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Machine Learning with Python Training in Chennai, India

Know how Statistical Modeling relates to Machine Learning

  • 50 hours of Instructor led Training
  • Comprehensive Hands-on with Python
  • Covers Unsupervised learning algorithms such as K-means clustering techniques
  • Get introduced to deep learning techniques
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Description

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 solve data problems using major Machine Learning algorithms, which includes Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:

  • The basic concepts of the Python Programming language
  • About Python libraries (Scipy, Scikit-Learn, TensorFlow, Numpy, Pandas,)
  • The data structure of Python
  • Machine Learning Techniques
  • Basic Descriptive And Inferential Statistics before advancing to serious Machine learning development.
  • Different stages of Data Exploration/Cleaning/Preparation in Python

The Machine Learning Course with Python by KnowledgeHut is a 48 hour, instructor-led live training sessions course, with 80 hours of MCQs and assignments. It also includes 45 hours of hands-on practical session, along with 10 live projects.

Why Learn Machine Learning from Knowledgehut?

Our Machine Learning course with Python will help you get hands-on experience of the following:

  1. Learn to implement statistical operations in Excel.
  2. Get a taste of how to start work with data in Python.
  3. Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
  4. Learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies.
  5. Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering.
  6. Learn about Decision Trees for regression & classification problems through a real-life case study.
  7. Get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID.
  8. Learn the implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines.

What is Machine Learning?

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.

Practical Definition from Credible Sources:

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?”

Origin of Machine Learning through the years

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.

1950:
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.

1952:
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.

1957:
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.

1967:
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.

1981:
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.

1990:
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.

1997:
IBM’s Deep Blue beat the world champion in a game of chess.

2006:
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.

2010:
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.

2011:
IBM’s Watson beat its human competitors at Jeopardy.

2011:
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.

2012:
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.

2014:
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.

2015:
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.

2016:
An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.

How does Machine Learning work?

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.

Advantages of Machine Learning

  1. It is used in multifold applications such as financial and banking sectors, healthcare, publishing, retail, social media, etc.
  2. Machine learning can handle multi-variety and multi-dimensional data in an uncertain or dynamic environment.
  3. Machine learning algorithms are used by Facebook and Google to push advertisements which are based on the past search behaviour of a user.
  4. In large and complex process environments, Machine Learning has made tools available which provide continuous improvement in quality.
  5. Machine learning has reduced the time cycle and has led to the efficient utilization of resources.
  6. Source programs like Rapidminer have helped increase the usability of algorithms for numerous applications.    

Industries using Machine Learning

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.

  1. Financial services:

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 well as identify clients with high-risk profiles.

  1. Government:

Machine learning has various sources of data that can be drawn used for insights. It also helps in detecting fraud and minimizes identity theft.

  1. Health Care:

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.

  1. Oil and Gas:

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.

  1. Retail:

Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.

What is the future of Machine Learning?

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.

  1. Personalization algorithms of Machine Learning offer recommendations to users and attract them to complete certain actions. In future, the personalization algorithms will become more fine-tuned, which will result in more beneficial and successful experiences.
  2. With the increase in demand and usage for Machine Learning, the usage of Robots will increase as well.
  3. Improvements in unsupervised machine learning algorithms are likely to be observed in the coming years. These advancements will help you develop better algorithms, which will result in faster and more accurate machine learning predictions.
  4. Quantum machine learning algorithms hold the potential to transform the field of machine learning. If quantum computers integrate to Machine Learning, it will lead to faster processing of data. This will accelerate the ability to draw insights and synthesize information.

What You Will Learn

1. Statistical Learning

Understand the behavior of data as you build significant models

2. Python for Machine Learning

Learn about the various libraries offered by Python to manipulate, preprocess and visualize data

3. Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning.

4. Optimization Techniques

Learn to use optimization techniques to find the minimum error in your machine learning model

5. Machine Learning Algorithms

Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail

6. Dimensionality Reduction

Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction

7. Neural Networks

Understand Neural Network and apply them to classify image and perform sentiment analysis

8. Ensemble Learning

Learn to use multiple learning algorithms to obtain better predictive performance

PREREQUISITES

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.

