Machine Learning with Python Training in Chennai, India

KnowledgeHut’s Machine Learning with Python workshop is focused on helping professionals gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals land lucrative jobs across industries. At the end of the course, you will be able to: 

• Build Python programs: distribution, user-defined functions, importing datasets and more 
• Manipulate and analyse data using Pandas library 
  
• Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot 
  
• Build data distribution models: variance, standard deviation, interquartile range 
  
• Calculate conditional probability via Hypothesis Testing 
  
• Perform analysis of variance (ANOVA) 
  
• Build linear regression models, evaluate model parameters, and measure performance metrics 
  
• Use Dimensionality Reduction 
  
• Build Logistic Regression models, evaluate model parameters, and measure performance metrics 
  
• Perform K-means Clustering and Hierarchical Clustering  
  
• Build KNN algorithm models to find the optimum value of K  
  
• Build Decision Tree models for both regression and classification problems  
  
• Use ensemble techniques like averaging, weighted averaging, max voting 
  
• Use techniques of bootstrap sampling, bagging and boosting 
• Build Random Forest models 
• Find optimum number of components/factors using scree plot, one-eigenvalue criterion 
• Perform PCA/Factor Analysis 
• Build Apriori algorithms with key metrics like Support, Confidence and Lift 
• Build recommendation engines using UBCF and IBCF 

The program is designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the course covers everything you need to know, whether you’re a novice or an expert. 

To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities. 

This immersive and interactive workshop with an industry-relevant curriculum, capstone project, and guided mentorship is your chance to launch a career as a Machine Learning expert. The curriculum is split into easily comprehensible modules that cover the latest advancements in ML and Python. The initial modules focus on the technical aspects of becoming a Machine Learning expert. The succeeding modules introduce Python, its best practices, and how it is used in Machine Learning.  

The final modules deep dive into Machine Learning and take learners through the algorithms, types of data, and more. In addition to following a practical and problem-solving approach, the curriculum also follows a reason-based learning approach by incorporating case studies, examples, and real-world cases.

Yes, our Machine Learning with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. 

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

1. Anyone interested in Machine Learning and using it to solve problems  
2. Software or Data Engineers interested in quantitative analysis with Python  
3. Data Analysts, Economists or Researchers

There are no prerequisites for attending this course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below

Hardware requirements 

• Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors 
• 4 GB RAM 
  
• 10 GB of free space  

Software Requirements  

• Web browser such as Google Chrome, Microsoft Edge, or Firefox  

System Requirements 

• 32 or 64-bit Operating System 
• 8 GB of RAM 
  

On adequately completing all aspects of the Machine Learning with Python course, you will be offered a course completion certificate from KnowledgeHut.  

In addition, you will get to showcase your newly acquired Machine Learning skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects. 

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.  

The Machine Learning with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.  

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.  

The Machine Learning with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Machine Learning. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects. 

Our Machine Learning course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.  

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it. 

Schedules for our upcoming workshops in Machine Learning with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.   

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.  

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing. 

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger. 

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us on support@knowledgehut.com and we will be happy to get back to you. 

With our 100% Satisfaction Guarantee policy, you are assured of the highest standards of quality. At KnowledgeHut, we strive to ensure that your learning experience with us is beyond reproach. However, if for any reason your expectations are not met, we will refund the course fee in accordance with our 100% Satisfaction Guarantee policy.   

Yes, you can switch the start date to your Machine Learning with Python training, with prior notice of at least 24 hrs and subject to availability in the desired batch. 

Yes, we have a 100% money-back guarantee in line with our 100% Satisfaction Guarantee policy. 

Yes, scholarships are available for students and veterans with grants up to 50% of the course fees. 

To avail scholarships for our Machine Learning with Python course, please get in touch with us at support@knowledgehut.com. The team shall send across the forms and instructions to you. Based upon the responses and answers that we receive, our panel of experts take a decision on the Grant. The entire process could take about 7 to 15 days. 

Yes, group discounts are available and apply to groups as small as three (3) participants. The more participants that attend a training course, the greater the discount. By registering in groups, you can typically save up 20% to 30% on the course fee. For more details, please check out upcoming schedules.  

