Machine Learning with Python Training in Hyderabad, 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
Group Discount


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.

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 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


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 Hyderabad, India

Machine learning is the scientific study of algorithms that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. Machine Learning can be considered as a subset of Artificial Intelligence. It provides systems with the ability to automatically learn, perform and improve upon set tasks, without requiring reprogramming or human intervention in any form. The process of learning is done by observing past outcomes or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The algorithms adaptively improve their performance as the amount of data available for learning increases. Machine learning has become a key technique for solving problems in areas, such as:

  • Computational biology: tumor detection, DNA sequencing, and drug discovery
  • Image processing and computer vision: face recognition, object detection, and motion detection
  • Computational finance: algorithmic trading, credit scoring
  • Automotive, aerospace, and manufacturing: predictive maintenance

There exist several methods of Machine Learning. These can all be categorized into two categories as follows:

  • Supervised machine learning algorithms:  Supervised Learning is used when having known data for the output you are trying to predict. This algorithm takes a known set of input data and known outputs to the data and trains a model to generate reasonable predictions for the response to new data. Therefore, it builds a model that makes predictions based on evidence in the presence of uncertainty. Supervised learning uses classification and regression techniques to develop predictive models which are given as below:
  • Classification techniques: It is used to predict discrete responses if your data can be tagged, categorized, or separated into specific groups or classes. For example, applications for hand-writing recognition use classification to recognize letters and numbers, to check whether an email is genuine or spam, for medical imaging, speech recognition, and credit scoring. Common algorithms for performing classification include, support vector machine (SVM), neural networks, boosted and bagged decision trees, Naïve Bayes, k-nearest neighbor, logistic regression and discriminant analysis.
  • Regression techniques: It is used to predict continuous responses when working with a data range or if the nature of your response is a real number. Its applications include algorithmic trading and electricity load forecasting. Common regression algorithms include, linear model, nonlinear model, Regularization, stepwise regression, boosted and bagged decision trees, neural networks,  and adaptive neuro-fuzzy learning.
  • Unsupervised machine learning algorithms: It is used to draw insights from datasets consisting of input data without labeled responses. These algorithms are used for more complex processing tasks than supervised learning systems, including image recognition, speech-to-text, and natural language generation. They work by combing through millions of examples of training data and automatically identifying often subtle correlations between many variables. The algorithm once trained, can use its bank of associations to interpret new data. Clustering is the most common unsupervised learning technique and is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for cluster analysis include gene sequence analysis, object recognition, and market research. Common algorithms for performing clustering include:
    •  k-means and k-medoids
    •  hierarchical clustering
    •  Gaussian mixture models
    •  hidden Markov models
    •  self-organizing maps,
    •  fuzzy c-means clustering
    •  subtractive clustering.

Machine learning has applications in all types of industries, including manufacturing, retail,  travel and hospitality, healthcare and life sciences, energy, financial services, feedstock, and utilities. It is a way for humans to solve problems without actually knowing how to solve them or why a particular approach works by trying out every possible choice and doing it very fast. Its various applications determine its importance in our society. Some of these applications are as follows:

  • Financial Services:  The insights obtained from data science can identify investment opportunities, or help investors know when to trade. The two key purposes for which Banks and other businesses in the financial industry use machine learning  are:
    • to identify important insights in data
    • prevent fraud
  • Government: Analyzing sensor data identifies ways to increase efficiency and save money. Machine learning can also help to detect fraud. Government agencies such as public safety and utilities have multiple sources of data that can be mined for insights.
  • Healthcare: The various applications of machine learning in healthcare are as follows:
    • Drug discovery 
    • Identifying diseases and diagnoses
    • Personalized Medicine
    • Medical Imaging Diagnosis
    • Machine Learning-based Behavioral Modification
    • Smart Health Records
    • Crowdsourced Data Collection
    • Clinical Trial and Research
  • Transportation: The applications of machine learning in transportation can be listed as follows:
    • Air traffic control
    • Vehicle safety monitoring
    • Autonomous vehicle and connected car
    • Anomalous event detection from surveillance video
    • Object detection and traffic sign recognition
    • Vehicle safety monitoring
    • Mobility services for data-driven transit planning, operations, and reporting
    • Monitoring and managing transportation system performance
    • Freight transportation operations
    • Passenger safety monitoring
    • Efficient carpooling and ride sharing

Data is transforming everything we do. All organizations, from startups to tech giants to Fortune 500 corporations, are racing to harness the immense amounts of data generated unknowingly every day and put it to use for key decisions. Big and small data is reshaping technology and business as we know it and will continue to do so (in the near future at least). 

