Conditions Apply

Machine Learning with Python Training in Delhi, 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

Live Online Classroom (Weekend)

Apr 11 - Jun 06 05:30 AM - 08:30 AM ( IST )

INR 42999

INR 36499

Live Online Classroom (Weekday)

Apr 13 - May 15 05:30 AM - 07:30 AM ( IST )

INR 42999

INR 36499

CITREP+ funding support is eligible for Singapore Citizens and Permanent Residents


Transformational advancements in technology in today’s world are making it possible for data scientists to develop machines that think for themselves. Based on complex algorithms that can glean information from data, today’s computers can use neural networks to mimic human brains, and make informed decisions based on the most likely scenarios. The immense possibilities that machine learning can unlock are fascinating, and with data exploding across all fields, it appears that in the near future Machine Learning will be the only viable alternative simply because there is nothing quite like it!

With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.

So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.

Our Machine learning course will help you to master this science and understand Machine Learning algorithms, which include Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:

  • The 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 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.

Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.

The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.

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

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

Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.

The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.

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

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

The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.

IBM’s Watson beat its human competitors at Jeopardy.

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

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

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

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

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

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 past search patterns 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 to identify clients with high-risk profiles.

  1. Government:

Machine learning is finding varied uses in running government initiatives. It helps in detecting fraud and minimizes identity theft. It’s also used to filter and identify citizen data.

  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


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

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.


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.

  • 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.

  • 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.

  • 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.

  • 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.

  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • KNN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study
  • 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

  • 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.

  • 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
  • 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.

  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study
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 


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 Delhi, India

Machine Learning is the field of science that uses the concepts of Artificial Intelligence to help the systems get the ability to learn, perform, and improve the given set of tasks. These systems do not require any human help or reprogramming. The area of Machine Learning focuses on developing computer programs and systems that will be able to access data, analyze and learn all on their own, without any human intervention.

When it comes to Machine learning, the processes that are used include observing the available data using examples or direct experience to derive information. The systems and programs then analyze this data to decipher the pattern. To help in making better decisions in the future, these observed patterns need to be extrapolated. All of this is done just on the basis of the datasets and the examples provided to the computer system.

There are several major algorithms in the field of Machine Learning that can be categorized into the following categories-

1. Supervised Machine Learning Algorithms

The supervised machine learning algorithms use labeled examples to take the information taken from the past data and apply them to the new data for predicting events in the future. This is how the whole process goes down:

  • First, a known dataset is provided to the system. The system then studies the data to train itself and learn from it.
  • Next, all the training and learning results in the derivation of a learning algorithm that makes predictions in the form of an inferred function.
  • Lastly, once sufficient training and learning have taken place, the learning algorithm is able to help us by getting the results for the new inputs.

2. Unsupervised Machine Learning Algorithms

The Unsupervised Machine Learning Algorithms are used when the data that is provided to the system for the learning and training of the system is unlabelled and unclassified. Here is how the unsupervised machine learning algorithms work:

  • Unsupervised learning systems are developed in such a way that they can use the unlabeled data to describe a hidden structure and infer a function.
  • These systems are unable to get the right results. However, they can draw inferences from the available data and identify and describe hidden structures from the available unlabeled data.

The vibrant city Delhi bustles with some of the best companies to work for and is home to several leading companies, such as Gaana, OLX, Indiamart, Lava, Samsung, etc. All these companies are looking for expert ML engineers to help them designing and developing Machine Learning Systems. 

The basic concept of Machine learning revolves around computers and data. 90% of the data on the internet has been created since 2016, according to an IBM Marketing Cloud study. A huge amount of this data is taken, analyzed and used in training systems to solve problems and obtain the best possible outcome for a problem. Basically, it solves a problem without us even knowing that it exists. It has a particular approach to a problem that helps in solving it. 

  •      It's easy and it works

It is no surprise that machines are faster than human problems. They can get a solution faster than we can understand the problem. For example, if there are a million options for a problem, a machine will be able to systematically resolve, evaluate, and work out all the options to give you the best possible result with the help of the right algorithm. 

  •     Being used in a wide range of applications today

Machine Learning has been used in several real-world applications. It has offered a solution to several problems. It helps save time, money and efforts by driving business. People are able to use Machine Learning to get the work done in a more appropriate, effective, and efficient manner. Many industries have started to incorporate Machine Learning and are benefitting from it. Some examples include transport, nursing, health care, banking, government institutions, finance, customer service, etc. 

Tons of data is generated every day. And now that we have started using data for decision making, it is changing everything we know. From small startups to large MNCs in Delhi, all the companies are trying to use the data for their own benefits. This data-driven decision making is reshaping the business and will continue to do so in the near future.

The state of Machine Learning in companies and in your daily life

Machine Learning is a new field of the tech world and still, a lot of research is required to harness the field. Over the years, tech experts have been trying to find ways to make use of Machine Learning. Social Media feeds shown by Facebook and Instagram, Product recommendations on Amazon, detection of financial fraud in banks, surge pricing in uber are some of the many functionalities of the Machine Learning algorithms. Day by day these systems are able to function with less human interference.

In some way or another, knowingly or unknowingly, every person is using one or the other product of Machine learning. It has become an inevitable part of every profession, especially the ones involved in the field of Data Science and Information Technology. 

Here are some of the benefits of Machine Learning that you should know - 

  1. It reels in better job opportunities: 

In a report published by Tractica, in the year 2016, the services driven by Artificial Intelligence were found to be of $1.9 billion worth. By the year 2025, this number is expected to increase to reach about $19.9 billion. Every corporation in the world is trying to use Machine Learning in their decision-making process. The domains of Machine Learning and Artificial Intelligence are expanding to every industry. This has led to more and better career opportunities in the present as well as future.