Yes, instalment options are available for payment of course fee. To avail the instalment option, please get in touch with us at support@knowledgehut.com. The team will explain how the instalments work and provide timelines for your case. 

Typically, the number of instalments vary from 2 to 3, but the full amount must be paid before you complete the course. 

Machine Learning or ML is the study of algorithms and statistical models used by computer systems to perform specific tasks. It is a field which deals with patterns and inferring from data sets to integrate and implement Machine Learning. It also includes certain concepts of Artificial Intelligence that enables the system to perform complicated tasks without any intervention from the user. 

Machine Learning, therefore, involves programming, coding skills and a keen eye for detail. Also, expect massive volumes of data sets- both structured and unstructured. The systems you thus develop would figure out patterns, gather valuable insights, and extrapolate the observations hence extracted to similar problems in the future. There are several methods of Machine Learning, we have discussed some of them below; 

• Supervised machine learning algorithms: Here you get to work with algorithms which are collected from previous data sets. The insights gained from the past data are then applied to the new data which is available. This improves the accuracy of the program and allows you to give accurate predictions about the results. 
• • A compressible data set is added to the system which in turn analyses it and extracts certain conclusions which can be used for future references. 
  • The algorithm thus derived from these conclusions are arranged to form an inferred function which allows the system to make predictions.
• Unsupervised machine learning algorithms: Here the algorithms which are created using provided data sets are not labeled, structured or classified. 
• The algorithms thus created are used to infer a function and describe the inherent structure derived from the unclassified data. 
• The system is hence programmed to figure out the accurate result and explore the available data sets to correctly identify the patterns 

In this day and age, technology has become the be all and end all of each action that we perform. From conducting business to communicating with people- we heavily rely on gadgets to perform even the simplest, most basic of tasks. Machine Learning allows users to take greater control of computers and systems. It enables one to quickly analyze huge data sets, analyze the given problems and derive solutions based on past experiences and training. It is the perfect combination of man and machine, of accuracy and intuition. Machine Learning has revolutionized the world in more than one way. Let’s see how: 

• Machine Learning is Super Easy and Effective 

There was a time when computers were not intuitive and produced only what was fed to the system- all that changed with Machine Learning and AI where an engineer can now train the computer to make decisions independently, sort through piles of unstructured data and gather insights automatically.

• Machine Learning has a wide Scope 

Contrary to popular belief, machine learning is not just confined to the IT sector. It has now ventured onto several other fields, becoming almost indispensable for most industries ranging from healthcare to fashion. Chennai is home to leading tech companies, such as Cognizant, Tata Consultancy Services, HCL Technologies, Accenture, Nokia, Tech Mahindra, PayPal India Pvt Ltd, Mindtree, Capgemini, Amazon, etc. Machine learning has enabled these businesses by boosting their productivity and reach tenfold. 

Chennai is the 2nd largest centre of IT development in India and is home to around 1400 startups, including BankBazaar, Freshdesk, Chargebee, Bharatmatrimony, etc. From startups to tech giants and corporations, companies in Chennai are leveraging machine learning to expand their profits. 

Machine Learning is no more than just a simple function of the tech world. Here are some ways in which data science and machine learning is changing the world for the better; 

• Machine learning opens up new employment opportunities for people in several sectors 
• It has simplified research and development for industries and corporate sectors 
• Machine learning jobs pay really well 
• There is no dearth of demand for data scientists and ML experts as well 

Machine Learning is a field which is slowly expanding its roots in various sectors, venturing into industries which are not strictly connected to IT as well. One can easily learn the basics of machine learning using these points. Note that a good combination of theory and practice will help you master ML. Go through research papers, journals, and websites to learn as much as you can. Also, practice working on real-time projects, build databases and upload them on platforms to attract employers.

Below are the steps you can follow:

• Decide your syllabus and then work your way to learn as much as you can about the different platforms and programs 
• For starters, we would recommend, start slow and stick to one programming language. 
• Also, strive to better your logical, statistical and mathematical skills 
• Now that you know the basics, you can move on to implement all that you have learned on data sets and ML algorithms. 
• Join bootcamps in Chennai to get real-world project experiences.

