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Machine Learning with Python Training in Pune, India

Know how Statistical Modeling relates to Machine Learning

  • 48 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

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

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

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.

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

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

View Profile

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 Pune, India

The aim of Machine Learning is to enable computer systems to perform tasks like locating data, analyzing them and learning from the data without any form of external help or intervention. The process of Machine Learning starts once the observations of data become available in the form of direct experiences or examples. The system looks at the provided data, tries to find patterns in them and then extrapolates these observed patterns in order to help in making decisions for the future. It takes into account all the information and datasets available to the program or system to make its decision.

There are several methods of Machine Learning. Broadly they can be categorized in to the following two categories-

  • Supervised Machine Learning Algorithm: These algorithms depend on old data already existing in the system to interpret the new data provided to them with the use of labeled examples to predict the events of the future.
    • A known dataset is provided to the system from which it trains and learns.
    • A learning algorithm derived from the training is then produced in the form of inferred function which is then used to make predictions from new data.
    • These inferred functions then provide us with results from new data after considerable training and learning.
  • Unsupervised Machine Learning Algorithm: These algorithms are used when the data required for learning by the system is either not labeled or has not been classified. 
    • Unsupervised learning systems have the ability to infer functions from unlabeled data to find hidden patterns. 
    • While such systems are unable to find the correct results, it can read available data and draw inferences from given datasets finding hidden patterns and rules from the unlabeled data.

The work of  Machine Learning is to take a huge amount of data, analyze it and find the best possible outcome for a task or hidden patterns in the data. This helps humans in working with large amount of data without actually knowing what it is about and finding solutions and patterns to this data without actually understanding why a certain solution works for a problem.

  • It’s easy and efficient

Machines and computers have the ability to work faster than the human brain by processing a huge number of data and working out a solution faster than the human brain can. For instance, if there are millions of options, possibilities and opinions, a machine can analyze all the possibilities, systematically working out and evaluating all possibilities and finding the best outcome or conclusion. 

  • It is used in a wide range of applications today

Machine Learning is the most practical solution to all our worldly problems and needs. It enables businesses to be more efficient by saving time, money and efforts. We can get more amount of work done with Machine Learning, making it a reliable, effective and appropriate system. Industries like health care, nursing, transport, governments and finance benefit from the developments in Machine Learning, making it an indispensable part of our society as of today.

Due to the social media boom, millions of data is being generated every day, helping companies understand consumer needs. At present, all companies- from startups to Fortune 500 are incorporating Machine Learning to make key decisions for their organizations. With data, big and small, being the basis of technological and industrial advancements, Machine Learning will remain an important sector for the next few decades, constantly reshaping itself to the market needs.

Importance and relevance of Machine Learning in industries and daily life

Machine Learning is rapidly becoming a popular technology among tech companies. From Facebook and Instagram feed to surge of price in Uber, Amazon recommendation to fraud detection, all kinds of functions are being performed purely through Machine Learning algorithms, without human intervention and minimal control.

More than half of the world’s population uses some device or another which deploys Machine Learning algorithms. It is inevitable that professionals in the field of Information Technology and Data Science take up Machine Learning programs to stay ahead and be relevant in society. Moreover, there is an increasing demand for professionals with Machine Learning knowledge in every industry with not enough Machine Learning experts. 

Listed below are the benefits of getting certified in Machine Learning-

  • Garner better job opportunities- Pune is home to Infosys, TCS, Tech Mahindra, Cognizant, Wipro, Amdocs, Accenture, etc. Machine Learning enables these organizations to become more efficient thereby getting more work done in the same amount of time. 
  • Overall increase in salary and remunerations- It is obvious when we think of it that with the highest grossing companies using Machine Learning tools, and limited number of Artificial Intelligence professionals out there, the net worth of such professionals will be much more than in any other job. Salaries according to the skills of data scientists in Pune are as follows:
    • Data analyst: Rs. 3,07,000
    • Python Developer: Rs. 4,09,045
    • Data Scientist: Rs. 7,59,095
  • Demand curve for Machine Learning experts is steadily going up and will keep going up- It is no surprise that the demand for Machine Learning professionals is increasing every day. The gap between the demand and availability of professionals is being lamented by the Chief Information Officers (CIO) of leading companies. AI is the future with enough job security that IT professionals should think of getting certified in Machine Learning soon. Pune is the eight largest city in India and one of its most important IT hubs. There are  several tech companies in Pune that are willing to pay handsomely to skilled Machine Learning professionals. These organizations include PubMatic, Aera Technology, Spatial Corp., Dassault Systems, Talentica Software India, Hella, Ideal Enterprises, Accenture, Druva, Suncore Microsystem, GridEdge Technologies, Infiniopes, DataMetica, Alpha Predictions LLP, Qualys, etc.
  • Industries from all sectors are slowly incorporating Machine Learning in their businesses- Availability of tons of free data at the disposal of companies makes it imperative to be used for business purposes. To harness that data and enable clever business venture through the analysis of this data can go a long way. Implementing Machine Learning to work efficiently and competently will put companies ahead of their competitors.

This provides an incentive to private and public sectors to incorporate Machine Learning. From information technology to health care industries or oil and gas mega corporations, every industry is gearing up to make use of the immense insight that Machine Learning provides.

Machine Learning is constantly evolving and developing itself everyday due to the immense amount of possibilities it offers. In Pune, there are several institutions that offer courses in Machine Learning including

While one can always opt for certified courses or diplomas, self-learning ML can also be equally effective if one has the motivation to keep working forward. The best way to learn Machine Learning is through a course. Few things to keep in mind are-

Machine Learning is a practical medium that requires hands-on experience rather than theoretical knowledge. While people should read up research papers or textbooks on ML in order to understand the concepts behind it, in real job situations, ML experts are hardly asked about their theoretical knowledge or their ability to derive proofs. Your ability will depend on your successful use and manipulation of available data for the development of a company.

