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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
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).
The aim of Machine Learning is to enable computer systems to perform tasks like locating data, analyzing them and learning from the data without any form of external help or intervention. The process of Machine Learning starts once the observations of data become available in the form of direct experiences or examples. The system looks at the provided data, tries to find patterns in them and then extrapolates these observed patterns in order to help in making decisions for the future. It takes into account all the information and datasets available to the program or system to make its decision.
There are several methods of Machine Learning. Broadly they can be categorized in to the following two categories-
The work of Machine Learning is to take a huge amount of data, analyze it and find the best possible outcome for a task or hidden patterns in the data. This helps humans in working with large amount of data without actually knowing what it is about and finding solutions and patterns to this data without actually understanding why a certain solution works for a problem.
Machines and computers have the ability to work faster than the human brain by processing a huge number of data and working out a solution faster than the human brain can. For instance, if there are millions of options, possibilities and opinions, a machine can analyze all the possibilities, systematically working out and evaluating all possibilities and finding the best outcome or conclusion.
Machine Learning is the most practical solution to all our worldly problems and needs. It enables businesses to be more efficient by saving time, money and efforts. We can get more amount of work done with Machine Learning, making it a reliable, effective and appropriate system. Industries like health care, nursing, transport, governments and finance benefit from the developments in Machine Learning, making it an indispensable part of our society as of today.
Due to the social media boom, millions of data is being generated every day, helping companies understand consumer needs. At present, all companies- from startups to Fortune 500 are incorporating Machine Learning to make key decisions for their organizations. With data, big and small, being the basis of technological and industrial advancements, Machine Learning will remain an important sector for the next few decades, constantly reshaping itself to the market needs.
Importance and relevance of Machine Learning in industries and daily life
Machine Learning is rapidly becoming a popular technology among tech companies. From Facebook and Instagram feed to surge of price in Uber, Amazon recommendation to fraud detection, all kinds of functions are being performed purely through Machine Learning algorithms, without human intervention and minimal control.
More than half of the world’s population uses some device or another which deploys Machine Learning algorithms. It is inevitable that professionals in the field of Information Technology and Data Science take up Machine Learning programs to stay ahead and be relevant in society. Moreover, there is an increasing demand for professionals with Machine Learning knowledge in every industry with not enough Machine Learning experts.
Listed below are the benefits of getting certified in Machine Learning-
This provides an incentive to private and public sectors to incorporate Machine Learning. From information technology to health care industries or oil and gas mega corporations, every industry is gearing up to make use of the immense insight that Machine Learning provides.
Machine Learning is constantly evolving and developing itself everyday due to the immense amount of possibilities it offers. In Pune, there are several institutions that offer courses in Machine Learning including
While one can always opt for certified courses or diplomas, self-learning ML can also be equally effective if one has the motivation to keep working forward. The best way to learn Machine Learning is through a course. Few things to keep in mind are-
Machine Learning is a practical medium that requires hands-on experience rather than theoretical knowledge. While people should read up research papers or textbooks on ML in order to understand the concepts behind it, in real job situations, ML experts are hardly asked about their theoretical knowledge or their ability to derive proofs. Your ability will depend on your successful use and manipulation of available data for the development of a company.
The demand for an ML enthusiast in a company will increase based on the number of practical projects undertaken by the individual. Thus, the best way to increase your chances of getting employed is by taking up as many projects during your self learning days as you can. Projects provide employers with an idea about the kind of problems and coding structures a possible candidate has worked with giving them the idea of the employee’s experience.
Below are the steps you can follow:
The most important aspect of grasping ML skills is practice and reworking on old projects again and again to attune your mindset to think up numerous situations and solutions to problems and pushing ourselves to think outside the box. Thus the proverb ‘practice makes perfect’ goes perfectly with learning ML.