  1. Machine Learning engineers earn a pretty penny: 

Payscale published a report in which it stated that a machine learning engineer can earn up to an average of about Rs. 7,25,000 per year in Delhi. 

  1. Demand for Machine Learning skills is only increasing: 

Even though Machine Learning is in such a huge demand, there are not enough qualified Machine Learning engineers available. This has led to a huge gap between demand and availability. The Chief Information Officers (CIOs) of some huge corporations in the world have pointed out this skill gap. However, this also means that if you have Machine learning skills, not only will you be in demand but you would also be paid quite handsomely. And this demand is only going to increase in the future. There are several companies in Delhi that are hiring Machine Learning engineer including Genpact, Boston Consulting Group, Adobe, Accenture, American Express, dunnhumby, Ericsson-Worldwide, Amazon, VMware, Expedia Group, Oracle, Orange, Saavn, Telesoft Technologies, Hike, BlackRock, etc.

  1. Most of the industries are shifting to Machine Learning: 

Most of the industries around the world are dealing with more data that they can handle. And this production of data is only going to increase in the future. Companies are quick to get the benefits of data analysis. Through this, not only are they working competently and efficiently but they are also getting ahead of their competitors. 

If you want to work in the field of Machine Learning, the time is right since all the fields are looking for Machine Learning engineers. These fields include healthcare, finance, oil and gas, transportation, and government agencies.

Machine learning is a huge and diverse field that is expanding every day. There are a number of certification courses in Delhi that will help you learn Machine Learning including:

  1.    Madrid Software Technologies
  2.    Coding Ninjas
  3.    Inventateq
  4.    Aptron
  5.    Croma Campus

However, you can also learn the field through self-learning as long as you are motivated and keep the following in mind:

  • Hands-on learning will help you to learn practical skills and their implementation faster than textbooks, research papers and learning to derive proofs. This is an advantage over going for a college degree since all the theoretical experience will be a must for the curriculum. 
  • When you are building your profile as an ML engineer, you need to make sure that there are several real-world projects. This not only helps you test your skills and implement them, but they will also attract employers. You can also join various bootcamps in Delhi, offering Machine learning hands-on industry project experience.

Below are the steps you can follow:

  • Structured Plan: First things first, you need to make a structured plan describing the structured plan on the topics that you need to focus on first and topics that will be learned later.
  • Prerequisite: Next, you need to pick a programming language that you understand and are comfortable working in. Also, you need to revise your mathematical and statistical skills as you will be dealing with statistical data.
  • Learning: This step is to get started on learning according to the structured plan that you created in the first step. You can take the help of online sources or books and understand the flow of the Machine Learning algorithms.
  • Implementation: Lastly, you need to implement your skills otherwise your learning is of no use. Use the algorithms that you have learned to build a project. There are several datasets available online that you can use and start solving problems. Another great way to practice your skills is to participate in online competitions like Kaggle.

What is important is that you solve machine learning problems daily to polish your skills and incorporate an out-of-the-box thinking approach to a simple solution.

One of the best ways to get started with Machine Learning is to connect with other professionals. Here is a list of Machine Learning meetups in Delhi where you can connect with other Machine Learning Engineers:

  1. New Delhi – Deep Math Machine
  2. Artificial Intelligence
  3. Data Science Network – Delhi chapter
  4. Delhi Women in Machine Learning and Data Science
  5. StepUp Analytics Delhi

If you are an absolute beginner, here is a 5 step process to help you get started with Machine Learning-

  • Adjust your mindset:
    • The only thing holding you back from reaching your goals in Machine Learning is your own mindset.
    • You need to keep reminding yourself that Machine Learning is not as difficult as people say.
    • Remember that like every other field, the only way to understand Machine Learning is to keep practicing.
    • Find people in the field of Machine Learning that will help you in your journey to becoming a Machine Learning engineer.
  • Pick a process that suits you best: Next, you need to select a structured and systematic process that is according to your way of working through problems and finding solutions.
  • Pick a tool: Once you have selected the process, you need to select the tool that suits your comfort level with the concepts of Machine Learning. You need to map the tool onto your processes.
    • For the beginners, the recommended tool is Weka Workbench
    • Intermediate level learners can use the Python Ecosystem
    • Advanced level learners should go for the R Platform
  • Practice on Datasets: There are a huge number of datasets available online for you to work on. You can practice the entire data collection and manipulation process on these datasets.
    • Use the small, installed memory datasets to practice your own Machine Learning skills.
    • Make sure that the problems you are tackling are real-world problems connected to Machine Learning.
  • Build your own portfolio: Once you have the knowledge of the field, you can use a portfolio to demonstrate your skills to the employers.

In Delhi, companies like cube26, Pinnacle Digital Analytics, GoPaisa, Fitfyles, Sentieo, Ank Aha, Bobble App, iNICU Medical Private Limited, Saffron Consultancy Services, Wingify Software Pvt Ltd, Secninjaz, Fintech, Sumo Logic, Mobileum, ByteDance, Genpact, etc. are looking for Machine Learning professionals with suitable experience that will help the organization make crucial marketing decision. 