The demand for an ML enthusiast in a company will increase based on the number of practical projects undertaken by the individual. Thus, the best way to increase your chances of getting employed is by taking up as many projects during your self learning days as you can. Projects provide employers with an idea about the kind of problems and coding structures a possible candidate has worked with giving them the idea of the employee’s experience.

Below are the steps you can follow:

  • Preparations- The best way to make the best use of your time is to read up on relevant documents and research papers on the subject and make a schedule of the important aspects that need to be learnt first and the specializations that you would want to delve further on to be learnt at a later stage.
  • Prerequisite- Decide on the programming language that you would like to learn and brush up on your statistical and mathematical skills since ML deals with statistical data.
  • Learning- Proceed with reading up on ML according to the schedule you created in Step 1 and make sure not to lag behind. Constantly refer to books and online materials from sites like Coursera or edX to clear any doubts that you might have. Understanding ML algorithm flow is a must for any ML enthusiast so make sure you have a clear idea about it.
  • Implementation of knowledge- No learning is complete without the successful implementation of what you have learnt in real life situations. ML is no different. Since the important aspect of ML is to understand and incorporate algorithms to understand data, it is important to apply these algorithms in projects. There are various online platforms which provide interesting datasets to test your skills on. Practicing on them will provide you with the ability of quick thinking and finding unusual, effective solutions to age old problems.

The most important aspect of grasping ML skills is practice and reworking on old projects again and again to attune your mindset to think up numerous situations and solutions to problems and pushing ourselves to think outside the box. Thus the proverb ‘practice makes perfect’ goes perfectly with learning ML.

The best recommendation anyone can give to beginners are the 5 point steps mentioned below-

  • Make up your mind: Understand what is holding you back in achieving your ML goals. Convince your brain that it is an easy subject matter and not as complicated as most people make it out to be. Realize that ML is like any other creative process where as you practice more the better you get.
  • Find your ML community: This means that you should look for a group of people or a community of ML enthusiasts whether online or offline who will help you and guide you when necessary in your ML journey. If you are a beginner in Machine learning, networking with other professionals will help you get a clear understanding of Machine learning concepts and the current trends. Here are a few meetups organized in Pune for Machine Learning professionals:
  • Find your rhythm and stick to it: Everybody has their own pace for learning as long as it is a structured and systematized plan of working through problems. So find your own pace and stick to it.
  • Pick a tool: Select a programming tool that corresponds well with your ML skills and include it in your schedule.
    • For beginners- Weka Workbench is the recommended tool
    • For intermediate learners, it is Python ecosystem
    • For advanced learners, R programming is the best tool to master
  • Practice, practice and more practice: With knowledge at our fingertips, it is unfair not to use it to our advantage. There are a number of datasets available in various platforms which should be used to practice data collection and manipulation. Depending on your skill in ML, you should get relatively small, installed in memory datasets to work on. Take note of the real world problems where ML is used the most and practice your skills of ML connected to those problems.
  • Create your portfolio: Once you are confident in your skills of ML, the next stage is to develop your portfolio which will demonstrate the kind of skills that you have picked up. 

Pune is a great place for machine learning engineers to work in. Some of the companies that employ machine learning professionals in Pune include LifeAI, Telstra, Credit Suisse, Velotio Technologies, Forgeahead, Maxmedia Developers, Zensar Technologies, Emblaze Training & Services, Acellere GmbH, Pratiti Technologies, Healthcoco Technologies, Foghorn Systems, etc. In order to consider yourself an ML expert, the following technical skills are mandatory to learn and imbibe in your projects-

  • Programming languages: One of the prerequisites of mastering ML skills is to have excellent knowledge of programming languages like Python, Java or Scala and so on. Having the ability to format data and processing it to make it compatible with ML algorithms are important skills to learn in order to incorporate them in real life situations. 
  • Database skills: Having the knowledge and expertise of working with MySQL will be a huge help considering that ML enthusiasts will need to work with a lot of unstructured data. And having an eye for finding relevant data from various sources and making them compatible with ML algorithms will be an important aspect of ML.
  • Machine learning Visualization tools: There are various tools available for visualizing data that are used in ML. Having the knowledge of using these tools will be expected of any ML expert.
  • Knowledge of Machine Learning Frameworks: There are various statistical and mathematical algorithms that are used in ML models to process and understand the data input and also to predict situations from given data set. For a complete ML experience, knowledge of frameworks like Apache Spark ML, ScalaNLP, R, TensorFlow, etc. are important.
  • Mathematical Skills: Mathematics is at the heart of Machine Learning. The mathematical algorithms are used to process, analyze and classify data that will maximize the utilization of data. The following list of mathematical concepts are essential for a student of Machine Learning.
    • Optimization
    • Linear algebra
    • Calculus of variations
    • Probability theory
    • Calculus
    • Bayesian Modeling
    • Fitting of a distribution
    • Probability Distributions
    • Hypothesis Testing
    • Regression and Time Series
    • Mathematical statistics
    • Statistics and Probability
    • Differential equations
    • Graph theory

Below are the steps required for executing a successful Machine Learning project with Python (ML Project)-

  1. Gather Data: The basic and the most important step is the gathering of data that is appropriate for a particular project. The quality and quantity of data will determine the performance of your ML model.
  2. Cleaning and preparing data: The data that is gathered is in raw form; which means that this data needs to be processed or cleaned by correcting the missing data and preparing it by specific feature engineering. This is done so that the data corresponds to the model of ML that is being used. The data is then divided into 2 parts: training data and testing data.
  3. Visualize the data: This is done to present the prepared data and find the correlations between the variables. Visualizing the data helps in understanding the data in our hands and eventually helping in selecting the correct model.
  4. Choosing the correct model: The kind of ML model that will be ideal for harvesting the given data is important as it determines the performance of the algorithm.
  5. Training and testing: The training data set is passed through the model to train it with the new set of data, then the accuracy of the model is checked by passing the test data.
  6. Adjust parameters: After determining the accuracy of the model, the parameters are fine tuned. One of the examples of that is changing the number of neurons in the neural networks.