Here is the 5 step process you need to follow to get started with Machine Learning:

• Adjust your mindset: Machine Learning is a difficult field and it is easy to lose confidence in oneself. You need to keep reminding yourself that you can do it. Stay focused and keep practicing. Find people working in the field of Machine learning to stay motivated. Try joining boot camps or meetups in Chennai to meet professionals working in the same field.
• Pick a process that suits you best: Create a system of working with problems and finding solutions in a systematic and structured way.
• Pick a tool: Select the process that you will be using to implement machine learning concepts:
  • Beginners - Weka Workbench
  • Intermediate level learners - Python Ecosystem
  • Advanced level learners - R Platform
• Practice on Datasets: Get the datasets available only to practice collecting and manipulating data. These datasets must be real-world problems.
• Build your own portfolio: Start creating your projects to build your portfolio.

Let us list down the key technical skills and concepts that one must understand to thoroughly master machine learning:

• Programming languages: For machine learning, one must be comfortable working with platforms like Python, Java, Scala, etc.   
• Database: Database platforms like MySQL and Oracle are also mandatory for students of machine learning 
• Visualization tools: Visualization tools allow the user to get a realistic picture of how the ML algorithm would work in real-time. ML frameworks like Apache Spark, Scala, Tensor Flow, and R programming are also important 
• Mathematical skills: Mathematical algorithms and concepts Linear Algebra-Calculus, Bayesian framework, probability, statistics, and graphs are necessary aspects of ML 

ML professionals often use Python to compile and execute their code because it is the easiest, most intuitive platform in the market. Here is how you can run your program on Python: 

• Getting the right data is the first step to writing a successful ML program, it is not just about how much data you have gathered but the quality of the data set which makes or breaks your program 
• Next, you have to arrange the raw data into the structured unit, fill in the missing information and engineer it to suit your needs 
• Visualization is the process which allows us to find the correlation between the variables and show just how the prepared data would run in real-time 
• The next step is to pick the right model or algorithm to structure the program on 
• After choosing an appropriate algorithm you have to train and test the program to see if it runs seamlessly in the chosen method. 
• Oftentimes you would have to adjust or tweak certain parameters of the program to suit the model or vice versa until you reach the desired conclusion 

Algorithms form the basis of Machine Learning and it is essential that you have a thorough understanding of them. Here is how you can learn the top essential machine learning algorithms:

1. List the various Machine Learning algorithms: The first step is listing down all the algorithms that you need to learn. Categorize them so that you can understand the different classes of algorithms.
2. Apply the Machine Learning algorithms that you listed down: Implement the algorithms that you studied in a project. Give special attention to algorithms like Support Vector Machines, Decision trees, etc.
3. Describe these Machine Learning algorithms: Explore the algorithms and write a description for it. Write down what you learnt, what problems you faced, and how you overcame it. This will help you create a mini-encyclopedia of machine learning algorithms.
4. Experiment on Machine Learning Algorithms: Start experimenting with the algorithms using the standardized datasets. This will help you learn how to customize algorithms according to your project.

Machine Learning Algorithms can be classified basically into the following 3 types - 

1. Supervised Learning: Linear Regression, Logistic Regression, Classification and Regression Trees (CART), Naïve Bayes, K-Nearest Neighbours 
2. Unsupervised Learning: Apriori, K-Means, Principal Component Analysis (PCA) 
3. Ensemble Learning: Bagging, Boosting