If you want to become a successful Machine Learning engineer working on developing successful machine learning projects, you need to have the following technical skill sets:

  • Programming languages: One of the most important prerequisites for Machine Learning is programming languages. You need to be familiar with programming languages like Java, Scala, Python, etc. before you start going through the concepts of Machine Learning. If you are not able to use the basics of programming languages, you won’t be able to grasp the machine learning concepts thoroughly. You must be familiar with programming concepts like processing data, formatting data formats, etc. to make the data compatible with the machine learning algorithm. These programming languages will come in handy when you are trying to master machine learning skills and their application.
  • Database skills: If you want to fully gauge and understand the concepts of machine learning, you need to have knowledge and experience in working with relational databases and SQL or MySQL. When you will be working on a real-world machine learning project, you will have to deal with different datasets obtained from different sources of data at the same time. As a programmer, you must be able to read this data and convert it into a format that is readable and compatible with the machine learning framework.
  • Machine Learning visualization tools: Visualization of data is as important as the analysis of data. There are several tools that can be used for data visualization. You need to have a basic understanding of these tools as these can be helpful while dealing with machine learning concepts.
  • Knowledge of Machine learning frameworks: While designing a machine learning model, you have to use statistical and mathematical algorithms. The model is then applied to current data to predict what would be the next. For this, you need to have the knowledge of certain frameworks like Apache Spark ML, R, ScalaNLP, TEnsorFlow, etc. It is a prerequisite for getting an in-depth knowledge of Machine Learning concepts.
  • Mathematical skills: It is only because of these mathematical concepts and algorithms that the data can be processed, analyzed and used to create the machine learning model. If you want to completely understand and implement the concepts and models of machine learning successfully, you need to make yourself an expert in the following concepts of mathematics:

    • Bayesian Modeling
    • Calculus
    • Calculus of variations
    • Differential equations
    • Fitting of a distribution
    • Graph theory
    • Hypothesis Testing
    • Optimization
    • Linear algebra
    • Mathematical statistics
    • Probability Distributions
    • Probability theory
    • Regression and Time Series
    • Statistics and Probability


The steps required for executing a successful Machine Learning project with Python are mentioned below:

  • Gathering data: The first and the most important step is to get the right data for your project upon which you will be applying your machine learning skills. The better the quality and quantity of data, the better the performance of the model will be.
  • Cleaning and preparing data: The data that will be provided to you is in a raw form. This data cannot be injected into the machine learning model. You need to clean the data to remove the unnecessary data and get the missing data. Next comes the preparation of data where you need feature engineering to convert the raw data to the data that our model expects. The data is divided into two parts – training and testing data.
  • Visualize the data: Data visualization is an important skill required for machine learning as you should be able to show the data and the correlation between the problems in a way that is clear and understandable to all the members of the team. It will also help you understand the kind of data you are dealing with and select the right model.
  • Choosing the correct model: Once you have visualized data and have good knowledge about how you can use this data, you need to select the best model or algorithm for it. This is a very important step as the type of model you choose will determine the performance of your algorithm.
  • Train and test: Once you have prepared the data and selected the model in which the data will be injected, you can train your model. Once the model is trained, it is tested using another dataset.
  • Adjust parameters: Once you get an accurate model, you can adjust the parameters to make it more accurate. For example in a neural network, you can change the number of neurons.

Once you have followed all the above-mentioned steps, you would have created and executed the machine learning project successfully.


Algorithms are one of the most integral parts of the Machine Learning field. It is very important that you completely understand the concepts of Machine Learning algorithms. Here is how you can do that-

  1. List the various Machine Learning algorithms: You need to make sure that you have listed down each algorithm with which you want to start learning. It is important to remember that every algorithm is unique and important for its own purpose. Once you have enlisted all the algorithms you want to learn in a file, categorize each algorithm. This will help you in building familiarity with different types and classes of algorithms.
  2. Apply the Machine Learning algorithms that you listed down: Machine Learning algorithms do not exist in isolation and no matter how many hours you spend on the theory of the machine learning algorithm, you won’t become an expert in it till you apply and implement it in a project with real-world datasets. So, along with learning the concepts and theory of machine learning algorithms, you also need to focus on Applied Machine Learning. Apply algorithms like Decision trees, Support Vector Machines, etc to different datasets and problems.
  3. Describe these Machine Learning algorithms: Once you have implemented the machine learning algorithms a couple of times, you need to gain an in-depth understanding of these algorithms. You need to explore these algorithms and analyze them to help you in building a description of these algorithms. As you discover more and more information on the algorithms during your course of study, keep adding them to your descriptions. This will lead to the creation of a mini-encyclopedia of your own about all the machine learning algorithms.
  4. Implement Machine Learning Algorithms: The most concrete way of learning how a machine learning algorithm works, is by implementing the algorithms into a project. When you implement a machine learning algorithm, you are able to better understand how the machine learning concepts are implemented in the algorithm. It will also help you understand the descriptions and mathematical extensions of the algorithm. You will have a thorough knowledge of how the algorithm works.
  5. Experiment on Machine Learning Algorithms: After you have understood and implemented the machine learning algorithms, you should go for experimenting. You can try standardizing datasets, studying the functioning of the algorithms, controlling variables, etc. once you understand how different parameters play in an algorithm, you can customize its workings according to your needs. You will be able to scale and adapt the problem to suit the needs of your project.

Machine Learning Algorithms

When you are a beginner in the field of Machine Learning, you need to understand the concept of K Nearest Neighbors algorithm. It is a simple and uncomplicated algorithm that will help you get started in the field of machine learning. The aim of the problem is to predict the class of a data point from a totally multiclass dataset using the K Nearest Neighbor algorithm.