Algorithms are an integral part of Machine Learning which makes it a necessary tool to understand, know and ingrain to create the best possible ML models. The following is a smart way of going about learning ML algorithms;

  1. Make a list of all ML algorithms: Every algorithm is useful and unique in its own way. Thus, it is important to list the algorithms you want to learn in the beginning of your Machine Learning journey. Writing down the general category under which an algorithm falls provides you with an idea of the different classes and types of algorithms available and prepares you for what lies ahead.
  2. Implement the Machine Learning algorithms you listed: Practicing the algorithms with different types of data provides you with the confidence to work in any given situation. Start building up an intuition for the various Machine Learning algorithms such as Support Vector Machines, decision trees etc. 
  3. Describe the Machine Learning algorithms: Once you are well accustomed to the various algorithms, the next step is to explore what you have already learnt. Being able to describe and analyze every algorithm will increase your knowledge of algorithms. Keep adding more and more information as you explore more information through the course of your study of Machine Learning algorithms.
  4. Use the Machine Learning algorithms: By implementing the algorithm yourself, you will be able to understand the micro decisions involved in the implementation of Machine Learning concepts. The implementation of algorithms also helps you get a feeling about the workings of an algorithm as well as understand the mathematical extensions and descriptions of the algorithm. 
  5. Experiment on Machine Learning algorithms: Once you have a strong grasp of the concepts of Machine Learning algorithms, it is time to experiment with them. Understanding the different parameters that are used during the working on an algorithm prepares you to customize the algorithms according to your needs in the future.

Machine Learning Algorithms

  • K-nearest neighbors (KNN): This algorithm usually has the user specify the value of K, and unlike other algorithms this trains the entire dataset. The aim of KNN is to predict outcome of new data instance. It trains to find either the K-nearest instance of the new data instance or the K number of instances that are most similar to the new instance. The prediction or output is one of two things;
    • The mode or the most frequent class in a classification problem
    • The means of the outcomes, in a regression problem.

The benefits of KNN is the ease of use and simplicity. Though it uses a lot of memory to store the large dataset, it calculates only when prediction is needed. 

  • Naїve Bayes: This uses the Bayes Theorem of probability to classify data. This helps calculate the probability of the occurrence of an event or a hypothesis being true on the basis of prior knowledge. This allows this algorithm to handle two types of probability:
    • Determining class
    • Determining the conditional probability of each class, on provided with X value

It is known to be ‘naїve’ because of its assumption that the variables are independent of each other, which is an improbable case in real life situations. This algorithm can be used on language-based content, like web pages and articles or smaller bodies of text like tweets or metadata for blogs. This algorithm is best for classifying content according to categories and effective in prediction disease development and location as well as analyzing human emotions. 

Your intention and future goals with Machine Learning determines the necessity of learning algorithms. If you simply want to use the existing Machine Learning algorithms without having any knowledge of classic algorithms, you can do so. There are various online courses on Machine Learning that do not teach algorithms with Machine Learning tools. However, if you want to bring innovations in the field of Machine Learning then the basic knowledge and workings of algorithms will be a prerequisite for you. As an innovator, it is your responsibility to find new and improved Machine Learning analyzing tools, you will need to have the knowledge of new algorithms as well as devise new algorithms of our own. This requires you to have a good grasp of the different aspects of algorithms and use that knowledge to devise your own.

Machine Learning algorithms are basically of three types:

  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: It includes the mapping function from the input variables (X) to the output variable (Y) by using categorically classified historical data.  
    • Linear Regression - The relationship between the input variables (x) and output variable (y) is expressed as an equation of the form y = a + bx
    • Logistic Regression - It is similar to linear regression model but in this you don’t get exact values but the regression is probabilistic. A Transformation function is then used for binary classification.
    • CART - An implementation of Decision Trees, Classification and Regression Trees (CART) helps chart the possibility of each outcome and provide the result based on the nodes and branches. 
    • Naïve Bayes - Given the basic value of some other variable, this algorithm predicts the possibility of an outcome happening. It works exactly on the principle of the Bayes theorem, and is considered “naive” as it makes the assumption that all variables are independent in nature.
    • K-Nearest Neighbours - This algorithm charts the entire data set given, collects “k nearest instances” of the value in the dataset after assigning a predefined value of “k” to find out the outcome for a given value of the variable.
  • Unsupervised Learning: It includes only the input variables and not the output ones. Hence, to reveal possible associations and clusters, the underlying structure of the given data sets is analysed. Examples of such algorithms include the following -
    • Apriori - This algorithm helps identify frequent associations or instances of two items occurring together and being used in various databases.
    • K-Means - This algorithm groups similar data into clusters, and then associates each data point in the cluster to an “assumed” centroid of the cluster. 
    • PCA - Principal Component Analysis (PCA) helps reduce the number of variables by making the data space easier to visualize. 
  • Ensemble Learning: Single learners are not able to perform as well as groups or ensembles of learners. It combines the results of each learner and then analyses them as one to get better representation of the actual result. Examples of such algorithms include the following:

    • Bagging - This algorithm helps generate multiple datasets followed by modeling the same algorithm on each to get distinct results, which can then be combined and performed upon to obtain the real outcome.
    • Boosting - This algorithm is same as above, but it works sequentially. Thus, each new dataset is created by learning from the previous one’s errors and miscalculations.

