Machine Learning with Python Training in Bangalore, 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

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 

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 Bangalore, India

Machine Learning is the application of systems of Artificial Intelligence concepts to complete set tasks, without requiring reprogramming or human intervention in any form. The idea is to develop programs that access data on their own and analyse it without human help. ML is consistently in the list of LinkedIn’s top emerging jobs and is gaining huge popularity in Bangalore, the Silicon Valley of India. Here are two efficient ways to Machine Learning:

  • Supervised machine learning algorithms: These are the algorithms that take the learnings from the past data and apply them to the new data made available by making use of labelled examples to predict the future.
    • A known dataset is fed into the system, which the system is then trained on and learns from. 
    • The learning algorithm that is then derived from this training and learning is then produced in the form of an inferred function that makes predictions. 
    • Such kind of algorithms are able to provide us with results for any new inputs after they have undergone sufficient learning and training
  • Unsupervised machine learning algorithms: These algorithms use the information that is neither labelled nor have been classified. 
    • Unsupervised learning systems figure out structure from unlabelled data.
    • Such systems, while being unable to figure out the correct result, are able to explore the available data and draw inferences from the available datasets in order to describe and identify hidden structures from unlabelled data.

Bangalore is known as one of the biggest tech hubs of India as over 50% of India’s IT Companies are located in the city.  Machine learning will help these companies get useful data from different sources, helping their businesses become more efficient and sustainable. 

The concept of Machine Learning is a way for humans to be able to solve problems, without having to actually know and understand what the problem really is, as well as understanding why a particular approach to that problem actually works. It makes everything from our everyday tasks to big business decisions easier.

  • It's easy; it works

Machines are able to work faster than human brains do and as such, are able to solve problems faster than we ever can. For example, while there may exist a million options, answers or approaches to a problem, a machine is able to systematically work out, resolve and simultaneously evaluate all the options in order to obtain the best possible outcome or result.

  • Widely used

Machine Learning has several practical applications in real life. It is the very solution that the world was looking for a variety of problems. Every industry, starting from health care, nursing, transport, customer service to government and financial institutions are benefitting from Machine Learning, which is what makes it an indispensable part of our society as it stands today.

Data has transformed every aspect of our lives. All organizations, from start-ups 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. 

Here are some benefits of learning Machine Learning if you are a professional in Bangalore:

  1. Job opportunities in Bangalore: Bangalore is India's tech capital and is home to many leading companies, including TCS, Cognizant, Wipro, Infosys, etc. The city also boasts of 7,200- 7,700 registered startups. All these organisations are looking for machine learning experts to enhance their business scalability and improve business operations.
  2. Great pay: The average salary for a Data Scientist, IT with Machine Learning skills in Bangalore, Karnataka is Rs 1,000,000.
  3. Higher demand: There exists a huge gap between the demand and the availability of Machine Learning engineers. The demand and pay for Machine Learning professionals in Bangalore is only going to increase with time.
  4. Industries are shifting to Machine Learning:  Most industries around the world deal with data every day. By gleaning insights from this data, companies are looking to work more efficiently and competently, as well as gaining an edge over their competitors.

Follow these steps to learn Machine Learning by yourself:

  • Structural Plan: Create a structured plan on the topics that you must familiarize yourself with first and what can be learnt later. 
  • Prerequisite: After making a plan, go ahead and pick a programming language you are comfortable with. You will learn more about it and others naturally as you progress.
  • Learning: Start the learning process learning in accordance with the plan created in step (1). Refer to sources online or in books. You can also ask around the community if you want.
  • Implementation: Take projects online or make some yourselves to implement. Take datasets from the internet and start solving problems, and take part in online competitions such as Kaggle to learn more.

Follow these 5 steps to get started in machine learning:

  • Adjust your mindset: Understand the scope and possibilities of this field and mould your mind to learn it for the long run.
  • Pick a process: Create a plan that suits your capabilities and convenience.
  • Pick a tool: Pick the desired tools that you are familiar with. You will get the chance to learn more about them and others as you go along.
    • Beginners are recommended Weka Workbench
    • Intermediate learners are recommended the Python Ecosystem
    • Advanced learners are recommended the R Platform
  • Practice on Datasets: Since ML is all about real world applications, it’s important to practice your skills outside textbook exercises. Practice on data sets available online.
  • Build your own portfolio: Make your own portfolio of the projects you complete. Make sure to choose a varied range of projects as you go.

Brush up on the following skills to become a machine learning professional in Bangalore:

  • Programming languages: Learning a programming language such as Python, Java, Scala, etc. is an important prerequisite before you embark upon your Machine Learning journey. Knowledge of formatting data formats, processing the data in order to make it compatible with the machine learning algorithm are also skills that will help you in the long run.
  • Database skills: A prior knowledge and experience of working with MySQL and relational databases is also a prerequisite to fully gauging and understanding Machine Learning concepts. Programmers must be able to read data available at different sources, and then convert this data obtained in a format that is readable as well as compatible with the machine learning framework.
  • Machine Learning visualization tools: There are 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 in real life. 
  • Machine learning frameworks: Knowledge of one or more of the frameworks including Apache Spark ML, Scala, NLP, R, TensorFlow etc. is a prerequisite for a thorough understanding of Machine Learning concepts.
  • Mathematical skills: Data is processed, analysed and used through the mathematical algorithms and concepts in order to form a Machine Learning model. Refer to this 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 Modelling
    • Fitting of a distribution
    • Probability Distributions
    • Hypothesis Testing
    • Regression and Time Series
    • Mathematical statistics