  • The most important requirement of the nearest neighbor classification is defining a pre-defined number which will be stored as ‘k'. This number will define how many training samples are close to the new data point to be classified in terms of distance.
  • This new data point will be assigned a label. This label will be the one that has already been assigned to and defined by the neighbors.
  • The K-nearest neighbor classifiers will have a user-defined and fixed constant for the number of neighbors which need to be determined.
  • This algorithm works according to the radius based classification. The concept behind this is that the density of the neighboring data points is used to identify the samples and classify them under a fixed radius. This radius is the Euclidean distance between the two points.
  • The method of classifying the neighbors is also known as a non-generalizing machine learning method. Instead of acting on them, these methods remember the training data that was provided to it.
  • Once the vote is conducted among the neighbors of the unknown sample, classification is performed.

It is one of the simplest machine learning algorithms. When it comes to classification problems or problems with a huge number of regressions, K Nearest Neighbor algorithm has proven to be very useful and successful. It can be used for image analysis as well as character recognition.

Whether you need to learn machine learning algorithms or not depends on what you want to do with machine learning. 

  • If you just want to use machine learning algorithms, you don’t need to know any classic algorithms while studying machine learning. There are several courses that you can easily find online that offer a lot of knowledge on machine learning without introducing any classic algorithm.
  • If you are looking to innovate using machine learning, basic knowledge of algorithms is important. You will be designing a new algorithm, or adopting a new one. And to achieve this, you will need the knowledge and tools required for designing, adapting, and innovating using machine learning. You need to know the correctness of an algorithm, how complex it is, how much time it takes, what are the costs involved, etc. Only after you have sufficient knowledge of algorithms will you be able to experiment with the machine learning concepts.  

The Machine Learning Algorithms can be categorized into the following 3 categories - 

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

  • Supervised Learning: Supervised learning involves using historical, classified data to understand the mapping functions of the input variables (X) to the output variable (Y). Algorithms that follow supervised learning include-
    • Linear Regression: Linear regression algorithms follow the simple relationship between input variables (x) and output variable (y) in the form of the equation, y = a + bx
    • Logistic Regression: Similar to the linear regression, Logistic Regression is an algorithm whose outcome is not an exact value but is probabilistic. This probability is then converted into a binary classification using a transformation function.
    • CART: Classification and Regression Trees (CART) is an algorithm that implements decision trees. On the basis of defined branches and nodes, the possibility of each outcome is charted and the result is predicted. Each non-terminal node is a single input variable (x). This node is split into various outcomes that can happen to the variable. The following node is the output variable (y).
    • Naïve Bayes: This algorithm uses the basic value of a variable for predicting the outcome’s possibility. It works on the principle of Bayes theorem. The reason it is said naïve is that it assumes that all the variables are independent in nature.
    • K-Nearest Neighbours: In this algorithm, the entire data set is charted. Next, the value of ‘k’ is predefined to figure out the outcome for the variable’s value. Next, all the ‘k nearest instances’ are collected and then the average of them is used to produce the output. For most frequent class problems, the mode of the averages is produced as an output.
  • Unsupervised Learning: In this type of learning, only input variables are provided, not the output ones. To reveal possible clusters and associations, the underlying structure of the given dataset is analyzed. Following are the examples of such types of algorithms.
    • Apriori: In this algorithm, databases that contain transactions are used for identifying instances of two items occurring simultaneously or frequent associations. These associations are then used to predict future relationships.
    • K-Means: In this algorithm, similar data are grouped together into clusters and each data point present in the cluster is associated with the assumed centroid of the cluster. After this, the real centroid for each cluster is determined by ensuring that the distance between the centroid and the data point is the closest.
    • PCA: The Principal Component Analysis (PCA) is used for making the visualization of the data space easier by decreasing the number of variables. This is done by mapping each point's maximum variance to a new coordinate system. The axes in this new system correspond to the selected principal components. The principle of orthogonality ensures that no pair of components is related to others.
  • Ensemble Learning: Ensembles of learners are able to perform better than single learners. The ensemble learning algorithms use the results of each learner and combine them to get an accurate representation of the outcome. Here are some of the examples of ensemble learning algorithms.
    • Bagging: In this algorithm, multiple datasets are generated on the basis of the original one, and then on each dataset, the algorithm is modeled to get different outcomes. These results are then compiled together and performed upon to get the real outcome.
    • Boosting: Similar to the bagging algorithm, the only difference is that the boosting algorithm works sequentially instead of parallel. This means that every new dataset is created after learning from the errors and miscalculations of the previous dataset.

The simple machine learning algorithms can be used to solve the simple ML problems. An algorithm can be defined as simple if it has the following characteristics:

  • It is easy to understand.
  • It is easy to implement the algorithm and its underlying principles are easily understandable.
  • Less time and resources are required as compared to the high-level algorithms for training and testing the data.

Now, based on the above-mentioned criteria, the simplest algorithm in the field of machine learning is the k-nearest neighbor algorithm. Here are some reasons what makes the kNN algorithm the simplest machine learning algorithm and is still extensively used for solving basic, but real-life problems:

  • Being the simplest supervised learning algorithms, it is best for beginners.
  • kNN  is a classification algorithm that is used for regression as well.
  • It is non-parametric performs classification on the basis on a measure of similarity. 
  • Labeled data is used for the training phase is labeled data (supervised learning).
  • The algorithm predicts the object’s on the basis of its k nearest surroundings. K is the number of neighbors.
  • Some real-life examples where kNN is used are:
    • Recognizing vehicle number plate
    • Detecting patterns in credit card usage
    • Searching documents containing similar topics.