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

 Some of the benefits of learning Machine Learning include the following:

  1. Better career opportunities: Machine learning opens a world of opportunities to develop cutting edge machine learning applications in various verticals, such as image recognition, medicine, cybersecurity, and face recognition. Every customer-centric organization is looking to adopt machine learning technology. It is the next big thing paving opportunities for IT professionals. Machine learning algorithms have become the favorites of business and consumers so if you want to put yourselves somewhere in the upper echelon of software engineers then this is the best time to learn Machine Learning. Machine learning is the rave of the moment. There is a number of primary books, articles and online courses that can help you learn machine learning.
  2. Machine Learning engineers earn a pretty penny: Depending on the experience and skills, a machine learning engineer can earn from Rs. 6,00,000 to Rs. 16,00,000 per year in Hyderabad. Most data scientists have four-year bachelor's degrees, and common undergraduate majors include computer science, economics, mathematics, physics, and statistics. Senior data scientists may have advanced degrees in these fields. Salaries according to the skills of data scientists in Hyderabad are as follows:
    • Data analyst: Rs. 3,02,450
    • Python Developer: Rs. 6,85,324
    • Data Scientist: Rs. 7,29,973
  3. Demand for Machine Learning skills is only increasing: There exists a huge gap between the demand and the availability of Machine Learning engineers. Major hiring is happening in all top tech companies in search of skilled and talented machine learning engineers. The job market for machine learning engineers is not just hot but it’s sizzling. The number of open machine learning jobs have been steadily rising from 2014 to the onset of 2016, from 60 job postings per million to more than 100 according to the popular job portal Indeed. To start a career in Machine Learning, you must understand basics of Math, Algebra, and Statistics along with a comprehensive machine learning MOOC. According to a report by “A New York Time” as of October 2017, there are less than 10,000 people in the world that have the required background and skills necessary for AI-related jobs. Hyderabad is a hub to tech companies. And since Machine Learning is one of the hottest topics right now, there are several companies looking for hiring professionals including Amazon, Neustar, Phenom People, Apple, Cotiviti, PayPal, Accenture, SpringML, Lynk, Oracle, JP Morgan, Google, VMware, Sonetel, TCPWave, etc.
  4. Most of the industries are shifting to Machine Learning: If you are good at Machine Learning technologies, you can analyze tons of data, extract value and glean insights from it, and later make use of that information to train a machine learning model to predict results. Therefore, a Machine learning career brings you two benefits, one is for a machine learning engineer job and the other is for a data scientist job. Most industries in operation today are dealing with a humongous amount of data that is only increasing every single day. The benefits reaped by a thorough analysis of this data is a fact that companies are keen on using to their benefit. By gleaning insights from this data, companies are looking to work more efficiently and competently, as well as gaining an edge over their competitors in the market.

Machine learning is a huge field which is expanding day by day due to its diverse applications. So, it is important that you get the right guidance. There are several Institutes in Hyderabad that offer Data Science courses. Some of them are mentioned below:

  1. Top Data Science Training Institute in Hyderabad - Social Prachar
  2. Lucid IT Training
  3. Imarticus Learning
  4. Data Science Training in Hyderabad - Kelly Technologies
  5. Tech Marshals Academy

However, it is possible to learn machine learning on your own, but there are some necessary prerequisites:

  • Learn calculus: Calculus lays an integral role in many machine learning algorithms. Machine learning uses integral calculus in determining the probability of events. Some of the applications of calculus in machine learning can be listed as follows:
    • Gradient computation
    • Numerical optimizations
    • Bayesian functions using probability density functions
    • Variational inference 
    • Generative adversarial network
  • Learn to code: As a part of machine learning, you need to think statistically and speak the language of your data which is coding.  Machine Learning deals with the pre-defined algorithm where you need to code along with the additional support codes. The programming language you use for machine learning should consider your own requirements and predilections. The best programming languages for Machine Learning are as follows:
    • R
    • Python
    • MATLAB/ Octave
  • Learn Machine Learning: Most of the progress in machine learning over recent times has been in deep learning, but there’s much more to the field. There are also decision trees, linear regression, support vector machines, and a bunch of other techniques. You’ll get introduced to these as you progress, but you can probably learn them as they come up. Some of the online courses that you can refer to are as follows:
  • Build personal projects: After you have got knowledge of statistics and machine learning, you must try some hands-on. There are various datasets available for you to practice your skills. You can find large datasets open to the public like the following:

Learn a process for working a problem end-to-end, map that process onto a tool and practice the process on data in a targeted way. You can create a program of traits to study and learn about the algorithm you need to address them, by designing a program of test problem datasets to work through. To apply your knowledge, you can follow the below sequence:

    • Define the problem
    • Prepare data
    • Evaluate algorithms
    • Improve results
    • Write-up results
  • Advanced topics: Having knowledge of advanced topics in machine learning will help you stand out among the other machine learning enthusiasts. These topics might be difficult compared to other topics, and therefore might need more practice. But they come with benefits of increasing your chances of getting hired. Some of those areas are as follows:
    • graphical models
    • convex analysis
    • Bioinformatics
    • topics in information theory
    • kernel methods
    • reinforcement learning
    • Optimisation
    • minimal description length principle

When it comes to Machine Learning, you will need help from other professionals. And the best way to network with these professionals is through meetups. Here is the list of top machine learning meetups in Hyderabad:

  1.     Machine Learning. Do it right.
  2.     IoT + Machine Learning + Python
  3.     Hyderabad Machine Learning Foundation meetup
  4.     Hyderabad Machine Learning
  5.     Deep Learning Hyderabad

The best recommendation to get started on Machine Learning as a beginner includes a 5 step process, which goes as follows:

Step 1: Adjust Mindset. Firstly you need to have patience and believe you can practice and apply machine learning. You need to figure out answers for some questions like the ones listed, to clear the doubts in your mind:

  • What is holding you back from your Machine Learning goals?
  • Why Machine Learning does not have to be so hard

 Machine Learning is a subset of Artificial Intelligence, and it provides systems with the ability to automatically learn, perform and improve upon set tasks, without requiring reprogramming or human intervention in any form. Therefore, you need a firm understanding in statistics. The type of machine learning methods that you need will derive the relationship between the inputs and outputs in your historical data.This constitution allows you to understand what that real underlying yet unknown mapping function might look like and how factors like  noise, corruption, and sampling of your historical data may impact approximations of this mapping made by different modeling methods. Therefore, you need to change the way you think and thus your approach to any problem.