K-nearest neighbor algorithm is the simplest algorithm for beginners. It can be used for regression as well as classification. It is non-parametric and classifies based on the similarity measure. In this, labeled data is used for the training phase and the goal of the algorithm becomes predicting a class for an object based on its k nearest surroundings where k defines the number of neighbors. Some cases where it is used includes vehicle number plate recognition, identifying patterns in credit card usage, etc. 

Machine Learning is the most popular and heavily used system. Thus it has a lot of tools, algorithms and models to choose from. Having an understanding of selecting the right algorithm for your problem will determine the final quality of your project.

  • Understanding your data: Understanding the data well is important to decide which algorithm will be ideal. 
    • Plot graphs to visualize your data 
    • Find correlation among the data indicating strong relationships
    • Clean your data if you find any missing data or bad data
    • Prepare your data by feature engineering to make your data ready to be injected into your model
  • Get the intuition about your task: There are instances when the ultimate aim of a task is lost in the process and that is why ML is so important to solve the problem. After realizing the need of a process, you can decide the kind of learning required to complete the task at hand. There are 4 types of learning in general.
    • Supervised learning
    • Unsupervised learning
    • Semi-supervised learning
    • Reinforcement learning
  • Understand your constraints: A lot of times we overestimate ourselves and do not apply constraints to our planning and find the best tools and algorithms out there. This is not the right approach. Most of the best models and algorithms are for high end machines which require high level storage. Understanding hardware and software constraints thus become important.
  • Find available algorithms: Follow the above-mentioned steps to find an algorithm that adheres to the requirements and constraints and finally implement the algorithm. 

The more you practice and implement Machine Learning algorithms, the more efficient and faster will your solutions become. The process of implementation of ML algorithms are as follows;

  1. Selecting a programming language: Decide the type of programming language you will use for your data. The programming language will depend on your libraries and APIs that you are going to use for your implementation.
  2. Selecting algorithm to be used for implementation: Next step is to find the ideal algorithm that you are going to implement. Having a clear idea of all the steps and specifics will determine how smoothly your implementation will go. This means you should decide the type of algorithm, the classes as well as the specific implementation and description of what needs to be implemented.
  3. Selecting the problem you will be working on: Next is to select the canonical problem set that you would like to use to test and validate the efficiency and correctness of your algorithm implementation.
  4. Thorough research on the algorithm you wish to implement: Researching different, books, articles, blogs and so on about the algorithm you are about to implement will give you a complete idea about the uses and methodologies of the algorithm. This prepares you against possible roadblocks and mistakes that might happen during the process.
  5. Undertake unit testing: Develop and run unit tests for each and every function of your algorithm. You will also do good by considering the test driven development aspects of your algorithm from the very initial phases of development. This also enables and forces you to understand the expectations as well as the purpose of each unit of code of your algorithm.

Apart from having a good understanding of the basic concepts and systems of Machine Learning, below are some of the most essential topics of Machine Learning that you need to master:

  • Decision Trees: It is used to help identify the features and conditions to be used for splitting. It is used for classification problems and is a type of a supervised learning algorithm. It can not only handle issues that require multiple outputs but is able to handle and analyze both categorical as well as numerical data. It needs minimal efforts in the direction of data preparation from the user. 
  • Support Vector Machines: Support Vector Machines are a type of classification methodology but can also be used in problems of regression as well. They provide a higher degree of accuracy in classification problems. They can be used in both, Linearly Separable (also known as Hard margin) as well as Non-linearly separable (also known as Soft Margin) data.
  • Naive Bayes: Based on the Bayes’ theorem, it works on the assumption of independence between the different predictors. It is highly scalable and requires less training data as compared to other techniques used for classification.
  • Random Forest algorithm: The Random Forest algorithm is a very easy to use and handy algorithm. It is a supervised learning algorithm and is basically a collection of randomized decision trees that are trained with the bagging method. It can be used for both, regression as well as classification problems and tasks.

Machine Learning Engineer Salary in Pune, India

The median salary of a Machine Learning Engineer in Pune city is ₹6,50,000/yr. The range differs from ₹3,00,000 to as high as ₹17,00,000.

The average salary of a machine learning engineer in Pune city compared with Bangalore is ₹6,50,000/yr whereas, in Bangalore, it’s ₹8,00,000/yr.

Mumbai is located in Maharashtra and the average salary of machine learning engineers sums up to around ₹6,50,000/yr whereas in Pune it is ₹414,229/yr.

Pune is the second largest city of Maharashtra. It is not only blessed with natural beauty but is technologically advanced too. A lot of companies, especially those dealing with technology are established in Pune. These companies understand the value of AI and ML and therefore the demand for an ML engineer remains high. And various reports expect this growth to continue accelerating in the coming years due to the increasing use of AI in companies’ operations.

More people are getting interested in Machine Learning every day due to high demand and better pay. Some other advantages are-

  • High payout - With an average base salary of INR 7,50,000 in India and an impressive 344% growth in career opportunities, machine learning engineers are expected to continue on this growth track in the coming years.
  • Career stability - As more companies begin to integrate artificial intelligence (AI) and machine learning into everyday processes, machine learning is here to stay. 