• Supervised Learning:  Supervised learning is when we train an algorithm and at the end of the procedure, we pick the function that best describes the input data, the one that for a given X makes the best assessment of y (X -> y)
  • Linear Regression is the connection between the input variables (x) and output variable (y) which is stated as an equation of the form y = a + bx
  • Logical Regression is when the outcome of the regression is vague rather than accurate as in the former linear regression 
  • CART or Classification and Regression Trees (CART) is an implementation of Decision Trees. The algorithm graphs the prospect of each consequence and predicts the result on the basis of distinct nodes and branches. At each non-terminal node is a single input variable (x). The splitting point on that node represents the numerous results that can occur, and the subsequent leaf node signifies the output variable (y).
  • Naïve Bayes - This algorithm guesses the likelihood of an outcome, given the rudimentary value of some other variable. It works on the Bayes theorem, based on the supposition that all variables are sovereign in nature. 
  • K-Nearest Neighbours - This algorithm charts the data set at once. It allows a predefined value of “k” to find out the result for an assumed value of the variable, it collects “k nearest instances” of the value in the dataset and then calculates the average of the given values to determine the output. 
• Unsupervised Learning is where the computer is trained to handle unlabelled data. Here, only the input variables are given and not the output ones. Thus, the fundamental construction of the assumed data sets is analyzed to discover conceivable relations and collections. 
  • Apriori: This algorithm is used in several databases to categorize recurrent links or incidences of two items occurring together, then such relations are used to forecast further associations.
  • K-Means: This algorithm assembles similar data into groups, and them connects individual data points to an “assumed” center point of the cluster. Next, we execute the reiteration of the several stages to check if the gap between the variable and the center point to calculate the closest point which resonates with each cluster. 
  • PCA: Principal Component Analysis (PCA) makes it easier for us to envisage the data set. This is done by reducing the number of variables. One has to then calculate and chart the extreme variances of each point onto a new coordinate system, with axes matching to the primary components chosen. 
• Ensemble Learning: A group is always considered better than individual units. Ensemble learning is built on this principle. Here the algorithms combine the results of each unit and then analyze them as a whole to find a precise picture of the definite outcome. 
  • Bagging - This algorithm is used to create numerous datasets (based on the original one), then model the same algorithm on each to yield different outputs. The result can then be amassed and performed upon to find the actual result.
  • Boosting - This algorithm runs parallel to the one above. The models work consecutively instead of the analogous nature of bagging. Thus, each new dataset is based upon the mistakes of the previous one. 

The simplest algorithms are the ones which are easy to understand, and are comprehensible, intuitive and very flexible. There are several such algorithms which can be easily implemented,  customised to suit the problem and optimised for platforms. Also, ensure that the program is effective time-saving and includes high-level concepts. The K-nearest neighbour algorithms is the easiest of the lot that we have discussed formerly above. This algorithm can be applied to several real-life situations and helps you figure out the solution in record time. 

ML comes with a wide variety of tools, algorithms, and functions which you can pick from. Here is how you can pick the right algorithm for the project; 

• To Understand The Given Data: First, you have to understand the data which comes your way, next you have to visualize the data, plot graphs, find correlations between variables and analyze the data sets accordingly so as to plot it on the model. 
• Find the right algorithm: There are several types of algorithms that we can adopt in the ML program which we have already discussed above. These models are used as a framework in which you can execute your program. 
• Understand and apply constraints: Constraints help you check the algorithm. Note that the best models and algorithms work on high-end machines and entail storage and operation resources. Constrictions can be in the form of hardware or software as well.
• Find available algorithms: Once after getting the right resources and models, the next step is to check the requirements of algorithms so as to see how well it would work out. 

Not necessarily, if you want to simply use and re-use the pre-existing ML algorithms. In such a case, only a basic knowledge about the theoretical concepts of ML is enough. However, if you plan on experimenting and exploring the different aspects of ML then you must understand the technicalities involved in designing and executing algorithms. One can learn several concepts regarding Algorithms both online and offline. 

Here is how you can practically design and implement a machine learning algorithm using python:

• Select the algorithm that you wish to implement: Select the algorithm. You need to be precise in choosing the algorithm. Determine the class, type, description, and special implementation of the algorithm.
• Select the problem you wish to work upon: Select the problem that you will be using for testing. Keep in mind the efficiency and the implementation of the algorithm while selecting the problem.
• Research the algorithm that you wish to implement: Go through outlooks, descriptions, methodologies, and implementation of the algorithm. It will help you avoid any roadblocks you might face.
• Undertake unit testing: Last step is creating and running unit test for all the functions involved in the algorithm. Consider this your algorithm’s test-driven development aspect.