When it comes to machine learning, there are a lot of algorithms, models, and tools that you can select from. But before you select an algorithm that is the backbone of your project, you need to keep certain things in mind, including the following:

  • Understanding your data: The algorithm that you will be using depends on the data that has been provided to you. So, before you select the right algorithm, you need to understand your data. Here is how you can do it:
    • Use the plotting graphs to visualize the data
    • Find the correlation between the data. This will indicate the presence of strong relationships.
    • You need to clean the data. Find the missing data and remove the data that can be sensitive to your model.
    • Perform feature engineering on your data to make it ready for injecting it into your model.
  • Get the intuition about the task: Then, you need to understand what the aim of the task is and why machine learning is required to solve the problem. Once you understand that, you will need to know what type of learning will help you complete your task. This includes figuring out what kind of learning you have to use. There are 4 types of learning:
    • Supervised learning
    • Semi-supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • Understand your constraints: We can't always choose the best tool and algorithm for our project. Because for the best tools and algorithms, you require high-end machines with manipulation resources and high data storage.
    • The Data storage capacity will limit the data mount we can store for training and testing.
    • Hardware constraints allow us to select algorithms that work for use. For example, if you are a self-learning ML enthusiast you don't require an algorithm that needs a high computational power because it won't work on your low-end machines.
    • Time constraints help us decide if the training phase can be long or not. Because if it is, we need to narrow our model.
  • Find available algorithms: Once you have gone through the above phases, you can see which algorithm fits all out requirements and constraints and implement it.

To design and implement a machine learning algorithm using python, you need to follow the below mentioned steps:

  • Select a programming language: In this case, the programming language is python. You need a programming language that can influence the APIs and the standard libraries during the implementation.
  • Select the algorithm that you wish to implement: The next step is to select the algorithm. Be as precise as possible in choosing the algorithm. Decide on the type of algorithm, its classes and even the description and special implementation you are planning to do.
  • Select the problem you wish to work upon: Next step is to select the problem set that you are going to use for testing. Validate its efficiency and your algorithm’s implementation according to it.
  • Research the algorithm that you wish to implement: Research your algorithm which means go through multiple descriptions, implementation, and outlooks on the selected algorithm which will help you gain a perspective on the different methodologies of the algorithm. It will also help you overcome any wrong assumptions or roadblocks you might have regarding the algorithm.
  • Undertake unit testing: Last step is the development and running of unit tests for every single function of your algorithm. During the initial phases of development, consider the test-driven development aspect of your algorithm

All the basic concepts of machine learning are required for you to work on a machine learning project. Here we have narrowed down the most essential topics of machine learning that one needs to master to get thoroughly acquainted with machine learning:

  • Decision Trees: A classification problem for the supervised learning algorithm. It helps in deciding to choose which features to select and which conditions to use for splitting. It also helps the system to determine the conditions for ending a particular iteration and splitting. Here are the advantages of the decision tree method:

Advantages of decision tree methods:

    • It is simple, easy to understand, interpret and visualize
    • They can perform variable screening as well as feature selection
    • They are not affected by the non-linear relationship between the parameters
    • Minimal effort is required in data preparation
    • They can handle and analyze numerical as well as categorical data
    • They can handle problems which require multiple outputs
  • Support Vector Machines: These are the classification methodologies that offer high accuracy in the classification problem. They can be used for the regression problem as well. The benefits of this include:
    • They offer guaranteed optimality in their solutions. The solution that they provide is a global minimum, not a local minimum that guarantees its optimality.
    • They are used in linearly separable as well as non-linearly separable data.
    • Owing to the ‘Kernel Trick’ of the Support Vector Machines, feature mapping has become easy as they carry out the process using simple dot products.
  • Naive Bayes: This algorithm is a classification technique based on Bayes theorem. It assumes the independence between the different predictors. The advantages of Bayes theorem include:
    • It is a simple technique that involves performing a bunch of counts
    • It requires less training data
    • It is highly scalable
    • It converges quickly
  • Random Forest algorithm: It is a supervised learning algorithm that creates a decision trees’ forest and randomizes the input. This helps in preventing the system from identifying a pattern in the input data based on its order. These are trained through the bagging method.

Some of the advantages of this algorithm include:

    • Used for regression as well as classification.
    • Easy to use
    • Hyperparameters are easy to understand and are their count is not that high
    • Can produce good prediction result

Machine Learning Engineer Salary in Delhi, India

The median salary of Machine Learning Engineer in Delhi is ₹8,75,635/yr. The Range differs from ₹3,76,000 to as high as ₹11,60,000

The average salary of a machine learning engineer in Delhi compared with Bangalore is ₹8,75,635/yr whereas, in Bangalore, it’s ₹8,00,000/yr.

Cities near Delhi have reported an average salary of ₹6,50,000/yr for machine learning engineers. Although most of these cities have an average less than that of Delhi, Gurgaon has a high average of ₹10,10,000/yr.

According to a recent study done by Research and Markets, the global machine learning market is anticipated to grow from $1.4B in 2017 to $8.8B by 2022. It is also revealed that ML patents have seen a huge development of about 344% in the last 3 years. The majority of these licenses were under colossal tech organizations like Microsoft and Facebook who additionally have a base in New Delhi and are continually hoping to update themselves. Besides, New Delhi is itself, home to a few top tech organizations. These companies are looking for talented engineers who can utilize machine learning to deliver the best outcomes. So, yes, machine learning engineers are in high demand in Delhi.

Having the most attractive job among engineers in the 'National Capital of India' has its very own advantages

  • High payout - One of the most convincing inspirations driving its enthusiasm among engineers is, of course, better pay. 

  • Overwhelming Bonus - When factoring in bonuses and additional compensation, a Machine Learning Engineer can expect better as compared to peers.