3 Months FREE Access to all our E-learning courses when you buy any course with us

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Who Should Attend?

  • If you are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem
  • If you're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis and machine learning

Knowledgehut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Certification Process

Learn Machine Learning in Chennai
Machine Learning Algorithms
Machine Learning Engineer Salary in Chennai
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Machine Learning Engineer Jobs in Chennai
Machine Learning with Python Chennai

Curriculum

Learning Objectives:

In this module, you will visit the basics of statistics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range; and explore data and measures and simple graphics analyses.Through daily life examples, you will understand the basics of probability. Going further, you will learn about marginal probability and its importance with respect to data science. You will also get a grasp on Baye's theorem and conditional probability and learn about alternate and null hypotheses.

Topics:
  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
Hands-on :
Learn to implement statistical operations in Excel.
Learning Objectives:

In this module, you will get a taste of how to start work with data in Python. You will learn how to define variables, sets and conditional statements, the purpose of having functions and how to operate on files to read and write data in Python. Understand how to use Pandas, a must have package for anyone attempting data analysis in Python. Towards the end of the module, you will learn to visualization data using Python libraries like matplotlib, seaborn and ggplot.

Topics:
  • Python Overview
  • Pandas for pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Matplotlib & Seaborn for Data Visualization
  • Scikit Learn

Hands-on: No hands-on

Learning Objectives :

This module will take you through real-life examples of Machine Learning and how it affects society in multiple ways. You can explore many algorithms and models like Classification, Regression, and Clustering. You will also learn about Supervised vs Unsupervised Learning, and look into how Statistical Modeling relates to Machine Learning.

Topics:
  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting

Hands-on: No hands-on

Learning Objectives:

This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.

Topics:
  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques

Hands-on: No hands-on

Learning Objectives:

In this module you will learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies. It covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance.You will be able to grasp the concepts of Linear and Logistic Regression with real-life case studies. Through a case study on KNN Classification, you will learn how KNN can be used for a classification problem. You will further explore Naive Bayesian Classifiers through another case study, and also understand how Support Vector Machines can be used for a classification problem. The module also covers hyper-parameter tuning like regularization and a case study on SVM.

Topics:
  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • KNN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study
Hands-on:
  • With attributes describing various aspect 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.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
  • Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 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 and non-biodegradable.
Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering

Topics:
  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study
Hands-on :
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. 
Learning Objectives:

This module will teach you about Decision Trees for regression & classification problems through a real-life case study. You will get  knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID.The module covers basic ensemble techniques like averaging, weighted averaging & max-voting. You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting.
Going further, you will learn Random Forest with a real-life case study and learn how it helps avoid overfitting compared to decision trees.You will gain a deep understanding of the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It covers comprehensive techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. Finally, you will examine a case study on PCA/Factor Analysis.

Topics:
  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study
Hands-on:
  • Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
  • In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights &  better modeling.
Learning Objectives: 

This module helps you to understand hands-on implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines. The courseware covers concepts like cold-start problems.You will examine a real life case study on building a Recommendation Engine.

Topics:
  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study
Hands-on:
You do not need a market research team to know what your customers are willing to buy.  Netflix is an example of this, having successfully used recommender system to recommend movies to its viewers. Netflix has estimated, that its recommendation engine is worth a yearly $1 billion.
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users 

Meet your instructors

Become an Instructor
Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

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Projects

Predict Property Pricing using Linear Regression

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.

Classify good and bad customers for banks to decide on granting loans.

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.

Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM.

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

Read More

Cluster teen student into groups for targeted marketing campaigns using Kmeans Clustering.

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.

Read More

Predict quality of Wine

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).

Note: These were the projects undertaken by students from previous batches.  

Learn Machine Learning

Learn Machine Learning in Chennai, India

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

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.

Machine Learning Engineer Salary in Chennai, India

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

Machine Learning Conference in Chennai, India

S.NoConference NameDateVenue
1.

Workshop on Machine Learning (MACHINE-2019)

June 29, 2019

IIT Madras Research Park, South Chennai

2.