Here are some of the essential topics of ML that one has to understand and master to run a program successfully:

• Decision Trees: A Decision tree is a type of supervised learning algorithm that is used for cataloging problems. It helps the system pick the features which can be used for spitting between datasets. They also help the system to determine the settings which aid the splitting of a particular iteration.
• Support Vector Machines: Support Vector Machines are methodologies which offer an advanced accuracy in classification problems. Support Vector Machines can also be used in problems of regression as well. 
• Naive Bayes: The Naive Bayes algorithm is a classification technique that is based on Bayes' theorem. It works on the supposition that predictions are independent and different from each other. 
• Random Forest algorithm: The Random Forest algorithm is a supervised learning algorithm. It designs a cluster of decision trees and creates random inputs. One can also identify the random patterns, analyze them and train programs with the bagging method.

The median salary of Machine Learning Engineer in Chennai 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 Chennai compared with Bangalore is ₹6,50,000/yr whereas, in Bangalore, it’s ₹8,00,000/yr.

The average salary of a machine learning engineer in Chennai is ₹6,50,000/yr whereas in Coimbatore it is between ₹2,50,000 - ₹4,00,000 P.A.

Chennai is viewed as among the top urban cities of India. A year ago, it was recorded in the rundown of top 10 quickest developing urban cities on the planet. Since industries in Chennai are realising that Machine Learning is the future, they are vigorously contributing to it which is the motivation behind why today machine learning engineer roles claim the number one spot due to higher salaries and faster growth. Also, due to the increasing use of AI, the study expects this growth to continue accelerating in the coming years.

Being one of the most demanding jobs in the present scenario, one can expect the amazing benefits that a machine learning engineering job can offer - 

• High payout - One of the main motivations behind its interest among engineers is a direct result of the high salary this field offers. 
• Acknowledgment - Organizations regularly look for a person who is a PhD or Masters in their field. Being an ML engineer, you are relied upon to have the right knowledge and skills.

Although the high salary is not the only reason, it surely is one of the crucial ones which makes ML engineering as one of the most promising careers in recent times. The future depends on AI and ML and it is not far away when we see Machine learning engineer’s demand overtaking that of a data scientist.

Although there are quite many companies offering jobs to Machine Learning Engineers in Chennai, following are the prominent companies - 

• Tata Consultancy Services
• Intel Corporation
• Infosys
• OptiSol Business Solutions Private Limited
• Hexaware Technologies
• Vigneshwara Exports
• Genesis Scada Solutions

1. Workshop on Machine Learning (MACHINE - 2019), Chennai

1. 1. About the conference: The workshop will be conducted by Top Engineers. The workshop will help the students learn the basics of Machine Learning. 
   2. Event Date: June 29, 2019
   3. Venue: IIT Madras Research Park, South Chennai
   4. Days of Program: 1
   5. Timings: 9:00 AM - 5:00 PM
   6. Purpose: The agenda of the workshop will be to dive into the world of Machine Learning, give an introduction to Artificial Neural Networks, Binary Classification, Logistic Regressions and introduction to next level research scopes. 
   7. Registration cost: Rs. 900 per person (India); $100 USD per person (International)
   8. Who are the major sponsors: Top International Educational Trust

2. Machine Learning Workshop - Lema Labs, Chennai

1. 1. About the conference - The two days workshop on Machine Learning will help the students learn the fundamentals of Machine Learning.
   2. Event Date: June 22, 2019 to June 23, 2019
   3. Venue: IIT Madras Research Park, South Chennai
   4. Days of Program: 2
   5. Timings: 9:00 AM - 5:00 PM
   6. Purpose: The purpose of this workshop is to teach the fundamentals of Machine Learning, Python Programming, Data Processing, Classification, Hyperparameters Grid Search, Linear Regression and Gradient Descent Algorithm.  
   7. Registration cost: Rs. 4500 per person
   8. Who are the major sponsors: Lema Labs

