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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
Hands-on: No hands-on
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 uses the concepts of Artificial Intelligence to help the system in learning, performing, and improving a given set of tasks. In this, programs and systems are developed for accessing and analyzing the data without any human help.
Machine Learning involves observing the available data to derive information. The analysis of data is used to decipher patterns and relationships in the data, enhancing business scalability and improving business operations. All the major Machine Learning algorithms can be divided into the following two categories:
This includes using labeled examples to use the past data to get information and then apply it to the new data to predict future events. First, a dataset is loaded to the system which then trains using the data. Next, algorithms are used to infer a function and make predictions.
These types of algorithms are used when the available data is unlabelled and unclassified. The systems use this unlabeled data for deciphering the hidden structure in the data and inferring a function. These algorithms can only draw inferences from the data.
As data is becoming an integral part of our society, so is machine learning. Machines are faster than humans. So, machine learning can help you in resolving, evaluating and working out different options that will help you get the best possible result. Now more than ever, businesses are deploying machine learning to get the work done effectively and efficiently. It is used in different applications for different domains like health care, transport, customer service, nursing, finance, banking, customer service, etc.
Mumbai may not match Bangalore when it comes to the startup scene. However, that doesn’t change the success stories of various startups in 'The City Of Dreams', such as Quikr, Purple Squirrel, Grabhouse, Doormint, Pepperfry, etc. There are more than 100 Artificial Intelligence startups in Mumbai, including Eightfold, Webaroo, CreditVidya, etc. Data has become an important part of these companies.
According to a study done by Analytics India Magazine, the AI industry has grown by close to 30 per cent in the last one year to USD 230 million. Yet, over 4,000 positions in India remain vacant due to a shortage of qualified talent at the mid and senior level.
Here are some other top reasons to learn Machine Learning:
According to a report published by Tractica, by the year 2025, the services driven by AI will reach about $19.9 billion. More and more corporations are now incorporating Machine Learning in their business. This allows machine learning engineers to have better career opportunities.
According to Analytics India Magazine, Mumbai is the highest paymaster in AI, at almost Rs 15.6 lakh per annum, followed by Bengaluru at Rs 14.5 lakh, and Chennai, the lowest paymaster, at Rs 10.4 lakh.
Since the demand for machine learning is so high, there is just not enough talent. And as more and more companies are shifting towards the field, this demand is only going to rise. Currently, there are 90 Artificial Intelligence jobs available in Mumbai.
To learn Machine Learning, you need to follow the below-mentioned steps:
If you are an absolute beginner, here is how you can get started in Machine Learning-
If you want to become a Machine Learning Engineer, you need to learn the following key technical skill sets:
Successful compilation of the project includes the following steps:
Algorithms are the most integral and essential part of the Machine Learning. Here is how you can learn the top essential Machine Learning algorithms:
K-Nearest Neighbor algorithm is the most basic and essential Machine Learning algorithms for beginners. It is used for predicting the data points’ class from a multiclass dataset. Here is how it works:
Knowing algorithms to learn Machine Learning is not necessary. You don’t need to know any classic algorithms if you are planning to just use the ML algorithms.
However, basic knowledge of ML algorithms is required if you want to experiment with concepts of machine learning or create a new algorithm. You need to have the knowledge of the algorithm’s accuracy, its complexity, the costs involved, time taken, etc.
The Machine Learning algorithm can be categorized into the following 3 categories:
For beginners, the simplest Machine Learning algorithm is k-nearest neighbor algorithm. It can be used for classification and regression problems. It uses the measure of similarity to perform classification and labeled data for the training phase.
Selecting the right Machine Learning algorithm for a particular statement is very important as it affects the performance of the ML model. Here is how you can do it:
Here is how you can practically design & implement a Machine Learning algorithm using Python:
To be able to work on a Machine Learning project, you must be familiar with the following essential concepts of Machine Learning:
The median salary of Machine Learning Engineer in Mumbai is ₹7,50,000/yr. The Range differs from ₹3,76,000 to as high as ₹11,60,000
The average salary of a Machine Learning engineer in Mumbai compared with Bangalore is ₹7,50,000/yr whereas, in Bangalore, it’s ₹8,00,000/yr.
Mumbai is located in Maharashtra and the average salary of machine learning engineers sums up to around ₹6,50,000/yr whereas in Pune it is ₹414,229/yr.
According to LinkedIn, there are currently 1,829 open Machine Learning Engineering positions on the website. Mumbai is considered among the most developed cities of India. As most companies are integrating ML and AI into their top data initiatives, ML Engineers are in good demand.