Machine Learning Workshop - Lema Labs

June 22, 2019 to June 23, 2019

IIT Madras Research Park, South Chennai

3.

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

June 23, 2019

Chennai, Tamil Nadu

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

June 26, 2019

Chennai, Tamil Nadu

5.

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

June 30, 2019

Hotel Sonas Inn Chennai

578, Anna Salai, Teynampet,

Chennai, Tamil Nadu

6.

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

July 07, 2019

Hotel Sonas Inn Chennai

578, Anna Salai, Teynampet,

Chennai, Tamil Nadu

7.

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

July 07, 2019

Chennai, Tamil Nadu

8.

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

July 28, 2019

Chennai, Tamil Nadu

  1. Workshop on Machine Learning (MACHINE - 2019), Chennai
    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
  1. Machine Learning Workshop - Lema Labs, Chennai
    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
  1. The ASAR- International conference on Machine learning Big data management Cloud and Computing (ICMLBDMCC), Chennai
    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
  1. The ASAR- International conference on Machine learning Big data management Cloud and Computing (ICMLBDMCC), Chennai
    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
  1. 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 

  1. 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. 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. 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 

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

    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
S.NoConference NameDateVenue
1.AI, Deep Learning ET AL: The Change AgentSeptember 21, 2018 - September 22, 2018Hotel Hilton, Guindy, Chennai
2.International Workshop on Machine Learning 2018July 15, 2018IIT Madras Research Park, South Chennai
  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
  1. Workshop on Machine Learning (MACHINE - 2018), Chennai

    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

Machine Learning Engineer Jobs in Chennai, India

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:

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/salaries/Machine-Learning-Engineer-Salaries-at-Infinity-HR,-Chennai-TN 

Machine Learning with Python Chennai, India

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.

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Faqs

The Course

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!

You will:
  • Get advanced knowledge on machine learning techniques using Python
  • Be proficient with frameworks like TensorFlow and Keras

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.

Finance Related

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.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

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.

Have More Questions?

Mail Us support@knowledgehut.com

Machine Learning with Python Course in Chennai

Chennai is South India’s educational,  economic, and cultural center.  One of the popular cosmopolitan cities in India, Chennai is a  tech haven with prestigious educational institutions and information technology companies.  Any good developer in Chennai would know the relevance of  Python for developing various applications. To help you grow your career in the city’s growing IT sector, KnowledgeHut offers data analysis training using Python in Chennai.

 A New Alternative

 KnowledgeHut’s Machine Learning with Python course in Chennai will familiarize you with design methodologies for working on data analysis and machine learning scenarios with various Python packages, such as Pandas,  Spicy, stats models to create solutions.  In this online training,  you will learn how to process,  manipulate, clean,  and analyze data with the help of relevant tools.  In the machine learning training using Python,  you will come across many real-life scenarios that will help reinforce the concepts taught in the online classes. You will get practical know-how of the applications of data analysis and machine learning using Python. The certified trainers delivering the machine learning using Python course in Chennai will also teach you the techniques for integrating Python with Hadoop.

 Keeping Ahead of the Curve

The course offered by KnowledgeHut covers all the essential topics required to learn machine learning using  Python. Some of the topics included in the data analysis using Python course in Chennai include design methodologies for creating data analysis solutions, including parallel processing, data classification, and clustering; an overview of data analysis frameworks; and more.  The training will also familiarize you with some application scenarios of data analysis.  This machine learning using Python course in  Chennai will help you get a deep understanding of the various Python packages that are useful for data analysis and machine learning.

Whether you are a programmer, professional software developer,  scientist,  analyst, or webmaster, you will benefit from this online course.  This training blends e-learning with hands-on training to integrate and reinforce the topics covered in the class.  By the end of the course, you will be able to make use of analytics libraries with  HIVE,  PIG,  and  Hadoop  MapReduce. You will get a course completion certification from KnowledgeHut at the end of training to validate your knowledge and skills in using Python for data analysis and machine learning.

 KnowledgeHut Empowers You

The high-quality courseware used for this Machine Learning with Python course in Chennai is designed by industry experts.  This  comprehensive  training  is  your stepping stone to taking your IT career to the next level.