Delhi offers endless opportunities due to the fact that it gives a massive exposure to all kinds of technology. Moreover, this allows the engineer to figure out where to go, what to use and how to deliver an apt result. So not only are you getting high package salaries, but there are added bonus, acknowledgement, networks and career stability.

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

  • Quantiphi
  • Tata Consultancy Services
  • Accenture
  • Microsoft
  • FactSet
  • Phenom People
  • OptiSol Business Solutions Private Limited

Machine Learning Conference in Delhi, India


Conference NameDateVenue

International Conference on Artificial Intelligence, Machine Learning and Big Data Engineering (ICAIMLBDE)

June 23rd, 2019

Hotel Suncourt Corporate, 6A/67, WEA, Channa Market, Karol Bagh, New Delhi,110005


International Conference on Robotics, Machine Learning and Artificial Intelligence (ICRMLAI)

June 23rd, 2019

Hotel Suncourt Corporate, 6A/67, WEA, Channa Market, Karol Bagh, New Delhi,110005


International Conference on Data Management, Analytics and Innovation - ICDMAI 2020

17-19 January, 2020

United Services Institute (USI) Rao Tula Ram Marg, Shankar Vihar, New Delhi, 110010

  1.  International Conference on Artificial Intelligence, Machine Learning and Big Data Engineering (ICAIMLBDE), Delhi
    1. About the conference: The conference contributes to knowledge in the fields of Artificial Intelligence, Machine Learning, and Big Data Engineering by providing opportunities to delegates from different areas to exchange ideas and application experiences.
    2. Event Date: June 23rd, 2019
    3. Venue: Hotel Suncourt Corporate, 6A/67, WEA, Channa Market, Karol Bagh,New Delhi,110005
    4. Days of Program: 1
    5. Purpose: The purpose of this conference is to provide a platform for delegates working in different areas to come together and exchange ideas and knowledge related to the latest research and innovations done in the fields of Artificial Intelligence, Machine Learning, and Big Data Engineering. 
    6. Registration cost: 
S.NoCategoriesRegistration Fee For Author outside of India

Registration Fee For Author of India


Authors (Academician/Practitioner)

300 USD9200 INR
2.Authors (Student USD7200 INR
3.Authors (B.Tech)200 USD6200 INR
70 USD
3000 INR
5.Additional Paper (s)**
100 USD
5000 INR
6.Additional Page
50 USD / One Page
1500 INR  / One Page
7.Extra Proceeding
100 USD

1000 INR

    7. Who are the major sponsors:

  • IRAJ Research Forum
  • BASE
  • IRAJ Explore
  • Open Academic Journals Index
  • Scholarsteer
  • Slideshare
  • DRJI
  1. International Conference on Robotics, Machine Learning and Artificial Intelligence (ICRMLAI), Delhi
  1. About the conference: This conference aims to contribute to the knowledge of robotics, machine learning, and Artificial Intelligence.
  2. Event Date: June 23rd, 2019
  3. Venue: Hotel Suncourt Corporate, 6A/67, WEA, Channa Market, Karol Bagh,New Delhi,110005
  4. Days of Program: 1
  5. Purpose: The aim of the conference is to provide a platform for scientists, engineers, scholar students, and researchers to come together and exchange their ideas, research results, innovations, and experiences about all aspects of Mechanical and Industrial Engineering, and highlight the problems faced and discuss the solutions to these issues.
  6. Registration cost:

Registration Fee For Author outside of India

Registration Fee For Author of India
1.Authors (Academician/Practitioner)
250 USD
9000 INR
2.Scholars (Ph.D./Post Doc.)
200 USD
7000 INR
3.Student(All Masters degree holders)
180 USD
6000 INR
4.Student(All Bachelors degree holders)
150 USD
5000 INR

   7. Who are the major sponsors:

  • WZB
  • Cite Factor
  • Springer
  • Elsevier
  • Google Scholar
  • Scholarsteer
  • Open Access Library
  1.  International Conference on Data Management, Analytics and Innovation - ICDMAI 2020, Delhi
    1. About the ICDMAI 2020 conference: The conference is held every year to make it an ideal platform for academicians, corporate executives, researchers, technocrats and experts from the field of Computer Science, Information Technology, Computational Engineering, Electronics and Telecommunication, Electrical, Computer Application, and all the relevant discipline for discussing and exploring the latest and upcoming advances in Analytics and Data Management.
    2. Event Date: 17-19 January, 2020
    3. Venue: United Services Institute (USI) Rao Tula Ram Marg, Shankar Vihar, New Delhi, 110010
    4. Days of Program: 3
    5. Purpose: The primary goal of the conference is the enhancement of data management and analytics through collaboration, innovative methodologies, and connections throughout the globe.
    6. Speakers & Profile:
      • Masood Parvania, Assistant Professor of Electrical and Computer Engineering with the University of Utah
      • Klaus McDonald-Maier, Professor, School of Computer Science and Electronic Engineering (CSEE), University of Essex, UK
      • Biswajit Patra, Director, Engineering & technologist, Intel India
      • Lipika Dey, Principal Scientist at Tata Consultancy Services, India
      • Aninda Bose, Senior Publishing Editor with Springer India Pvt. Ltd. 
      • Dinanath Kholkar, Vice President and Global Head of the Analytics & Insights unit of Tata Consultancy Services (TCS)
    7. Who are the major sponsors:
      • Springer
      • IBM
      • Wizer
      • Durgapur Society of Management Science
  1. Business Data Analytics & Data Mining, Delhi

