Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors.
Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as to identify clients with high-risk profiles.
Machine learning is finding varied uses in running government initiatives. It helps in detecting fraud and minimizes identity theft. It’s also used to filter and identify citizen data.
Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real time, which might lead to improved treatment or diagnosis.
There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc.
Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.
Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.
Understand the behavior of data as you build significant models
Learn about the various libraries offered by Python to manipulate, preprocess and visualize data
Learn about Supervised and Unsupervised Machine Learning.
Learn to use optimization techniques to find the minimum error in your machine learning model
Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail
Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
Understand Neural Network and apply them to classify image and perform sentiment analysis
Learn to use multiple learning algorithms to obtain better predictive performance
For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.
Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey, and earn an industry-recognized Machine Learning Certification from KnowledgeHut upon successful completion of the Machine Learning course.
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Hands-on: No hands-on
With attributes describing various aspects of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.
Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set contains 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable
In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.
Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
Machine Learning is the application of systems of Artificial Intelligence concepts to complete set tasks, without requiring reprogramming or human intervention in any form. The idea is to develop programs that access data on their own and analyse it without human help. ML is consistently in the list of LinkedIn’s top emerging jobs and is gaining huge popularity in Bangalore, the Silicon Valley of India. Here are two efficient ways to Machine Learning:
Bangalore is known as one of the biggest tech hubs of India as over 50% of India’s IT Companies are located in the city. Machine learning will help these companies get useful data from different sources, helping their businesses become more efficient and sustainable.
The concept of Machine Learning is a way for humans to be able to solve problems, without having to actually know and understand what the problem really is, as well as understanding why a particular approach to that problem actually works. It makes everything from our everyday tasks to big business decisions easier.
Machines are able to work faster than human brains do and as such, are able to solve problems faster than we ever can. For example, while there may exist a million options, answers or approaches to a problem, a machine is able to systematically work out, resolve and simultaneously evaluate all the options in order to obtain the best possible outcome or result.
Machine Learning has several practical applications in real life. It is the very solution that the world was looking for a variety of problems. Every industry, starting from health care, nursing, transport, customer service to government and financial institutions are benefitting from Machine Learning, which is what makes it an indispensable part of our society as it stands today.
Data has transformed every aspect of our lives. All organizations, from start-ups to tech giants to Fortune 500 corporations, are racing to harness the immense amounts of data generated unknowingly every day and put it to use for key decisions. Big and small data is reshaping technology and business as we know it and will continue to do so.
Here are some benefits of learning Machine Learning if you are a professional in Bangalore:
Follow these steps to learn Machine Learning by yourself:
Follow these 5 steps to get started in machine learning:
Brush up on the following skills to become a machine learning professional in Bangalore:
Follow these steps to execute a Machine Learning project with Python:
Algorithms are one of the most integral parts of the Machine Learning field. It is important for all learners completely understand the concepts of Machine Learning algorithms. Here’s how to do that:
The K Nearest Neighbours algorithm is an uncomplicated Machine Learning algorithm. Given a totally multiclass dataset to be worked on, with the goal of predicting the class of a given data point, we can make use of the K Nearest Neighbour algorithm.
This depends upon what you intend to do with Machine Learning.
There are basically 3 types of Machine Learning Algorithms -
The simplest of machine learning algorithms used to solve the simplest of ML problems (simple recognition) is k-nearest neighbour algorithm. Below are some of the reasons why kNN is used extensively for solving some of the basic, but important, real-life problems:
Keep these points in mind to choose the right algorithm:
Designing and implementing a Machine Learning algorithm involves the following steps:
Basic concepts are of vital importance in Machine Learning. We recommend you familiarize yourself with them first thing. Here are some others you should learn:
The median salary of Machine Learning Engineer in Bangalore is ₹8,00,000/yr. The range differs from ₹3,59,000 to as high as ₹20,00,000.
The average salary of a machine learning engineer in Bangalore compared to Bangalore is ₹8,00,000/yr whereas, in Chennai, it’s ₹6,50,000/yr
Bangalore is called the Silicon Valley of India. Technologically, it is the most developed city in India. Moreover, it is the home to most of the tech companies and as per reports, machine learning engineering is one of the fastest growing jobs. The reason being technology needs machine learning and this is the reason for such a massive demand. Therefore the demand for qualified Machine Learning Engineers is quite high in Bangalore.
Having the most desirable job among engineers in the ‘Silicon city of India’ has its own perks -
The one thing which is unique in terms of Bangalore is the massive opportunity machine learning presents. Apart from the high package, machine learning engineering offers a great insight into the world of technology due to the ability to make predictions through algorithms, hence offering greater knowledge. Moreover, due to the increasing use of AI in companies’ operations, Machine learning engineers in Bangalore have better networking opportunities.