Step 2: Pick a Process: Use a systemic process to work through problems like the one given:

  • Define the Problem: Try to visualize the problem from different perspectives. You need to analyze and understand the problem thoroughly. You must clarify the answers to the following:
    • What is the problem?
    • Why does the problem need to be solved?
    • How would I solve the problem?
  • Prepare Data: You can start with data preparation with a data analysis phase that involves summarizing the attributes and visualizing them using scatter plots and histograms, or any other method that you are comfortable with. You should describe in detail each attribute and relationships between attributes. The actual process can be broken down into the following:
    • Data selection
    • Data preprocessing
    • Data transformation
  • Spot Check Algorithms: You can spot check algorithms, which means loading up a bunch of standard machine learning algorithms into your test harness and performing a formal experiment. You can typically run 10-20 standard algorithms from all the major algorithm families across all the transformed and scaled versions of the dataset that is available to you.
  • Improve Results: You can do this by an automated sensitivity analysis on the parameters of the top performing algorithms. The process of improving results involves the following:
    • Algorithm tuning
    • Ensemble methods
    • Extreme feature engineering
  • Present Results: The results of a complex machine learning problem should be put to work. In industry, it means a presentation to stakeholders. Even if it is a competition or a problem you are working on for yourself, it is good to go through presentation. It gives you checkpoints and helps you to summarize it.

Step 3: Pick a Tool. Choose a tool based on your level of understanding and knowledge and put it in action. Here is a sample of levels:

    • Beginners: Weka Workbench.
    • Intermediate: Python Ecosystem.
    • Advanced: R Platform.

Step 4: Practice on Datasets: Data Science projects are a great way to boost up your knowledge. You get a practical edge to the problems and their solutions, you can decipher how data science contributes in real time. It not only lets you apply your knowledge but also boost up your CV, hence increases your chances of getting hired. There are lots of datasets available online. Some of the websites where you can find free data sets are as follows: 

    • BuzzFeed
    • ProPublica
    • Socrata OpenData
    • AWS Public Data sets
    • Google Public Data sets
    • Wikipedia
    • Kaggle
    • UCI Machine Learning Repository
    • Quandl

    Step 5: Build a Portfolio: You should demonstrate your results and solutions in a simplified way.

    There are several organizations that are looking for skilled Machine Learning Professionals in Hyderabad including DataRobot, Salesforce, ZettaMine, AlienTT, Cyient, WaveLabs Technologies, Genpact, Creditvidya, ServiceNow, Wipro, Techshra, Xilinx, etc. These companies are willing to pay a handsome amount to deserving candidates. To get a job as a Machine Learning Engineer, you need to have an in-depth knowledge of ML. In order to thoroughly understand the concepts of Machine Learning and to develop successful Machine Learning projects, it is important to know the following:

    • Programming languages: Fundamentals of computer science such as data structures , algorithms, computation and complexity are really important in order to become a machine learning engineer. Skills in programming languages such as Python, C++,R,Java are high in demand.  A learner of Machine Learning must be able to implement these skills in a basic manner so as to be able to grasp Machine Learning skills more thoroughly. Knowledge of data formatting, data processing in order to make it compatible with the machine learning algorithm etc are also skills that will come in handy on.
    • Database skills: A prior knowledge and experience of working with MySQL as well as relational databases is also a prerequisite to fully gauging and understanding Machine Learning concepts. During the course of their Machine Learning journey, learners will have to make use of data sets obtained from various different data sources simultaneously. Programmers must be able to read data available at different sources, and then convert this data obtained in a format that is readable by as well as compatible with the machine learning framework that they are working on at the moment.
    • Machine Learning visualization tools: There exist several tools that are available for visualizing the data being used in Machine Learning. A basic knowledge and understanding of some of these tools will turn out to be helpful while you are applying the concepts of Machine Learning in real life. 
    • Knowledge of Machine learning frameworks: Several statistical, as well as mathematical algorithms, are made use of in order to design a Machine Learning model to learn from the input data and come to a prediction for a given data set. Knowledge of one or more of these frameworks including Apache Spark ML, ScalaNLP, R, TensorFlow etc. is a prerequisite for a thorough understanding of Machine Learning concepts.
    • Mathematical skills: Mathematics is at the heart of Machine Learning. This is because it is through these mathematical algorithms and concepts that the data is processed, analyzed and used in order to form a Machine Learning model. The following is a list of some of the mathematical concepts that a student of Machine learning must know in order to understand and implement the machine learning models and concepts successfully:

      • 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

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

    • Data collection: This is the first and most important step in executing machine learning.
      • Parsing highly-nested data structures such as those from XML or JSON files into a tabular form.
      • Automatically determining relevant attributes in a data string stored in a .csv file
      • Identifying and searching relevant data from external repositories.
    • Data Exploration and Profiling: You should assess the data after collecting it which includes looking for:
      • trends, 
      • outliers, 
      • exceptions, 
      • incorrect, inconsistent, missing, or skewed information.