    1. About the Business Data Analytics & Data Mining conference: It is a workshop on Business Analysis and covers topics like challenges in data analysis, statistical applications, and predictive analysis. 
    2. Event Date:  20 July 2019
    3. Venue: The Lalit New Delhi Barakhamba Avenue, Near Modern School, Connaught Place Fire Brigade Lane, Barakhamba New Delhi, Delhi 110001
    4. Days of Program: 1
    5. Timings: 9:00AM - 5:00PM
    6. Purpose: The purpose is to align the basics of statistics utility with business objectives by learning statistical concepts and developing skills for Predictive Modeling and Data pattern discovery.:
    7. Registration cost: ₹ 11,210
  1.  Pydata Delhi 2017, Delhi
    1. About the Pydata Delhi 2017 conference: The conference was an educational program of NumFOCUS, and it invited proposals on every aspect of data science including machine learning, big data, and artificial intelligence.
    2. Event Date: 2-3 September, 2017
    3. Venue: Indraprastha Institute of Information Technology Delhi, Okhla Industrial Estate, Phase III, Near Govind Puri Metro Station, Shyam Nagar, Okhla Industrial Area, New Delhi, Delhi 110020
    4. Days of Program: 2
    5. Timings: 8 A.M. - 5:45 P.M.
    6. Purpose: The purpose was to provide a platform for developers and users of data analysis tools to come together and share their ideas on the latest innovations, best practices, and challenges for data management, analytics, processing, and visualization.
    7. Speakers & Profile:
      • Siraj Raval, Data scientist & Youtube star
      • Prabhu Ramachandran, a faculty member at the Department of Aerospace Engineering, IIT Bombay
      • Farhat Habib, Senior Research Scientist at MARA Labs Inc.
      • Ponnurangam Kumaraguru, Associate Professor of Computer Science at IIIT Delhi
      • Anuj Gupta, senior ML researcher at Freshworks
    8. Who were the major sponsors:
      • Anaconda
      • Python software foundation
      • Fossee
      • Jet brains
      • Xebia
  2. International Data Science Summit, Delhi
    1. About the International Data Science Summit conference: The International Data Science Summit, organized by the Data Science Foundation and provided a platform to discuss the significance of data science and machine learning in decision making.
    2. Event Date: 19 Feb, 2018
    3. Venue: India Habitat Center, Lodhi Road Near Airforce Bal Bharati School Institutional Area Lodi Colony New Delhi Delhi 110003
    4. Days of Program: 1
    5. Timings: 09:00 AM-06:00 PM

Machine Learning Engineer Jobs in Delhi, India

The responsibilities of a Machine Learning Engineer include:

  • Researching and implementing appropriate ML algorithms and tools
  • Performing statistical analysis 
  • Running machine learning tests and experiments
  • Designing and developing Machine Learning Systems

Delhi is not only home to several leading tech companies but there are more than 5000 startups in the capital, including Snapdeal, Limetray, Hike, Ecom, Lenskart, etc. Delhi has been able to generate $2.8 Billion of funding during the first half of the previous financial year. It is a great time for being a Machine Learning Engineer in Delhi. As more and more companies these days are adopting artificial intelligence technologies, Machine learning engineers are in high demand.

  • Delhi AI / ML Group
  • Open Data AI-driven Innovations for India
  • Artificial Intelligence and Data Analytics Group (AIDAG)

Some of the ML job roles in demand are:

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

Below are some ways to network with other Machine Learning Engineers in Delhi:

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

The average salary for a Data Scientist with Machine Learning skills in New Delhi, Delhi is Rs 725,000.

Machine Learning with Python Delhi, India

Here's how you can start using Python for mastering Machine Learning:

  1. The first step is to believe you can apply ML concepts
  2. Download and Install Python SciPy Kit and install all the packages
  3. Take a tour to get the idea of all the available functions and their uses
  4. Load a dataset and understand its workings and structure using data visualization and statistical summaries
  5. Practice on some popular datasets to get an understanding of the ML concepts
  6. Start small and simple and work your way to complicated projects.
  7. All this knowledge and practice will help you get the confidence to go through your journey of using Python for mastering ML concepts.

The top essential Python libraries used to implement machine learning with python include:

  • Scikit-learn: Used for data analysis, data science, and data mining.
  • Numpy: Gives high performance with N-dimensional arrays.
  • Pandas: Useful for data extraction, preparation, and high-level data structures.
  • Matplotlib: Used for plotting graphs to better represent the data. It can plot graphs in 2D.
  • TensorFlow: It is the best library to use if you are using deep learning in your project. It will help you to train, setup, and deploy artificial neural networks.

Here we have compiled the steps required to execute a successful machine learning project using python:

  • Gathering data: The first step is to collect the data on which you will be performing the ML concepts. The better the quality of your data, the better the performance of your model will be.
  • Cleaning and preparing data: Once you have collected the data, you need to clean and prepare it to inject into the model because the data that we collect is in the raw form. This means that there can be missing data. In order to prepare the data, we use feature engineering in which the data is first converted into the data that our model is expecting and then it is divided into two parts – testing and training data.
  • Visualize the data: Next you need to visualize the data to show the prepared data and find the relation between the variables.
  • Choosing the correct model: Once you have a good knowledge of how the data can be harvested, you need to select the model and the algorithm suited for the data. This step will highly determine the performance of your project.
  • Train and test: Now, we need to train our model with the data and then once this is done, we have to test the accuracy of our model.
  • Adjust parameters: The last step is fine-tuning the parameters. Once you have found out your model's accuracy, you can adjust the parameters to make your model more accurate. For example, you can try changing the number of neurons in a neural network.