Pune is known to provide huge encouragement to a wide range of innovation and technology. Machine learning has evolved quickly in the last few years with new languages, new frameworks, new techniques, and various new things to learn. And being a city of techies, it offers everything you can ask for if you are eager to learn.

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

  • Tata Consultancy Services
  • Intel Corporation
  • Infosys
  • Atos-Syntel
  • Ferromatik Milacron

Machine Learning Conference in Pune, India

S.NoConference Name DateVenue
1.IRAJ-International Conference on Smart Technology, Artificial Intelligence and Computer Engineering (ICSTAICE)16th June 2019Pune, Maharashtra
2.ISSRD- International Conference on Recent Developments in Computer & Information Technology19th June 2019Kapila Business Hotel, Pune, Maharashtra
3.ieeeforum-International Conference on Computer Science, Industrial Electronics (ICCSIE)30th June 2019Pune, Maharashtra
4.The ASAR- International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC)6th July 2019
Pune, Maharashtra
5.ISETE - International Conference on Artificial Intelligence, Machine Learning and Big Data Engineering (ICAIMLBDE)
21st July 2019
Pune, Maharashtra
6.Science Plus - International Conference on Robotics, Automation and Communication Engineering (ICRACE-2019) 
28th July 2019
Pune, Maharashtra
7.IRAJ-International Conference on Big Data, IoT, Cyber Security and Information Technology (ICBDICSIT)
11th August 2019
Pune, Maharashtra
8.Academics World 656th International Conference on Artificial Intelligence and Soft Computing (ICAISC)
24th August 2019
Pune, Maharashtra
  1. IRAJ-International Conference on Smart Technology, Artificial Intelligence and Computer Engineering (ICSTAICE), Pune
    1. About The Conference: A global platform for scientists to discuss their work on Smart Technology, AI and Computer Engineering 
    2. Event Date: 16th June, 2019
    3. Venue: Pune, Maharashtra
    4. Days of Program: 1 day
    5. Purpose: The conference aims to create a space that facilitates the exchange of ideas and encourages healthy discussions in the field of Data Science, Machine Learning and Artificial Intelligence.
    6. Sponsored By: IRAJ
  2. ISSRD- International Conference on Recent Developments in Computer & Information Technology, Pune
    1. About The Conference: ISSRD presents an international platform for scholars to share their findings on topics related to the IT sector 
    2. Event Date: 19th June, 2019
    3. Venue: Kapila Business Hotel, Pune, Maharashtra
    4. Days of Program: 1 day
    5. Purpose: The conference brings together some of the best minds in the country and also some well-known global figures in the IT sector to discuss innovations in the field of IT, such as Data Science, Machine Learning and Artificial Intelligence
    6. Sponsored By: ISSRD
  1. IRAJ-International Conference on Smart Technology, Artificial Intelligence and Computer Engineering (ICSTAICE), Pune
    1. About The Conference: The ICSTAICE conference aims to expand research on AI and Computer Engineering by collaborating with researchers and businessmen alike 
    2. Event Date: 22nd June, 2019
    3. Venue: Pune, Maharashtra
    4. Days of Program: 1 day
    5. Purpose: The conference creates a healthy and educational environment that encourages free exchange of ideas on Artificial Intelligence and its uses in the IT sector. 
    6. Sponsored By: IRAJ 
  1. IEEE forum-International Conference on Computer Science, Industrial Electronics (ICCSIE), Pune
    1. About The Conference: The ICCSIE conference is a collaboration of researchers, scientists, corporate houses and budding entrepreneurs to discuss Computer Science and Industrial Electronics 
    2. Event Date: 30th June, 2019
    3. Venue: Pune, Maharashtra
    4. Days of Program: 1 day
    5. Purpose: Registered participants will be allowed to present their research papers in front of an esteemed panel. The conference aims to provide an opportunity for a healthy exchange of ideas on the latest advancements in the IT field.  
    6. Sponsored By: IEEEForum 

5.The ASAR- International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC),  Pune

    1. About The Conference: Bring together the top-notch research scientists from across the globe to talk about Machine Learning, Cloud Computing, and Big Data Management 
    2. Event Date: 6th July, 2019 
    3. Venue: Pune, Maharashtra
    4. Days of Program: 1 day
    5. Purpose: The conference aims to widen the horizons of Machine Learning and Data Science by offering key updates and exclusive R&D data published in select research papers.  
    6. Sponsored By: ASAR
  1. ISETE - International Conference on Artificial Intelligence, Machine Learning and Big Data Engineering (ICAIMLBDE), Pune
    1. About The Conference: ICAIMLBDE is a platform that connects international academics and scholars to present their findings in the field of AI and ML 
    2. Event Date: 21th July, 2019
    3. Venue: Pune, Maharashtra
    4. Days of Program: 1 day 
    5. Purpose: The conference aims to bring together scholars and researchers for direct face-to-face interactions and establish business relations 
    6. Sponsored By: ISETE
  1. Science Plus - International Conference on Robotics, Automation and Communication Engineering (ICRACE-2019), Pune
    1. About The Conference: The ICRACE in association with IRAJ is giving young research scholars a platform to present their views on topics such as Robotics, Automation, and Communication Engineering 
    2. Event Date: 28th July, 2019
    3. Venue: Pune, Maharashtra
    4. Days of Program: 1 day
    5. Purpose: To create a holistic and healthy learning atmosphere where academics can discuss and present research papers on Machine learning and Robotics 
    6. Sponsored By: Science Plus 
  1. IRAJ-International Conference on Big Data, IoT, Cyber Security and Information Technology (ICBDICSIT), Pune
    1. About The Conference: ICBDICSIT is a world-class platform for research scholars and scientists to present their work in Big Data, ML, Cyber Security, IoT, and IT sectors.  
    2. Event Date: 11th August, 2019
    3. Venue: Pune, Maharashtra
    4. Days of Program: 1 day 
    5. Purpose: The conference aims to bring together international scholars to discuss the problems faced by companies in the IT sector and how to solve them.
    6. Sponsored By: IRAJ
  1. Conference Name: Academics World 656th International Conference on Artificial Intelligence and Soft Computing (ICAISC), Pune