    Data we have gathered is just raw data and is yet not ready to be injected directly into our model.  it is critical to be sure it does not contain unseen biases because your source data will inform all of your model’s findings.

    1. Formatting data to make it consistent: It is formatting the data in a way that best fits your machine learning model. If your data is taken from different sources or it has been updated by different people, there might be some anomalies like change in units, numbers, etc. The main goal is to  ensure that the entire data set uses the same input formatting protocols.
    2. Visualize the data: Sometimes this is the final step of the project, to just show the prepared data and find the correlation between the variables. Visualization helps in understanding the kind of data that we have in our hands and help to make a good selection of model accordingly.
    3. Choosing the correct model: After visualizing of data, we have a good knowledge about how this data can be harvested and which model or algorithm is best suited to do so. Choosing the correct model significantly determines the performance of your algorithm.
    4. Train and test: We have our prepared data ready to be injected into our chosen model. As in the earlier step we have divided our data into training and testing data, we now train our model with the training data and after it is trained, we test its accuracy with the test data in which it wasn’t trained.
    5. Adjust parameters: After finding how accurate your model is, we can fine tune our parameters. One example is changing the number of neurons in a neural network.

    After following all these steps, you would have successfully created and executed your machine learning project. 

    Algorithms form an integral part of the study of Machine Learning, which is why it is very vital for all learners of Machine Learning to know, understand and ingrain the concepts of Machine Learning algorithms. Here’s how to do that:

    • List the various Machine Learning algorithms: While each algorithm is unique and important in its own way, it is vital for you to decide and list down some algorithms that you wish to begin your Machine Learning journey with. Begin by making a text or doc file and enlist all the algorithms that you wish to learn. Also make sure that you list the general category that the particular algorithm falls under. This particular activity will also help you build familiarity with the various classes and types of algorithms that are available and prepare you for what lies ahead.
    • Apply the Machine Learning algorithms that you listed down: Remember that Machine Learning algorithms do not exist in isolation and no matter how much time you spend in learning the theory of Machine Learning algorithms, the best learning comes from the practical application and implementation of Machine Learning algorithms to data sets.Thus, apart from learning the basic concepts and theory of Machine Learning algorithms, it is also important to practice Applied Machine Learning.Start building up an intuition for the various Machine Learning algorithms such as Support Vector Machines, decision trees etc. Build confidence by applying these algorithms to various problems and data sets.
    • Describe these Machine Learning algorithms: The next logical step to be undertaken in order to gain a better understanding of Machine Learning algorithms is to explore what has already been understood about these algorithms. A thorough analysis and understanding of  Machine Learning algorithms will help you build up a description of these algorithms. Continue adding more and more information to these descriptions as you discover more information through the course of your study of Machine Learning algorithms. This is quite a valuable technique to help you build up a mini algorithm encyclopaedia of your own. 
    • Implement Machine Learning Algorithms: The implementation of Machine Learning algorithms is the most concrete way of learning the working of an algorithm. 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 help you get a feeling about the workings of an algorithm as well as understand the mathematical extensions and descriptions of the algorithm.
    • Experiment on Machine Learning Algorithms: Once you have implemented and understood a Machine Learning algorithm, you are well placed to experiment with an algorithm. You can now use standardized data sets, control variables as well as study the functioning of algorithms in the form of a complex system in itself. Understanding the parameters at play during the working on algorithm is a great way to be able to customize its working in order to suit your needs while working on a problem. Understanding the behaviour of an algorithm also enables you to better scale and adapt an algorithm to suit your problem needs in the future.

    Machine Learning Algorithms

    The K Nearest Neighbour algorithm is the simplest of all machine learning algorithms. It is a rather simplistic and uncomplicated Machine Learning algorithm. This number, k, defines the number of training samples that are closest in distance to a new data point that is to be classified. K-nearest neighbor classifiers possess a fixed user-defined constant for the number of neighbors which have to be determined. These algorithms work on the concept of radius based classification. The concept behind the radius based classification is that depending on the density of the neighbouring data points,all the samples are identified and classified under and inside a fixed radius. This fixed radius is a metric measure of the distances and is most popularly, the Euclidean distance between the points.

    Whether you need to know algorithms to learn machine learning depends on the following two things:

    • If you just want to use the Machine Learning algorithm, you don’t need to know any algorithms to learn Machine Learning. There are many courses available online that teach you Machine Learning without having to learn any algorithm.
    • If you want to use Machine Learning for innovating, you need to have a basic understanding of how algorithms are used. You need to have the required knowledge and tools to design a new algorithm or modify an old one. You will need this knowledge for adapting, designing, and innovating. You should have a thorough understanding of how correct an algorithm is, how much time it takes, what are the costs involved, how complex is it, etc.