The best recommendation anyone can give to beginners are the 5 point steps mentioned below-
Pune is a great place for machine learning engineers to work in. Some of the companies that employ machine learning professionals in Pune include LifeAI, Telstra, Credit Suisse, Velotio Technologies, Forgeahead, Maxmedia Developers, Zensar Technologies, Emblaze Training & Services, Acellere GmbH, Pratiti Technologies, Healthcoco Technologies, Foghorn Systems, etc. In order to consider yourself an ML expert, the following technical skills are mandatory to learn and imbibe in your projects-
Below are the steps required for executing a successful Machine Learning project with Python (ML Project)-
Algorithms are an integral part of Machine Learning which makes it a necessary tool to understand, know and ingrain to create the best possible ML models. The following is a smart way of going about learning ML algorithms;
The benefits of KNN is the ease of use and simplicity. Though it uses a lot of memory to store the large dataset, it calculates only when prediction is needed.
It is known to be ‘naїve’ because of its assumption that the variables are independent of each other, which is an improbable case in real life situations. This algorithm can be used on language-based content, like web pages and articles or smaller bodies of text like tweets or metadata for blogs. This algorithm is best for classifying content according to categories and effective in prediction disease development and location as well as analyzing human emotions.
Your intention and future goals with Machine Learning determines the necessity of learning algorithms. If you simply want to use the existing Machine Learning algorithms without having any knowledge of classic algorithms, you can do so. There are various online courses on Machine Learning that do not teach algorithms with Machine Learning tools. However, if you want to bring innovations in the field of Machine Learning then the basic knowledge and workings of algorithms will be a prerequisite for you. As an innovator, it is your responsibility to find new and improved Machine Learning analyzing tools, you will need to have the knowledge of new algorithms as well as devise new algorithms of our own. This requires you to have a good grasp of the different aspects of algorithms and use that knowledge to devise your own.
Machine Learning algorithms are basically of three types:
K-nearest neighbor algorithm is the simplest algorithm for beginners. It can be used for regression as well as classification. It is non-parametric and classifies based on the similarity measure. In this, labeled data is used for the training phase and the goal of the algorithm becomes predicting a class for an object based on its k nearest surroundings where k defines the number of neighbors. Some cases where it is used includes vehicle number plate recognition, identifying patterns in credit card usage, etc.
Machine Learning is the most popular and heavily used system. Thus it has a lot of tools, algorithms and models to choose from. Having an understanding of selecting the right algorithm for your problem will determine the final quality of your project.
The more you practice and implement Machine Learning algorithms, the more efficient and faster will your solutions become. The process of implementation of ML algorithms are as follows;
Apart from having a good understanding of the basic concepts and systems of Machine Learning, below are some of the most essential topics of Machine Learning that you need to master:
The median salary of a Machine Learning Engineer in Pune city is ₹6,50,000/yr. The range differs from ₹3,00,000 to as high as ₹17,00,000.
The average salary of a machine learning engineer in Pune city compared with Bangalore is ₹6,50,000/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.
Pune is the second largest city of Maharashtra. It is not only blessed with natural beauty but is technologically advanced too. A lot of companies, especially those dealing with technology are established in Pune. These companies understand the value of AI and ML and therefore the demand for an ML engineer remains high. And various reports expect this growth to continue accelerating in the coming years due to the increasing use of AI in companies’ operations.
More people are getting interested in Machine Learning every day due to high demand and better pay. Some other advantages are-
Pune is known to provide huge encouragement to a wide range of innovation and technology. Machine learning has evolved quickly in the last few years with new languages, new frameworks, new techniques, and various new things to learn. And being a city of techies, it offers everything you can ask for if you are eager to learn.