    1. About The Conference: Assembly of researchers, upcoming scientists, and academicians to share their work on AI and Soft Computing and discuss the innovations and trends in the industry
    2. Event Date: 24th-25th August, 2019
    3. Venue: Pune, Maharashtra
    4. Days of Program: 1 day
    5. Purpose: an Interdisciplinary forum for researchers to talk about the latest trends in AI, the practical problems faced by companies and finding solutions to them
    6. Sponsored By: Academics World
S.NoConference NameDateVenue
1.India Analytics & Big Data Summit20-21st January 2017Royal Orchid Central Kalyani Nagar, Marisoft Annexe Building,  Pune, Maharashtra 411014

2.

UX Design Con6th May 2017Royal Orchid Central Kalyani Nagar, Marisoft Annexe Building,  Pune, Maharashtra 411014
3.DevOps Summit14th September 2017Royal Orchid Central Kalyani Nagar, Marisoft Annexe Building,  Pune, Maharashtra 411014
4.Seminar On Python Analytics (Data Analytics/ Machine Learning)
23rd Feb 2018
Learn Well Technocraft 203, Above Pizza Hut, Supreme Centre, ITI Rd, Near Parihar Chowk, Anand Park, Aundh, Pune, Maharashtra 411007, India
5.Pune Data Conference 2018
31st March 2018
The Westin Pune, Koregaon Park, Mundhwa Road, Pingale Wasti, Annexe, Ghorpadi, Pune
6.Pune DevCon 2018
8th December 2018
5thFloor, A - wing, MCCIA Trade Towers, International Convention Center, 403, Senapati Bapat Road, Pune- 411016.
7.IEEE SPS Winter School on Advances in Machine Learning and Visual Analytics for Forensic and Security Applications

10-14th December 2018

The Pride Hotel, Shivaji Nagar, Pune

  1. India Analytics & Big Data Summit, Pune
    1. About the conference: The India Analytics & Big Data Summit brought together academicians and entrepreneurs from the IT sector to exchange ideas and share innovations on Machine Learning.
    2. Event Date: 20-21st January, 2017
    3. Venue: Royal Orchid Central Kalyani Nagar, Marisoft Annexe Building,  Pune, Maharashtra 411014
    4. Days of Program: 2 days 
    5. Timings: 8:15am to 5:15pm 
    6. Purpose: Share ideas and findings with corporate houses and investors. 
    7. How many speakers: 8 speakers 
    8. Speakers & Profile:
      1. Atul Bengeri- Regional Manager - Strategic Projects & Smart Cities, Dell-EMC
      2. Aniruddha Pant- Founder And CEO, Algoanalytics Financial Consultancy Pvt Ltd
      3. Vinay Gupta- Head Data Analytics & Business Excellence, Suzlon Group
      4. Vijay Srinivas Agneeswaran- Director Of Technology, Sapientnitro
      5. Atul Khot- VP Engineering For Emerging Technologies, Webonise Lab
      6. Ghanasham Lavand- Founder, Lecapro.com
    9. Who were the major sponsors: Unicom 
  1. UX Design Con, Pune
    1. About the conference: UX Design Con was a collaboration of scientists, researchers, and business leaders to figure out solutions for coding programs and machine learning applications.
    2. Event Date: 6th May, 2017
    3. Venue: Royal Orchid Central Kalyani Nagar, Marisoft Annexe Building, Pune, Maharashtra 411014
    4. Days of Program: 1 day
    5. Timing: 8:45am to 5:15pm
    6. How many speakers: 8+ speakers
    7. Who were the major sponsors: Unicom 
  1. DevOps Summit, Pune
    1. About the conference: DevOps Summit brought together some of the best minds of the country to discuss ways in which the scope of the IT sector and Machine Learning can be expanded and optimized. 
    2. Event Date: 14th September, 2017
    3. Venue: Royal Orchid Central Kalyani Nagar, Marisoft Annexe Building,  Pune, Maharashtra 411014
    4. Days of Program: 1 day 
    5. Timings: 8:45am to 5:15pm
    6. Purpose: The conference aimed at creating a harmonious and healthy exchange of ideas for a smoother workflow and intuitive solutions.
    7. How many speakers: 8+ speakers 
    8. Who were the major sponsors: Unicom 
  1. Seminar On Python Analytics (Data Analytics/ Machine Learning), Pune
    1. About the conference: This seminar specially catered to researchers in the field of machine learning and data analytics. It discussed the basics and advanced concepts of the IT sector.  
    2. Event Date: 24th February, 2017
    3. Venue: Learn Well Technocraft 203, Above Pizza Hut, Supreme Centre, ITI Rd, Near Parihar Chowk, Anand Park, Aundh, Pune, Maharashtra 411007, India
    4. Days of Program: 1 day 
    5. Timings: 6:00am to 6:00pm
    6. Purpose: The seminar welcomed students, academicians, teachers, and businessmen to indulge in healthy conversations about Data Science and Machine Learning.  
    7. Who were the major sponsors: Learn Well Technocraft 
  1. Pune Data Conference 2018, Pune
    1. About the conference: It was one of the biggest data science conferences in Pune which brought together developers, tech enthusiasts, researchers, and businessmen to discuss the intricacies of Big Data, Data Mining, and Machine Learning 
    2. Event Date: 31st March, 2018
    3. Venue: The Westin Pune, Koregaon Park, Mundhwa Road, Pingale Wasti, Annexe, Ghorpadi, Pune
    4. Days of Program: 1 day
    5. Timings: 9:00am to 6:00pm 
    6. Who were the major sponsors: Clairvoyant India Pvt. Ltd. 
  1. Pune DevCon 2018, Pune
    1. About the conference: Pune Dev Con aimed at creating a holistic atmosphere for developers and scientists to meet up and discuss the different aspects of machine learning and coding.
    2. Event Date: 8th December, 2018
    3. Venue: 5th Floor, A - wing, MCCIA Trade Towers, International Convention Center, 403, Senapati Bapat Road, Pune- 411016.
    4. Days of Program: 1 day
    5. Timings: 9:30 am to 6 pm
    6. Purpose: Interactive sessions on Microsoft Azure, ML, AI, VSTS, DevOps, Windows, .NET, Visual Studio, Dockers and many more technologies in Microsoft and Open Source Family
    7. How many speakers: 25 speakers 
    8. Speakers & Profile:
      1. Aalok Singh- Cloud Consultant, Rapid Circle
      2. Aditee Rele- Director, Tech Solutions, Microsoft
      3. Amitabh Jain- Principal Architect, Icertis
      4. Ashvini Shahane- President – Learning Services, Synergetics Information technology Services India Pvt. Ltd
      5. Chandrashekhar Deshpande- Practice Head, Architecture Design Consultant, Trainer and Mentor, Synergetics Information technology Services India Pvt. Ltd
      6. Deepak Agarwal- Senior Software Analyst, Icertis
      7. Dhananjay Kumar- Developer Evangelist, Infragistics
      8. Gandhali Samant- Software Engineering Manager, Microsoft
      9. Garima Agrawal- Cloud Consultant, Rapid Circle
      10. Gouri Sohoni- Director, SSGS IT EDUCON Services Pvt. Ltd
      11. Kunal Chandratre- Cloud Solution Architect, Microsoft
      12. Mahesh Sabnis- Director, Mahesh IT Services
      13. Manish Prabhu- Director, Digital Security & Risk Engineering, Microsoft
      14. Mayur Tendulkar- Technology Specialist, Microsoft
      15. Monish Darda- Co-Founder & Chief Technology Officer, Icertis
      16. Nabajyoti Boruah- AVP Technology, Synergetics India Information Technology Pvt Ltd
      17. Nauzad Kapadia- Architect and Consultant, Quartz Systems
      18. Nish Anil- PM on .NET Team, Microsoft Corporation
    9. Registration cost: Free
    10. Who were the major sponsors: Pune DevCon 