    The top different types of Machine Learning Algorithms include:

    • Supervised Learning: In this kind of learning, one learns how to map function from the input variable (x) to the output variable (y) by using classified historical data.
      • Linear Regression - The equation y = a + bx can be used to express the relationship between the input variables (x) and output variable (y).
      • Logistic Regression – It is similar to the linear regression model except that the outcome is not the exact value, instead it is probabilistic. To force this probabilistic outcome into a binary classification, a transformation function is applied.
      • CART - Classification and Regression Trees (CART) involves implementing Decision Trees. In this algorithm, each outcome’s possibility is charted and based on the defined nodes and branches, result is predicted. Input variable (x) is each non-terminal node. The various possible outcomes are depicted by the node’s splitting point and the following leaf node is the output variable (y).
      • Naïve Bayes – In this algorithm, the possibility of an outcome is predicted. It works on the principle of Bayes theorem. The term “naïve” is used because in the algorithm, the assumption is made that all variables are independent in nature.
      • K-Nearest Neighbours – In this algorithm, the entire given dataset is charted. To find the outcome a variable’s value a predefined value to ‘k’ is assigned. Next, ‘k nearest instances’ are collected and then it is averaged to produce the output.
    • Unsupervised Learning: In these problems, output variables are not given, just the input variables. Possible clusters and associations are revealed by analyzing the underlying structure of a dataset. Some examples of such algorithms include:
      • Apriori – It is used for transactional databases where you have to identify frequent instances or associations of two items. This association is then used for predicting further relationships.
      • K-Means – It groups similar data into clusters and then each data point in the cluster is associated to the assumed centroid of the cluster. The real centroid is arrived by performing step’s iteration while making sure that the distance between the centroid and the data point is the closest.
    • Ensemble Learning: These algorithms combine the results of different learners and then analyze them to give an accurate representation of the outcome. Examples of such algorithms are:
      • Bagging – In this, multiple datasets are generated. Then on each dataset the same algorithm is modeled producing different outputs. These are then compiled together to obtain the real outcome.
      • Boosting – It is similar to bagging. However, it works sequentially as opposed to parallel working in bagging. In this way, each time a new data set is created, it has learned from last one’s miscalculations and errors.

    To be the simplest Machine Learning algorithm for beginners, an algorithm must fulfill the following criteria:

    • Easy to understand the algorithm and the underlying principles
    • Easy to implement
    • Takes less resources and time for training and testing the data

    Keeping this in mind, the simplest Machine Learning algorithm for beginners is k-nearest neighbor algorithm. Here are the reasons why this algorithm was chosen:

    • It is the simplest supervised learning algorithms for beginners
    • It can be used for classification as well as regression
    • It is non-parametric algorithm based on the similarity measure
    • Real-life examples where KNN is used include:
      • Searching documents containing similar topics.
      • Detecting patterns in credit card usage.
      • Recognizing vehicular number plate

    There are lots of tools, models and algorithms available in the field of Machine Learning to choose from. They are the backbone of your project and you don’t want to end up with the wrong one. Here are a few things that you should keep in mind while choosing the right ML algorithm:

    • Understanding your data: The first step towards selecting your ML algorithm is to understand your data. Here is what you need to do:
      • Plot graphs to visualize your data.
      • Find correlation among the data that will indicate strong relationships.
      • Find the missing data or remove the bad data that can be sensitive to your model.
      • Use feature engineering to prepare your data to be injected into the model.
    • Get the intuition about the task: You need to understand what the aim of the task is. Once you have figured that out, you need to find which of the following learning will help you achieve that task.
      • Supervised learning
      • Semi-supervised learning
      • Unsupervised learning
      • Reinforcement learning
    • Understand your constraints: You cannot always choose the best algorithm and model. You might not have the high-end machines required by algorithms that provide the required data manipulation and storage resources. Here are the constraints that you must lookout for:
      • You need to store data for training and testing phases.
      • Algorithms run according to the hardware they are provided. For example, a high-level ML algorithm would need high computational power that cannot be provided by low-end machines.
      • Before you select the model, you need to figure out if you can allow training phase to last for a long duration or not, and whether testing phase will be long or short.
    • Find available algorithms: Once you have figured out your requirements, select the algorithms that fulfill the requirements and constraints and implement it.

    Here is how you can design and implement a Machine Learning algorithm using python:

    1. Select a programming language: Select the programming language that you want to use for implementation. This will also affect the APIs and standard libraries that you will be using in your implementation.
    2. Select the algorithm that you wish to implement: The next step is figuring out what algorithm you will be implementing. You need to decide the type of algorithm you will be using, its classes, and the specific implementation and description of what you want.
    3. Select the problem you wish to work upon: Select the canonical problem on which you will test and validate your algorithm to test its correctness and efficiency.
    4. Research the algorithm that you wish to implement: Before you start the implementation, go through books, research papers, websites about the algorithm, how it can be implemented, its conceptual understanding, etc. This will give you a wider perspective of the different methodologies and uses of the algorithm.
    5. Undertake unit testing: For each function of the algorithm, start developing and running unit tests. This will help you understand what to expect and purpose from each code unit of the algorithm.

    While all the concepts of machine learning are important, here are a few topics of which you must have an in-depth knowledge:

    • Decision Trees: This is a supervised learning algorithm used for classification problems. Decision trees are used for deciding which conditions to use and features to select for splitting. They also determine the conditions required for ending iteration by stopping the splitting. The advantages of using the decision trees include:
      • Simple, easy to understand, interpret, and visualize.
      • Perform feature selection and variable screening.
      • Not affected by non linear relationships between parameters.
      • Require minimal efforts in the direction of data preparation from the user.
      • Handles and analyzes categorical as well as numerical data.
      • Handles problems that require multiple outputs.
    • Support Vector Machines: This classification methodology is known for providing high accuracy. It can be used in regression problems as well. Here are a few benefits of Support Vector Machines:
      • Because of its nature of convex optimization, it provides optimal solutions. The solution is a global minimum, instead of local minima.
      • Can be used in Linearly Separable as well as Non-linearly Separable data.
      • Owing to the Kernel Trick of support vector machines, feature mapping has become easy. It is used for carrying out feature mapping through simple dot products.
    • Naive Bayes: It is a classification technique, based on Bayes’ theorem that assumes the independence between variables. However, here are a few advantages of Native Bayes algorithm:
      • Simple technique that just involves counting
      • Requires less training data
      • Highly scalable
      • Converges quicker
    • Random Forest algorithm: The Random Forest is a collection of randomized decision trees trained using the bagging method. Some of the advantages of the Random Forest algorithm include:
      • Used for regression as well as classification problems
      • Easy to use and handy algorithm
      • Count of hyper parameters included in a random forest is not high
      • Produces a good prediction result