3. The ASAR- International conference on Machine learning Big data management Cloud and Computing (ICMLBDMCC), Chennai

1. 1. About the conference - The ASAR conference will help in bringing new techniques in the field of Machine Learning. 
   2. Event Date: June 23, 2019
   3. Venue: Chennai, Tamil Nadu
   4. Days of Program: 1
   5. Timings: 9:30 AM - 5:00 PM
   6. Purpose: The purpose of the conference is to provide new opportunities and to develop the field of Machine Learning.  
   7. How many speakers: 3
   8. Speakers & Profile: 
      1. Dr. P. Suresh , M.E, Ph.D., KCE, Coimbatore, India 
      2. Dr. Bommanna Kanagaraj, PSNA College of Engineering and Technology, India
      3. Dr. Yuchou Chang, University of Wisconsin, United States
   9. Who are the major sponsors: Asian Society for Academic Research

4. The ASAR- International conference on Machine learning Big data management Cloud and Computing (ICMLBDMCC), Chennai

1. 1. About the conference - The ASAR conference will help in bringing new techniques in the field of Machine Learning. 
   2. Event Date: June 26, 2019
   3. Venue: Chennai, Tamil Nadu
   4. Days of Program: 1
   5. Timings: 9:30 AM - 5:00 PM
   6. Purpose: Explore the new techniques and horizons that will contribute to Machine learning Big data management Cloud and Computing in the next few years.  
   7. How many speakers: 3
   8. Speakers & Profile: 
      1. Dr. P. Suresh, M.E, Ph.D., KCE ,Coimbatore, India 
      2. Dr. Bommanna Kanagaraj, PSNA College of Engineering and Technology, India
      3. Dr. Yuchou Chang, University of Wisconsin, United States
   9. Who are the major sponsors: Asian Society for Academic Research

5. IRF - International Conference on Computer Science, Machine Learning and Artificial Intelligence (ICCSMLAI - 2019), Chennai

1. About the conference: IRF - International Conference on Computer Science, Machine Learning and Artificial Intelligence, 2019 will be held in Chennai.
2. Event Date: June 30, 2019
3. Venue: Hotel Sonas Inn Chennai, 578, Anna Salai, Teynampet, Chennai, Tamil Nadu
4. Days of Program: 1
5. Timings: 9:00 AM - 5:00 PM
6. Purpose: The purpose of the program is to share and propagate ideas related to Machine Learning, Artificial Intelligence and Computer Science.  
7. Registration cost:

• • • Listeners - 2500 INR (Indian); $70 USD (International)
    • Authors - 
      • Academician - 7000 INR (Indian); $300 USD (International)
      • Student (Masters/PhD) - 6000 INR (Indian); $250 USD (International)
      • Student (Bachelors) - 5500 INR (Indian)l $200 USD (International)

      8. Who are the major sponsors: International Research Forum 

6. IRF - International Conference on Computer Science, Machine Learning and Artificial Intelligence (ICCSMLAI - 2019), Chennai

1. 1. 1. About the conference: IRF - International Conference on Computer Science, Machine Learning and Artificial Intelligence, 2019 will be held in Chennai. It will focus on the ideas in globally trending technologies in Computer Science, Machine Learning and Artificial Intelligence and many more.
      2. Event Date: July 07, 2019
      3. Venue: Hotel Sonas Inn Chennai, 578, Anna Salai, Teynampet, Chennai, Tamil Nadu
      4. Days of Program: 1
      5. Timings: 9:00 AM to 5:00 PM
      6. Purpose: The purpose of the program is to share and propagate ideas related to Machine Learning, Artificial Intelligence and Computer Science.  
      7. Registration cost: 

1. 1. 1. 1. Listeners - 2500 INR (Indian); $70 USD (International)
         2. Authors - 
            1. Academician - 7000 INR (Indian); $300 USD (International)
            2. Student (Masters/PhD) - 6000 INR (Indian); $250 USD (International)
            3. Student (Bachelors) - 5500 INR (Indian)l $200 USD (International)