Although there are quite many companies offering jobs to Machine Learning Engineers in Bangalore, following are the prominent companies -
|1.||TESTCON 2019, Bangalore||July 4, 2019, to July 5, 2019||Novotel Bengaluru Outer Ring Road|
|2.||Introduction To Data Science and Artificial Intelligence, Bangalore||June 23, 2019||Learnbay, Bangalore|
Practical Foundation on AI & Deep Learning, Bangalore
June 30, 2019
OpenCube Labs, North Bangalore
Understanding Blockchain Workshop, Bangalore
June 22, 2019
Opencube Labs, Bhuvaneswari Nagar, Dasarahalli, Bengaluru
|5.||Practical IoT Workshop, Bangalore|
June 16, 2019
|Opencube Labs, Bhuvaneswari Nagar, Dasarahalli, Bengaluru|
6th Annual IoT & AI Summit 2019, Bangalore
July 3, 2019
Sterlings Mac (Matthan Hotel) Bangalore
A.I. Deep-Dive Technology Workshop, Bangalore
July 25, 2019 to July 27, 2019
IKP Eden, Near Forum Mall, Koramangala, Stage 2 16, Bhuvanappa Layout, Tavarekere Main Rd, Domlur, Bengaluru
Machine Learning and Deep Learning Day, Bangalore
|September 12, 2019||Sterlings Mac Hotel, Hal Old Airport Road, Kodihalli, Bengaluru|
|9.||Open Data Science Conference India 2019, Bangalore||August 8, 2019||Sheraton Grand Bangalore Hotel At Brigade Gateway, 26/1 Dr. Rajkumar Road,, Malleswaram-rajajinagar, Bengaluru|
|10.||WORLD RPA, COGNITIVE AND AI SUMMIT, Bangalore||August 30, 2019||Doubletree Suites By Hilton Hotel Bangalore, Amblipura, PWD Quarters, 1st Sector, HSR Layout, Bengaluru, India|
|1.||Workshop: Machine Learning as a Service, Bangalore||July 25, 2017, to July 26, 2017||Thought Factory, Bangalore|
|2.||The Fifth Elephant 2017, Bangalore||July 28, 2017||MLR Convention Centre, Bangalore|
|3.||Deep Learning and Machine Learning for Computer Vision, Bangalore||November 4, 2017||Innov8 Coworking, Bengaluru|
|4.||Hacker Math for Machine Learning, Bangalore||November 25, 2017 to November 26, 2017||IKP EDEN, Bengaluru|
|5.||Introduction to Recommendation Systems, Bangalore||December 16, 2017||IKP EDEN, Bengaluru|
|6.||Deep Learning Bootcamp, Bangalore||March 10, 2018 to March 11, 2018IKP EDEN, Bangalore||IKP EDEN, Bangalore|
|7.||Machine Learning with Amazon SageMaker, Bangalore||April 4, 2018||IKP EDEN, Bangalore|
|8.||Building Data products at Uber, Bangalore||June 15, 2018||HasGeek House, Bangalore|
|9.||Role of data science in fraud detection, Bangalore||June 23, 2018||WalmartLabs, Bangalore|
|10.||Machine Learning with Amazon SageMaker, Bangalore||July 26, 2018||Auditorium 3, NIMHANS, Bangalore|
As a Machine Learning engineer, you will be responsible for the following:
Bangalore is one of the best cities to live in if you want to work in the IT sector. IT firms in Bangalore employ about 75% of India's pool of 2.5 million IT professionals. The city is filled with startups and big corporations hiring Machine Learning engineers. There are 387 Artificial Intelligence startups in Bangalore. As more and more companies are starting to use ML and AI, the demand of machine learning engineers has also gone high in this city.
If you want to network with fellow Machine Learning engineers in Bangalore, here is a list of professional groups you can try:
Some of the in-demand ML job roles include:
Follow these steps to use Python for mastering Machine Learning:
These are some of the most useful libraries when it comes to Python and ML:
Follow these steps to execute a Machine Learning project as a professional in Bangalore:
These tips will help you learn basic Python skills:
Here is a list of Python libraries which are best for machine learning purposes:
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Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data. Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!
By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and be able to build applications models. This will help you land lucrative jobs as a Data Scientist.
There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers this training online.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Machine Learning experts who have years of industry experience.
Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details.
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.
Learn Machine Learning with Python in Bangalore
Join our 5-day intensive machine learning training with python in Bangalore and get hands-on expertise on the use and benefits of Python programming language. This Data analysis and machine learning class training in Bangalore have been designed to give maximum learning benefit to beginners who want to get proficient in Python programming language. On joining this course you will receive 40 hours of intensive training from trained and certified experts, downloadable course book and course completion certificate issued by KnowledgeHut. You will get a chance to get your hands dirty with our intensive lab exercises that will teach you the full functionality and working of Python. By the end of this course, you would have mastered Python to build your own Python packages.
For busy professionals who are unable to attend our classroom training, we bring online classes that represent e-learning at its best and will give you all the advantages of a regular course.
Machine Learning with Python certification in Bangalore
So many programming languages are now being used and each has its own advantages and disadvantages. But Python has stood out among all others because of the numerous advantages it offers. First of all, it does away with many of the redundancies and repetitions in code. Python is fairly easy to master and has a syntax that is clear and readable. Also, its object-oriented nature allows code to be re-used. Its other advantages are that it is free, is cross-platform so can run on major operating systems like Windows, Linux and Mac is widely supported by a strong community of developers and is extensible and safe.
All these make it a very likable programming language and many organizations like their programs to be built on Python. There is a huge demand for Python professionals and hence KnowledgeHut has created this machine learning certification training in Bangalore on successful completion of which you will receive the machine learning certification in Bangalore.
Our machine learning with python course in Bangalore will teach you advanced data structures and algorithms, using libraries such as SciPy, NumPy, and Pandas to create data frames, grouping, processing data and performing numerical and scientific analysis and creating classifiers and clusters. This is a complete workshop and at the end of this course, you would have completely mastered Python.
Bangalore is a great city and has created a reputation for itself as being India’s IT capital. Python programmers are much in demand and the right skills can have a huge impact on your career. Join today for great coaching and great classroom environment