    Machine Learning Engineer Salary in Hyderabad, India

    The median salary of a Machine Learning Engineer in Hyderabad is ₹6,50,000/yr. The range differs from ₹1,77,000 to as high as ₹14,60,000.

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

    Hyderabad is considered to be among the top developing cities of India. Last year, it was listed in the list of top 10 fastest growing cities in the world. Since industries are realizing that Machine Learning is the future, they are heavily investing in it which is the reason why today ML engineering stands as the fastest growing sector. All these factors are evidence that ML engineering is in good demand.

    Hyderabad is moving towards being one of the finest cities in India. Why? Because of technological advancement. Machine learning engineers in Hyderabad enjoy a privileged life. How? Here are the reasons -

    • Acknowledgement - Everyone acknowledges knowledge. ML engineering is a dream job and rightly so, it does offer opportunities to look at the bigger picture. 
    • Salary - It is considered to be a high paying job and this is indubitably the major source of motivation for candidates.

    Other perks than the high paying salary include - 

    • Career growth
    • Promising future
    • Network
    • Large bonus and incentives

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

    • Accenture
    • Microsoft
    • Infosys
    • FactSet
    • Phenom People
    • Cognizant Technology Solutions
    • BITS

    Machine Learning Conference in Hyderabad, India


    Conference nameDateVenue

    International conference on computer science, machine learning, and artificial intelligence

    22nd and 25th June



    International Conference On Machine Learning Big Data Management Cloud And Computing

    23rd June

    Hampshire Plaza hotel 679 and 80, Lakdikapul, Hyderabad-500004


    International Conference on Computer Science, Machine Learning and Artificial Intelligence ICCSM LAI - 2019

    7th July

    Hampshire Plaza Hotel 679 and 80, Lakdikapul, Hyderabad-500004 India


    Intelligent Computing and Smart Communication Technologies

    26-27 July

    Anurag Group Of Institutions Hyderabad


    International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC)

    26th June

    Hampshire Plaza Hotel 679 and 80, Lakdikapul, Hyderabad-500004 India


    International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC)

    7th July

    Hampshire Plaza Hotel 679 and 80, Lakdikapul, Hyderabad-500004 India


    International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC)

    28th July

    Hampshire Plaza Hotel 679 and 80, Lakdikapul, Hyderabad-500004 India

    1. International Conference On Machine Learning Big Data Management Cloud And Computing (ICML DMCC), Hyderabad
      1. About the Conference: The conference aims to discuss applications and future challenges of machine learning.
      2. Event date: 23 June, 2019
      3. Venue: Hampshire Plaza Hotel, 679 and 80, Lakdikapul, Hyderabad-500004, India
      4. Days of program: 1
      5. Purpose: This conference offers a track of quality R and D updates from key experts and provides an opportunity in bringing the new techniques and horizons that will contribute to machine learning.
      6. With whom can you network in this conference: In this conference, you'll be able to network with like minded people from cloud computing and machine learning sector.
      7. Registration Fees: ₹1500
      8. Who are the major sponsors: ASAR
    2. International Conference on Computer Science, Machine Learning and Artificial Intelligence ICCSM LAI - 2019, Hyderabad
      1. About the conference: This Conference is organised to share the ideas in globally trending technologies, such as Computer Science, Machine Learning and Artificial Intelligence and many more.
      2. Event date: 7 July, 2019
      3. Venue: Hampshire Plaza Hotel, 679 and 80, Lakdikapul, Hyderabad-500004, India
      4. Days of program: 1
      5. Registration fees: ₹ 2500
    3. ICSC T2019: Intelligent Computing And Smart Communication Technologies (ICSCT), Hyderabad
      1. About the Conference: The conference covers topics such as computing technology, intelligent systems, machine learning, etc.
      2. Event date: 26-27 July, 2019
      3. Venue: Anurag Group of Institutions, Hyderabad
      4. Days of program: 2
      5. Purpose:  ICSCT 2019 will serve as a forum for the exchange of knowledge among academicians, scientists, researchers, and industry personnel from all over the globe in the areas of intelligent computing and smart communication technologies.
      6. Who can you network with in this conference: You can network with the highly qualified professionals of smart computing sector in this conference.
      7. Who are the major sponsors: Anurag Group Of Institutions.
    4. International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC), Hyderabad
      1. About the conference: The ICMBDC conference offers a track of quality R&D updates from key experts and provides an opportunity in bringing in the new techniques and horizons that will contribute to Machine learning, Big data management Cloud and Computing in the next few years.
      2. Event date: 26 June, 2019
      3. Venue: Hampshire Plaza Hotel, 679 and 80, Lakdikapul, Hyderabad-500004, India
      4. Number of days: 1
      5. Timings: 9:30 AM onwards
      6. Registration cost: ₹1500
      7. Major sponsors: Google Scholar
      8. With whom can you network: You can network with several Ph.D scholars and experts around the globe.
      9. Number of Speakers: 3
      10. Speakers :
        • Dr. P. Suresh, M.E, Ph.D., KCE ,Coimbatore, India 
        • Dr. Bommanna Kanagaraj, PSNA College of Engineering and Technology, India
        • Dr. Yuchou Chang, University of Wisconsin, United States
    5. International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC), Hyderabad
      1. About the conference: The conference aims to discuss the latest ideas that will contribute to Machine learning, Big data management, Cloud and Computing in the next few years
      2. Event date: 7 July, 2019
      3. Venue: Hampshire Plaza Hotel, 679 and 80, Lakdikapul, Hyderabad-500004, India
      4. Number of days: 1
      5. Timings: 9:30 AM onwards
      6. Registration cost: ₹1500
      7. Major sponsors: Google Scholar
      8. Number of Speakers: 3
      9. Speakers :
        • Dr. P. Suresh, M.E, Ph.D., KCE ,Coimbatore, India 
        • Dr. Bommanna Kanagaraj, PSNA College of Engineering and Technology, India
        • Dr. Yuchou Chang, University of Wisconsin, United States
    6. International conference on Machine Learning, Big Data Management and Cloud Computing (ICMBDC), Hyderabad
      1. About the conference: The conference explores updates from key experts that will contribute to Machine learning, Big data management, Cloud and Computing in the future.
      2. Event date: 28 July, 2019
      3. Venue: Hampshire Plaza Hotel, 679 and 80, Lakdikapul, Hyderabad-500004, India
      4. Number of days: 1
      5. Timings: 9:30 AM onwards
      6. Registration cost: ₹1500
      7. Major sponsors: Google Scholar
      8. Number of Speakers: 3
      9. Speakers & Profile:
        • Dr. P. Suresh, M.E, Ph.D., KCE, Coimbatore, India 
        • Dr. Bommanna Kanagaraj, PSNA College of Engineering and Technology, India
        • Dr. Yuchou Chang, University of Wisconsin, United States
    S.NoConference nameDateVenue