                   8. Who are the major sponsors: International Research Forum 

7. The ASAR- International conference on Machine learning Big data management Cloud and Computing (ICMLBDMCC), Chennai

1. 1. About the conference: The ASAR conference will help in bringing new techniques in the field of Machine Learning. 
   2. Event Date: July 07, 2019
   3. Venue: Chennai, Tamil Nadu
   4. Days of Program: 1
   5. Timings: 9:30 AM - 5:00 PM
   6. Purpose: The purpose of the conference is to discuss the new techniques and horizons that will contribute to Machine learning, Big data management, Cloud and Computing in the next few years  
   7. How many speakers: 3
   8. Speakers & Profile: 
      1. Dr. P. Suresh, M.E, Ph.D., KCE, Coimbatore, India 
      2. Dr. Bommanna Kanagaraj, PSNA College of Engineering and Technology, India
      3. Dr. Yuchou Chang, University of Wisconsin, United States
   9. Who are the major sponsors: Asian Society for Academic Research

8. The ASAR- International conference on Machine learning Big data management Cloud and Computing (ICMLBDMCC), Chennai

1. 1. About the conference: Hosted by ASAR- India, the conference offers a track of quality R&D updates from key experts.
   2. Event Date: July 28, 2019
   3. Venue: Chennai, Tamil Nadu
   4. Days of Program: 1
   5. Timings: 9:30 AM - 5:00 PM
   6. Purpose: The purpose of the conference is to provide new opportunities and to develop the field of Machine Learning.  
   7. How many speakers: 3
   8. Speakers & Profile: 
      1. Dr. P. Suresh , M.E, Ph.D., KCE, Coimbatore, India 
      2. Dr. Bommanna Kanagaraj, PSNA College of Engineering and Technology, India
      3. Dr. Yuchou Chang, University of Wisconsin, United States
   9. Who are the major sponsors: Asian Society for Academic Research

1. AI, Deep Learning ET AL: The Change Agent, Chennai
   1. About the conference: The conference was held to envision, transform and sustain machine learning fundamentals. 
   2. Event Date: September 21, 2018 - September 22, 2018
   3. Venue: Hotel Hilton, Guindy, Chennai
   4. Days of Program: 2
   5. Timings: 8:30 AM - 5:30 PM
   6. Purpose: The purpose of the conference was to dive deep into the world of Artificial Intelligence to learn whether machines can mimic human minds. 
   7. How many speakers: 11
   8. Speakers & Profile:

• • • Dr Sibichen K Mathew, IRS, Commissioner of Income Tax, Govt. Of India
    • Mr. Abhigyan Arun, CEO of TNQ Technologies
    • Dr. Rajiv Raman, Sankar Nethralaya
    • Dr. R.R. Sudir, Medical Research Foundation, Sankar Nethralaya
    • Mr. GV Ananad Bhushan, Partner & Chennai Head, Shardul Amarchand Mangaldas & Co
    • Mr. Anantha Sayana, Chief Digital Officer, L&T
    • Mr. Pankaj Singh, Group Manager, Data Management and Quantitative Analysis (Machine Learning), BNY Mellon
    • Mr. AV Rangaramanujam, Senior Director, Insights and Data Practice, Capgemini
    • Ms. B Kalpana, Partner & Head, Robotics and Cognitive Automation, KPMG
    • Mr. N Murali, Founder, Hethi Technologies
    • Mr. Samrat Kishor, Blockchain Strategist from a leading management consulting company

9. Who were the major sponsors: 

• • • Indian Bank
    • Paramount
    • Bahwan Cybertek 
    • Fortinet
    • Sify
    • Tata Tele Business Services

2. Workshop on Machine Learning (MACHINE - 2018), Chennai

1. 1. About the conference: The workshop was conducted by Top Engineers. The workshop helped the students learn the basics of Machine Learning. 
   2. Event Date: July 15, 2018.
   3. Venue: IIT Madras Research Park, South Chennai
   4. Days of Program: 1
   5. Timings: 9:00 AM - 5:00 PM
   6. Purpose: The agenda of the workshop were to dive into the world of machine learning, give an introduction to Artificial Neural Networks, Binary Classification, Logistic Regressions and introduction to next level research scopes. 
   7. Registration cost: Rs. 900 per person (India); $50 USD per person (International)
   8. Who were the major sponsors: Top International Educational Trust

The roles and responsibilities of a Machine Learning Engineer include:

• Implementation of Machine Learning algorithms
• Conducting tests and experiments on data
• Creating deep learning and machine learning systems
• Clean data and identify patterns
• Perform exploratory data analyses

From cloud applications to wedding planners, Chennai is a successful playground for multiple startups. There are more than 1400 startups in Chennai. With startups like Freshdesk, Zoho, Chargebee, etc., Chennai has grown to become the SaaS hub in India. More data than ever is routinely generated by these firms, including large multinationals (MNCs), medium- and small-sized and ML engineers are needed to draw sense and value from a ton of data. 