7. IEEE SPS Winter School on Advances in Machine Learning and Visual Analytics for Forensic and Security Applications, Pune

    1. About the conference: The conference aimed at creating a healthy environment for sharing ideas on Machine Learning and Visual Analytics.  
    2. Event Date: 10th to 14th December 2018
    3. Venue: The Pride Hotel, Shivaji Nagar, Pune
    4. Days of Program: 4 days
    5. Purpose: The purpose of this conference was to bring together the best minds from around the world to discuss machine learning solutions for forensic and security companies. 
    6. Who were the major sponsors: IEEE SPS Winter School

Machine Learning Engineer Jobs in Pune, India

The responsibilities of a Machine Learning Engineer include:

  • Implementing suitable ML algorithms and tools
  • Defining validation strategies
  • Verifying data quality
  • Performing statistical analysis 
  • Designing software to automate predictive models

Pune is the second-largest city in the Indian state of Maharashtra, after Mumbai and is one of the leading IT services centers in India. There are several tech companies in Pune, including Cognizant, TCS, Tech Mahindra, Infosys, Wipro, etc. With more than 2000 startups, Pune is a developing hub for the growing Indian startup ecosystem. As companies are investing in machine learning and artificial intelligence, they’re looking for ML engineers to integrate these technologies into their business initiatives.

Some of the companies hiring in Pune are:

  • Globant
  • Accenture
  • Cummins Inc
  • Hitachi
  • Bajaj
  • ZS
  • Briq

Some of the ML job roles in demand are:

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

Here are some effective ways to network with other Machine Learning Engineers in Pune:

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

Machine Learning with Python Pune, India

Here's how you can get started on the use of Python for Machine Learning:

  1. Start with a positive mindset that you can apply Machine Learning Concepts.
  2. Download and install the Python SciPy Kit for Machine learning and install all useful packages.
  3. Get an idea of all the functionalities available and their uses by taking a tour of the tool.
  4. Load a dataset and make use of statistical summaries and data visualization in order to understand its structure and workings.
  5. Practice some of the used and popular datasets to gain a better understanding of the concepts.
  6. Start small and move to bigger and more complicated projects.
  7. Gathering all this knowledge will eventually give you the confidence of slowly embarking on your journey of applying Python for Machine Learning Projects.

Some of the essential Python libraries used to implement Machine Learning with Python are:

  • Scikit-learn: Data mining, data analysis and in data science as well.
  • Numpy: N-dimensional arrays
  • Pandas: High-level data structures, data extraction and preparation.
  • Matplotlib: Data representation, matplotlib.