Although there are quite many companies offering jobs to Machine Learning Engineers in Pune, following are the prominent companies -
|1.||IRAJ-International Conference on Smart Technology, Artificial Intelligence and Computer Engineering (ICSTAICE)||16th June 2019||Pune, Maharashtra|
|2.||ISSRD- International Conference on Recent Developments in Computer & Information Technology||19th June 2019||Kapila Business Hotel, Pune, Maharashtra|
|3.||ieeeforum-International Conference on Computer Science, Industrial Electronics (ICCSIE)||30th June 2019||Pune, Maharashtra|
|4.||The ASAR- International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC)||6th July 2019||Pune, Maharashtra|
|5.||ISETE - International Conference on Artificial Intelligence, Machine Learning and Big Data Engineering (ICAIMLBDE)||21st July 2019||Pune, Maharashtra|
|6.||Science Plus - International Conference on Robotics, Automation and Communication Engineering (ICRACE-2019) ||28th July 2019||Pune, Maharashtra|
|7.||IRAJ-International Conference on Big Data, IoT, Cyber Security and Information Technology (ICBDICSIT)||11th August 2019||Pune, Maharashtra|
|8.||Academics World 656th International Conference on Artificial Intelligence and Soft Computing (ICAISC)||24th August 2019||Pune, Maharashtra|
5.The ASAR- International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC), Pune
|1.||India Analytics & Big Data Summit||20-21st January 2017||Royal Orchid Central Kalyani Nagar, Marisoft Annexe Building, Pune, Maharashtra 411014|
|UX Design Con||6th May 2017||Royal Orchid Central Kalyani Nagar, Marisoft Annexe Building, Pune, Maharashtra 411014|
|3.||DevOps Summit||14th September 2017||Royal Orchid Central Kalyani Nagar, Marisoft Annexe Building, Pune, Maharashtra 411014|
|4.||Seminar On Python Analytics (Data Analytics/ Machine Learning)||23rd Feb 2018||Learn Well Technocraft 203, Above Pizza Hut, Supreme Centre, ITI Rd, Near Parihar Chowk, Anand Park, Aundh, Pune, Maharashtra 411007, India|
|5.||Pune Data Conference 2018||31st March 2018||The Westin Pune, Koregaon Park, Mundhwa Road, Pingale Wasti, Annexe, Ghorpadi, Pune|
|6.||Pune DevCon 2018||8th December 2018||5thFloor, A - wing, MCCIA Trade Towers, International Convention Center, 403, Senapati Bapat Road, Pune- 411016.|
|7.||IEEE SPS Winter School on Advances in Machine Learning and Visual Analytics for Forensic and Security Applications|
10-14th December 2018
The Pride Hotel, Shivaji Nagar, Pune
7. IEEE SPS Winter School on Advances in Machine Learning and Visual Analytics for Forensic and Security Applications, Pune
The responsibilities of a Machine Learning Engineer include:
Pune is the second-largest city in the Indian state of Maharashtra, after Mumbai and is one of the leading IT services centers in India. There are several tech companies in Pune, including Cognizant, TCS, Tech Mahindra, Infosys, Wipro, etc. With more than 2000 startups, Pune is a developing hub for the growing Indian startup ecosystem. As companies are investing in machine learning and artificial intelligence, they’re looking for ML engineers to integrate these technologies into their business initiatives.
Some of the ML job roles in demand are:
Here's how you can get started on the use of Python for Machine Learning:
Some of the essential Python libraries used to implement Machine Learning with Python are:
If you want your ML project to be executed successfully, we have compiled the steps for the same below:
The following steps will make it easier for you to learn using Python programming.
Below is a list of Python libraries which are best for machine learning:
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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.
Machine Learning with Python Course in Pune has been making waves in the tech sector in recent times with the city combining great talent pool mirroring high standards of life. Quite a few companies have switched their branches and headquarters to Pune in recent years, so opportunities are galore in the city. KnowledgeHut has started Machine Learning with Python course in Pune, so as to feed the rising demand for data scientists. Data processing is turning out to be a lucrative option, and experts in the field are sure to have highly successful careers in the city. What is this course all about? Data processing and analysis are being done using python packages like Scipy, Pandas, and Matplotlib, owing to the ease of use and flexibility that Python provides. The course teaches its students about the usage of such packages and covers various other topics such as Regression Analysis, Decision Trees and Collaborative Filtering. Machine learning and data mining techniques which are in high demand are all given importance as part of the job-friendly curriculum of machine learning using Python course in Pune. Benefits of the course: Data scientists are in high demand with tech companies, as it can give a ready assessment of the market situation, and can even predict ups and downs in the future. Data analysis training using Python in Pune has the capability to open a range of career opportunities for students with the average salary of a data scientist on a higher scale compared to his counterparts. The KnowledgeHut Way: The main advantage of Knowledge Hut's machine learning training using Python is the flexibility associated with it; students can schedule it according to their priorities with ease. The tutor who is a subject matter expert in his chosen field will thus leave no stone unturned in ensuring that his students receive a real hands-on experience of all the principles discussed, during the training itself.