Some of the companies hiring in Chennai are:

• Ford Global Business Services
• Bluescheme
• Contract Wrangler Inc.
• CareerXperts

The top professional groups for Machine Learning Engineer in Chennai:

• Chennai Women in Machine Learning and Data Science
• Chennai – Machine Learning for: Beginners to Experts
• Machine Learning and Data Science Using Python and R

In 2019, the following ML jobs are the most in demand:

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

Networking is very important. Your professional connections will help you with referrals that will eventually help you land a job. Here is how you can network with other machine learning engineers in Chennai:

• Social gatherings like meetups
• Online platforms like LinkedIn
• Machine Learning conferences

Average Infinity HR Machine Learning Engineer yearly pay in Chennai, Tamil Nadu is approximately Rs 10,00,000.

https://www.indeed.co.in/cmp/Infinity-HR/salaries/Machine-Learning-Engineer/Chennai-TN 

Python is a simple and user-friendly platform which allows engineers and coders to create and implement ML algorithms. Here is how one can get started with Python; 

• First, figure out the purpose of the program before you apply the ML concepts
• Next, download the Python SciPy Kit for Machine learning and install all useful packages that come with it
• Explore the different features of the tool, and gather insights into the elements available
• Insert and integrate the dataset and apply statistical summaries and data visualization to understand the structure and workings of the program
• Practice the common concepts and insights gathered from previous data sets to understand the algorithms better 
• One of the best tips we can offer is to start small and then work your way onto larger and more complex projects.
• The last step is to gather all this information you have thus gathered and then implement it on the ML projects. 

Thanks to the large and diverse open source community of Python and its libraries, there are some very useful libraries as below:

• Scikit-learn: Primarily for data mining, data analysis and in data science as well.
• Numpy: Useful due to its high performance when dealing with N-dimensional arrays.
• Pandas: Useful for high-level data structures and incredibly useful for data extraction and preparation.
• Matplotlib: In almost every ML problem, we need to plot a graph for better data representation, matplotlib helps in the plotting of graph in 2D.
• TensorFlow: If you are using deep learning in your project then this library, created by Google, is the go-to library as it uses multi-layered nodes which allow us to quickly train, setup and deploy artificial neural networks.

If you want your ML project to be produced effectively, we have listed out the steps for the same below:

1. Gather Data
2. Prepare the data and analyze it 
3. Visualize the data
4. Picking the right model 
5. Train and test the algorithm 
6. Adjust the different parameters 

To perfect Python, one has to understand the different aspects of the platform. Here are a few useful tips using which one can create a good algorithm program; 

• Reliable and consistent, start small with the basic coding practices and then work your way up to the desired conclusion 
• Learn to take notes, write out the algorithms manually, this gives you a better understanding of the program 
• Python is an interactive and intuitive platform which allows a lot of learning tools to use which one can code and create 
• Another aspect of the program is not just to design a strong algorithm, you also have to protect it from bugs and other online threats 
• Collaborate with other coders and ML students. One can not only develop their skills but also learn with fellow coders to understand how to execute a successful program 

 The best and most important Python libraries required for Machine Learning in 2019 are:

• Scikit-learn: Required in data mining, data analysis, and data science.
• SciPy: Used for manipulation of concepts of statistics, mathematics and engineering.
• Numpy: Used to perform fast vector and matrix operations efficiently.
• Keras: Required for dealing with neural network
• TensorFlow: Used to train, setup, and deploy artificial neural networks using multi-layered nodes.
• Pandas:  Used in data extraction and preparation through high-level data structures.
• Matplotlib: Helps in plotting 2D graphs for data visualization.
• Pytorch: Used for Natural Language Processing.