Apart from the handsome salary package, other benefits of being a Machine Learning Engineer includes -
Although there are many companies offering jobs to Machine Learning Engineers in Mumbai, following are the prominent companies that hire them
|1.||INBA General Counsel Summit 2019||19th July 2019||Taj Lands End, Bandra, Mumbai|
|2.||Analytics In Markets (AIM)||20th July 2019||Renaissance Hotel, Powai, Mumbai|
|3.||International conference on Artificial Intelligence And Robotics (ICAIR-2019), Mumbai||19th – 20th August 2019||Mumbai, ICAIR-19, Mumbai, India|
As a Machine Learning Engineer, you will be responsible for designing and developing machine learning systems, executing the machine learning algorithms, conducting experiments and tests, working on customizing the algorithms according to your needs, etc.
Mumbai is home to leading banks like ICICI, DCB, IndusInd, etc., hundreds of legacy organisations, family businesses, and over 5000 startups. It is known as India’s fintech capital with over 400 fintech startups. Industries like IT, finance, healthcare and startups are looking to transition into AI, over the next few years, so there is a huge opportunity for machine learning professionals in Mumbai. Currently, there are 831 Machine Learning Jobs in Mumbai.
The companies with Machine Learning open positions in Mumbai are:
If you want to network with other Machine Learning Engineers, you can try one of the following professional groups:
The following ML job roles are in demand in 2019:
If you want to master Machine Learning using Python, you need to follow these steps:
To implement machine learning, you need to have the knowledge of the following essential Python libraries:
Here are the steps you need to follow to create a successful Machine Learning project with Python:
If you are a beginner in programming and want to learn Python, you need to follow these 6 tips:
If you want to learn Machine Learning, you must have a complete knowledge of the following Python libraries-
Everything from the course structure to the trainer and training venue was excellent. The curriculum was extensive and gave me a full understanding of the topic. This training has been a very good investment for me.
All my questions were answered clearly with examples. I really enjoyed the training session and am extremely satisfied with the overall experience. Looking forward to similar interesting sessions. KnowledgeHut's interactive training sessions are world class and I highly recommend them .
The KnowledgeHut course covered all concepts from basic to advanced. My trainer was very knowledgeable and I really liked the way he mapped all concepts to real world situations. The tasks done during the workshops helped me a great deal to add value to my career. I also liked the way the customer support was handled, they helped me throughout the process.
Overall, the training session at KnowledgeHut was a great experience. I learnt many things. I especially appreciate the fact that KnowledgeHut offers so many modes of learning and I was able to choose what suited me best. My trainer covered all the topics with live examples. I'm glad that I invested in this training.
The course materials were designed very well with all the instructions. The training session gave me a lot of exposure to industry relevant topics and helped me grow in my career.
The course material was designed very well. It was one of the best workshops I have ever attended in my career. Knowledgehut is a great place to learn new skills. The certificate I received after my course helped me get a great job offer. The training session was really worth investing.
I really enjoyed the training session and am extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.
This is a great course to invest in. The trainers are experienced, conduct the sessions with enthusiasm and ensure that participants are well prepared for the industry. I would like to thank my trainer for his guidance.
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.
Machine Learning with Python Training in Mumbai
Mumbai, tagged as the City of Dreams is the capital city of Maharashtra. Throbbing with huge numbers of billionaires and millionaires, this city is also known as the economic and financial capital of India. Developing on the IT front as well, this growth has prompted the need for data analysis professionals to make their presence felt in churning out projects, publishing and writing papers by a process which inspects, transforms, cleans and remodels data. Primarily meant to find out useful information, support decision making and conclusions, this is teamed with machine learning using Python by KnowledgeHut as part of a comprehensive course plan.
What is the course all about?
Nowadays, one can learn both data analysis and machine learning by attending workshops related to python course. Data Analysis using python course in Mumbai is being taught by KnowledgeHut, an online academy. This course helps one learn programming techniques using python. Apart from that, one can also learn about scientific computing libraries and modules. Data analysis training using python in Mumbai can give a big boost to one's career as most of the companies work on data analysis. KnowledgeHut through a demo offers student-friendly procedures for them to clear their doubts whenever possible, supported by exhaustive training material for ready reference. Benefits of the course Python has its own libraries using which a student can learn machine learning very properly. A student can attend various lectures and practice sessions of machine learning using python course in Mumbai which can later be incorporated into machine learning.
The KnowledgeHut Way
The coaching of machine learning includes various modules like linear regression, parallel computing, decision tree and clustering, deep learning and unsupervised learning. The cost charged by machine learning training using python facilitated by KnowledgeHut is very less. Therefore, one should register and enroll in a purse-friendly and knowledge-centric course.