Here are the 6 best tips to learn python programming as a beginner:

  1. Consistency is Key: Practice every day. Commit to it. When it comes to programming, muscle memory plays an important role. Start small with coding for, say, 30 minutes each day and slowly increase your efforts.
  2. Write it out: Take notes from the beginning. It is the key to retention. If you are looking to become a full-time developer, you must write down important things by hand before you implement it on your computer.
  3. Go interactive!: The interactive Python shell is a great tool. You can learn about data structures of python like list, strings, dictionaries, etc. If you want to initialize the Python shell, just open a terminal, type in Python into the command line and press Enter.
  4. Assume the role of a Bug Bounty Hunter: When you are practicing programming, you will run into bugs. All you can do is sit down and solve each bug. It will frustrate you in the beginning but take it as a challenge and become the Bug Bounty Hunter.
  5. Surround yourself with other people who are learning: To many, coding is a solitary activity, but the best results are shown when it comes out in a collaborative manner. You need to socialize with people who are learning or working on Python. This will help you get useful tips and tricks.
  6. Opt for Pair programming: Pair programming is a technique where two programmers work on code together. One is the driver, the writer of the code, and the other is a navigator, who guides the process, gives feedback and confirms if the code is correct or not. This technique helps developers learn from each other and exposes them to multiple ideas and a fresh perspective on problem-solving and debugging.

Thanks to its big, open-source community; Python has several libraries for you to play with. Here are the best Python libraries essential for Machine Learning:

  • Scikit-learn: Used for data analysis, data science, and data mining.
  • SciPy: Contains packages for engineering, mathematics, and science (manipulation).
  • Numpy: Provides efficiency using free and fast vector and matrix operations.
  • Keras: It is used while dealing with neural networks.
  • TensorFlow: This package used multi-layered nodes for quick training, setup, and deployment of artificial neural networks.
  • Pandas: Offers high-level data structures useful in data extraction and preparation.
  • Matplotlib: Used in data visualization by plotting 2D graphs
  • Pytorch: It is used for Natural Language Processing.

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Goldina Wei

Java Developer
Attended Agile and Scrum workshop in May 2018
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The course materials were designed very well with all the instructions. The training session gave me a lot of exposure to industry relevant topics and helped me grow in my career.

Kayne Stewart slavsky

Project Manager
Attended PMP® Certification workshop in May 2018
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I really enjoyed the training session and am extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

Tilly Grigoletto

Solutions Architect.
Attended Agile and Scrum workshop in May 2018
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The workshop was practical with lots of hands on examples which has given me the confidence to do better in my job. I learned many things in that session with live examples. The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.

Marta Fitts

Network Engineer
Attended PMP® Certification workshop in May 2018
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The workshop held at KnowledgeHut last week was very interesting. I have never come across such workshops in my career. The course materials were designed very well with all the instructions were precise and comprehenisve. Thanks to KnowledgeHut. Looking forward to more such workshops.

Alexandr Waldroop

Data Architect.
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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This is a great course to invest in. The trainers are experienced, conduct the sessions with enthusiasm and ensure that participants are well prepared for the industry. I would like to thank my trainer for his guidance.

Barton Fonseka

Information Security Analyst.
Attended PMP® Certification workshop in May 2018


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.

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?

Machine Learning with Python Course in Delhi

Learn Machine Learning with Python in Delhi

Delhi is a delightful tapestry of medieval monuments, marvelous Mughal architecture, and busy old world bazaars that exist harmoniously alongside high rises and glitzy modern malls. As the capital city of India, in Delhi rests the seat of political and economic power. Delhi and the adjacent National Capital Region are the largest business and commercial center of northern India. This region has attracted both the best companies as well as great talent that come to the capital city looking to grow and prosper. However, the competition is tough and companies are spoilt for choice and will recruit only trained and certified candidates. As a result, candidates especially developers will benefit from pursuing e-learning programs in data analysis using Python course in Delhi. Attaining this certification will ensure that they remain highly employable and experience the rewards of career advancement. Made available by KnowledgeHut, this training is via online classes where programmers are given an exhaustive overview of both, theory and practical aspects of the subject. Python finds industry-wide acceptance and is a preferred language of many developers. Easy to learn and use, several organizations use this tool to build applications. However, to build a game or a web app, programmers need to enroll for formal training like the machine learning using Python course in Delhi to get a full grasp on the subject and have the ability to create effective applications or products.

Delhi is a central hub of technology and innovation, hosting both well established and many next-gen start-ups in the tech space. Pursuing online training for machine learning with Python is a good way to ensure that IT professionals have a chance at exploring great prospects in Information Technology domain, and many other such areas where such expertise is required. New Alternative Python is a powerful programming language used for creating several different applications. An open source language, this has a huge community that has created effective tools within the Python framework and over the last few years? specific tools have been developed for data science and analysis. Easy to install and use, this language can be deployed for application of any scale and size. It enables clear programs and emphasizes clarity of syntax along with easy comprehension and readability.

Keeping Ahead of the Curve

Taking up the Machine Learning with Python Course in Delhi is a remarkable way to stay ahead of the curve because this program ensures they have enhanced employment along with the potential of higher income. Developers can join the KnowledgeHut online classes, led by industry veterans who make sure that there is seamless knowledge transfer and the students build capability in this space.

KnowledgeHut Empowers You

The machine learning training using Python is available at a great price in Delhi. The online modules, conducted by KnowledgeHut provide superior training enabling developers to become highly proficient and can ace an exam with absolute ease.