If you want your ML project to be executed successfully, we have compiled the steps for the same below:

  1. Gather Data: The basic and the most important step is the gathering of data that is appropriate for a particular project. The quality and quantity of data will determine the performance of your ML model.
  2. Cleaning and preparing data: The data that is gathered is in raw form; which means that these data have to be processed or cleaned by correcting the missing data and preparing it by specific feature engineering. This is done so that the data corresponds to the model of ML that is being used. The data is then divided into 2 parts: training data and testing data.
  3. Visualize the data: Usually this is the final step. It is done to present the prepared data and find the correlations between the variables. Visualizing the data helps in understanding the data in our hands and that helps in selecting the correct model.
  4. Choosing the correct model: The kind of ML model that will be ideal for harvesting the given data is important as it determines the performance of the algorithm.
  5. Training and testing: After having decided on the model through which the processed data will be passed, the previous division of data is ready to be used. The training data set is passed through the model to train it with the new set of data, then the accuracy of the model is checked by passing the test data.
  6. Adjust parameters: After determining the accuracy of the model, the parameters are fine tuned. One of the examples of that is changing the number of neurons in the neural networks.

The following steps will make it easier for you to learn using Python programming. 

  1. Consistency is key: Practice everyday. Consistency is really important when you are starting out to learn programming language. Once you have the understanding of successfully solving algorithms, you should keep practicing it everyday. It might seem stressful but once you start working and practicing it won’t feel as tiring.
  2. Write it out: This might seem like an age old myth but writing and taking notes as you read is an excellent way to retain knowledge in our memory. The process of writing clears out a lot of concepts which might seem difficult to grasp or enables us to review our understanding of concepts. Writing out the code before implementing it will help beginners while working on the computer.
  3. Get interactive: The interactive Python shell is a really helpful learning tool whether you are writing codes or getting to know dictionaries, lists, strings or debugging an application. Just open your terminal and type Python/Python3 to initiate Python shell.
  4. Assume the role of a Bug Bounty Hunter: Make sure to solve the bug on your own if you come across any. Instead of getting frustrated, take this as a challenge. 
  5. Surround yourself with other people who are learning: Coding is best when done in a collaborative manner, so make sure to surround yourself with other people who are learning Python as this will not keep you motivated but will also help you receive helpful tips and tricks from other, along the way.

Below is a list of Python libraries which are best for machine learning:

  • Scikit-learn: Data mining, data analysis and in data science as well.
  • SciPy: Contains packages for Mathematics, engineering, and science (manipulation).
  • Numpy: Free and fast vector and matrix operations.
  • Keras: When one thinks Neural network, one goes to get the help of Keras.
  • TensorFlow: It uses multi-layered nodes which allow us to quickly train, setup and deploy artificial neural networks.
  • Pandas: Provides high-level data structures. Significantly helpful while during data extraction and preparation.
  • Matplotlib: It helps in the visualization of data by plotting of graph in 2D.
  • Pytorch: If NLP is our aim, Pytorch is our go-to library. 

reviews on our popular courses

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IT Manager.
Attended Agile and Scrum workshop in May 2018
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I am glad to have attended KnowledgeHut’s training program. Really I should thank my friend for referring me here. I was impressed with the trainer, explained advanced concepts deeply with better examples. Everything was well organized. I would like to refer some of their courses to my peers as well.

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Front End Developer
Attended PMP® Certification workshop in May 2018
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The trainer was really helpful and completed the syllabus on time and also provided live examples which helped me to remember the concepts. Now, I am in the process of completing the certification. Overall good experience.

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Data Quality Manager
Attended PMP® Certification workshop in May 2018
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The customer support was very interactive. The trainer took a practical session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

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The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He is very patient.

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Senior Engineer
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Knowledgehut is known for the best training. I came to know about Knowledgehut through one of my friends. I liked the way they have framed the entire course. During the course, I worked a lot on many projects and learned many things which will help me to enhance my career. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

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Junior Software Engineer
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The course materials were designed very well with all the instructions. The training session gave me a lot of exposure and various opportunities and helped me in growing my career.

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Project Manager
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I am really happy with the trainer because the training session went beyond expectation. Trainer has got in-depth knowledge and excellent communication skills. This training actually made me prepared for my future projects.

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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.

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 Pune

Machine Learning with Python Course in Pune has been making waves in the tech sector in recent times with the city combining great talent pool mirroring high standards of life. Quite a few companies have switched their branches and headquarters to Pune in recent years, so opportunities are galore in the city. KnowledgeHut has started Machine Learning with Python course in Pune, so as to feed the rising demand for data scientists. Data processing is turning out to be a lucrative option, and experts in the field are sure to have highly successful careers in the city. What is this course all about? Data processing and analysis are being done using python packages like Scipy, Pandas, and Matplotlib, owing to the ease of use and flexibility that Python provides. The course teaches its students about the usage of such packages and covers various other topics such as Regression Analysis, Decision Trees and Collaborative Filtering. Machine learning and data mining techniques which are in high demand are all given importance as part of the job-friendly curriculum of machine learning using Python course in Pune. Benefits of the course: Data scientists are in high demand with tech companies, as it can give a ready assessment of the market situation, and can even predict ups and downs in the future. Data analysis training using Python in Pune has the capability to open a range of career opportunities for students with the average salary of a data scientist on a higher scale compared to his counterparts. The KnowledgeHut Way: The main advantage of Knowledge Hut's machine learning training using Python is the flexibility associated with it; students can schedule it according to their priorities with ease. The tutor who is a subject matter expert in his chosen field will thus leave no stone unturned in ensuring that his students receive a real hands-on experience of all the principles discussed, during the training itself.