    International Conference On Computer Science, Machine Learning And Artificial Intelligence (ICCSMLAI- 2018)

    5th May

    Hampshire Plaza Hotel, 679 and 80, Lakdikapul, Hyderabad- 500004, India

    1.  International Conference On Computer Science, Machine Learning And Artificial Intelligence (ICCSMLAI - 2018), Hyderabad
      1. About the conference: International Conference on Computer Science, Machine Learning and Artificial Intelligence had a discussion on applications of Computer Science, Machine Learning and Artificial Intelligence and many more.
      2. Event date: 5 May, 2018
      3. Venue: Hampshire Plaza Hotel, 679 and 80, Lakdikapul, Hyderabad-500004, India
      4. No of days: 1
      5. Registration cost: ₹2500
      6. Who were the major sponsors: Google Scholar and EBSCO Information Service
      7. Speakers:
        • Prof. Jamal Ahmad Al-Matawah, The Public Authority For Applied Education And Training, Jordan
        • Prof. Ayyaswamy Kathirvel, Karpaga Vinayaga College of Engineering and Technology, India
        • Prof. Kiritkumar Modi, Ganpat University, Gujarat, India
        • Others who would present their papers

    Machine Learning Engineer Jobs in Hyderabad, India

    Machine Learning Engineers are in huge demand right now. They play a very important role in analyzing the data for providing insights. Their responsibilities include:

    • Applying Machine Learning Algorithms to the data
    • Exploring and visualizing data to gain an understanding of it
    • Conducting experiments and tests
    • Designing and developing systems required for data analysis.

    Hyderabad is a technical hub. It is home to several startups as well as large corporations. These companies are focusing towards data-driven decision making. To facilitate this, they need machine learning engineers to develop systems that can analyze the data and implement appropriate ML algorithms to gain insights.

    Some of the companies hiring in Hyderabad are:

    • Microsoft
    • Google
    • NeelBlue technologies
    • DataRobot
    • Onward Assist

    The most in-demand ML job roles in 2019 are:

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

    If you want to network with other Machine Learning Engineers in Hyderabad, you can try one of the following methods:

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

    Machine Learning Engineers manage to get a hefty salary in return for their services. In Hyderabad, the average salary of a machine learning engineer is Rs. 7,24,000.

    Machine Learning with Python Hyderabad, India

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

    1. The first step is downloading and installation of Python SciPy Kit for Machine learning and all its packages.
    2. Get familiar with its functionalities and their uses.
    3. Load a dataset. Understand the workings and structure using data visualization and statistical summaries.
    4. Use different datasets to understand the concepts of Machine Learning.
    5. Start with small and slowly move up to complicated projects.

    Here are the top essential python libraries used to implement machine learning with python:

    • Scikit-learn: Used for data science, data analysis, and data mining.
    • Numpy: Provides high performance with N-dimensional arrays.
    • Pandas: Used for high-level data structures, data extraction and preparation.
    • Matplotlib: Using graph for data representation.
    • TensorFlow: Allows quick training, setting-up, and deploying of artificial neural networks through multi-layered nodes.

    The steps required for executing a successful Machine Learning project with python includes:

    1. Gathering data: The first step is to collect the correct data. The better the quality of your data is, the better your model will perform.
    2. Cleaning and preparing data: Most of the data that you would have collected will be in unstructured form. Feature engineering is used to convert this data into a form expected by our ML model.Divide the data into two parts, one for training and the other for testing.
    3. Visualize the data: It helps in understanding what kind of data we are dealing with. Also, this will help us select the right model for the data.
    4. Choosing the correct model: Once you have an understanding of your data, you need to figure out which algorithm and model you should use. This step will drastically affect the performance of the algorithm.
    5. Train and test: Inject the data into the selected model. Use the training data to train the model. After that, testing data will be used to determine the accuracy.
    6. Adjust parameters: Fine tune the parameters depending on the accuracy. For example, changing the number of neurons in a neural network.

    The following are some tips to help you learn basic Python skills:

    1. Consistency is Key: Practice every day. When you start learning a new programming language, consistency is the key. It may seem difficult at first. Start with just 20 minutes a day and gradually increase your time.
    2.  Write it out: Take notes from the beginning. It has proven that writing down things helps in retaining them for a longer period of time. This is especially helpful if you plan on becoming a full time python developer.
    3. Go interactive!: The interactive Python shell will help you learn about Python data structures like strings, lists, dictionaries, list, strings etc or how to debug an application. Just fire up the terminal and enter python or python3 in the command line and hit enter.
    4.  Assume the role of a Bug Bounty Hunter: Running into bugs is inevitable. All you can do is just sit and debug your code. Take it as a challenge, instead of getting frustrated by it.
    5. Surround yourself with other people who are learning: Surround yourself with coders so that you get the motivation of continuing. Also, you will be able to learn helpful tips and tricks.
    6. Opt for Pair programming: In this type of programming, two developers work on the same code. One is the driver who writes the code. The other is the navigator who guides the process, provides feedback and reviews. This allows the developer to get a fresh perspective on problem solving, debugging or writing the code.

    The best Python libraries essential for Machine Learning in 2019 include:

    • Scikit-learn: Used for data science, data analysis, and data mining.
    • SciPy: Includes packages for Engineering, Science, and Mathematics.
    • Numpy: Offers free and fast vector and matrix operations.
    • Keras: Used for Neural network.
    • TensorFlow: Used for training, setting-up, and deploying artificial neural networks using multi-layered nodes.
    • Pandas: Offers high-level data structures used for extracting and preparing data.
    • Matplotlib: Provides data visualization in 2D.
    • Pytorch: Go-to library for NLP. 

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    Attended PMP® Certification workshop in May 2018
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    Overall, the training session at KnowledgeHut was a great experience. Learnt many things, it is the best training institution which I believe. My trainer covered all the topics with live examples. Really, the training session was worth spending.

    Lauritz Behan

    Computer Network Architect.
    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.

    Yancey Rosenkrantz

    Senior Network System Administrator
    Attended Agile and Scrum workshop in May 2018
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    I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and liked the way of teaching. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

    Ike Cabilio

    Web Developer.
    Attended Certified ScrumMaster®(CSM) workshop in May 2018
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    The course material was designed very well. It was one of the best workshops I have ever seen in my career. Knowledgehut is a great place to learn and earn new skills. The certificate which I have received after my course helped me get a great job offer. Totally, the training session was worth investing.

    Hillie Takata

    Senior Systems Software Enginee
    Attended Agile and Scrum workshop in May 2018
    Review image

    Knowledgehut is the best platform to gather new skills. Customer support here is really good. The trainer was very well experienced, helped me in clearing the doubts clearly with examples.

    Goldina Wei

    Java Developer
    Attended Agile and Scrum workshop in May 2018
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    My special thanks to the trainer for his dedication, learned many things from him. I liked the way they supported me until I get certified. I would like to extend my appreciation for the support given throughout the training.

    Prisca Bock

    Cloud Consultant
    Attended Certified ScrumMaster®(CSM) workshop in May 2018
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    I was totally surprised by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. Training session changed my way of life.

    Matteo Vanderlaan

    System Architect
    Attended Agile and Scrum 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 Hyderabad

    Machine Learning using Python Course in Hyderabad

    One of the most popular cities for technology in India, Hyderabad takes pride in housing some of the biggest tech giants engaged in the development of multiple technologies. KnowledgeHut provides an e-learning option for the development of software products as well as improve on existing ones. One of the most popular online courses is machine learning training using Python. The course helps one understand the intermediate level of the course and its methodologies.

    What is the course about?

    The Machine Learning with Python course in Hyderabad trains you to develop syntax and simplify the data for readability and comprehension. You will learn advanced data structures as well as its algorithms in Numpy. Also, you will be able to plot using Matplotlib and create classifiers and clusters. Benefits of the course: There are plenty of other benefits that come with taking the machine learning using python course in Hyderabad. You can benefit from the lectures delivered by some of the best tutors. Also, the data analysis training using python in Hyderabad comes with a workshop where you can benefit from an extra edge over others who haven't signed up for this course.

    The KnowledgeHut Way:

    E-learning courses of KnowledgeHut come at a nominal fee which you can learn everything online and at your convenience. There are demo and practice sessions which will support you in your pursuit of acquiring practical knowledge of the basics. Along with these benefits, the luxury of completing your course from the comfort of your study room makes this course a top bet for students and working professionals.