Follow these steps to execute a Machine Learning project with Python:

  • Gathering data: The most important step - the quality and quantity of your data is directly proportional to the performance of your model.
  • Cleaning and preparing data: Clean the data which means correct the missing data that we may have followed by preparing the data. Convert the raw data to the data our model is expecting and finally divide the data into 2 parts: training data and testing data.
  • Visualize the data: Visualization helps in understanding the kind of data that we have in our hands and helps to make a good selection of model accordingly.
  • 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. 
  • Train and test: We have our prepared data ready to be injected into our chosen model. As in earlier step, we have divided our data into training and testing data, we now train our model with the training data.
  • Adjust parameters: Lastly, fine tune the parameters to make it perform the best.

Algorithms are one of the most integral parts of the Machine Learning field. It is important for all learners completely understand 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 want to learn. 
  • Apply the algorithms that you listed down: Along with learning the theory of Machine Learning algorithms, it is also important to practice Applied Machine Learning. Start building up intuitions such as Support Vector Machines, decision trees etc.
  • Describe these Machine Learning algorithms: A thorough analysis and understanding of Machine Learning algorithms will help you create a description of these algorithms. Continue adding more and more information to these descriptions on your own. 
  • Implement Machine Learning Algorithms: The implementation of algorithms helps 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: 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 behaviour of an algorithm enables you to better scale and adapt an algorithm to suit your problem needs in the future.

Machine Learning Algorithms

The K Nearest Neighbours algorithm is an uncomplicated Machine Learning algorithm. Given a totally multiclass dataset to be worked on, with the goal of predicting the class of a given data point, we can make use of the K Nearest Neighbour algorithm.

  • The primary requirement of the nearest neighbour classification is the definition of a pre-defined number, which will be stored as the value of ‘k’. This number, k, defines the number of training samples that are closest in distance to a new data point that is to be classified. 
  • The label that will be assigned to this new data point, will then, be one that has already been assigned to and defined by these neighbours. 
  • K-nearest neighbour classifiers possess a fixed user-defined constant for the number of neighbours which have to be determined.
  • The Radius based classification, the concept behind this, states that depending on the density of the neighbouring data points, all the samples are identified and classified under and inside a fixed radius. 
  • All these methods based on the classification of the neighbours are also known as the non-generalizing Machine Learning methods. This is majorly owing to the fact that these methods ‘remember’ all the training data that was fed into it, instead of acting on them.
  • Classification is then performed as a result of a majority vote conducted among the nearest neighbours of the unknown sample.

This depends upon what you intend to do with Machine Learning. 

  • If you simply wish to use the existing Machine Learning algorithms, then you can study Machine Learning without knowing any classic algorithms. There are various online classes offering similar courses. 
  • In case you wish to innovate with the help of Machine Learning, having some basic knowledge of the workings and uses of algorithms is a critical prerequisite. Since you will basically be involved in the adaptation of a new algorithm or even designing a new one, you need the tools and the knowledge that is necessary in order to adapt, design and innovate using Machine Learning.

There are basically 3 types of Machine Learning Algorithms - 

  • Supervised Learning: Using categorically classified historical data to learn the mapping function from the input variables (X) to the output variable (Y). Examples of such include
    • Linear Regression - The relationship between x and y, the input and output variable is expressed as y = a + bx
    • Logistic Regression - Logistic Regression is just like the linear regression model; the only difference is the outcome of the regression is probabilistic, rather than exact values. 
    • Classification and Regression Trees (CART) - This algorithm charts the possibility of each outcome and predicts the result on the basis of defined nodes and branches.
    • Naïve Bayes - This algorithm predicts the possibility of an outcome happening, given the basic value of some other variable. 
    • K-Nearest Neighbours - This algorithm charts the entire data set given after assigning a predefined value of “k” to find out the outcome for a given value of the variable.
  • Unsupervised Learning: In these types of problems, only the input variables are given and not the output ones. Thus, the underlying structure of the given data sets is analysed to reveal possible associations and clusters. Examples include the following
    • Apriori - This algorithm is used in various databases containing transactions to identify frequent associations or instances of two items occurring together.
    • 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. 
    • Principal Component Analysis (PCA) - It makes the data space easier to visualize, by reducing the number of variables. The basic principle of orthogonality ensures that each pair of components is unrelated to each other.
  • Ensemble Learning: Building on the premise that groups perform better than single learners, these types of algorithms combine the results of each learner and then analyse them as a whole to obtain a fairly accurate representation of the actual outcome. Examples of such algorithms include the following:
    • Bagging - This algorithm is used to generate multiple datasets (based on the original one), then model the same algorithm on each to produce different outputs.
    • Boosting - This algorithm is similar to the above one, but it works sequentially instead of the parallel nature of bagging. Thus, each new dataset is created by learning from the previous one’s errors and miscalculations. 

The simplest of machine learning algorithms used to solve the simplest of ML problems (simple recognition) is k-nearest neighbour algorithm. Below are some of the reasons why kNN is used extensively for solving some of the basic, but important, real-life problems:

  • It is a classification algorithm though can be used for regression as well.
  • It classifies based on the similarity measure and is non-parametric.
  • Data set used for the training phase is labelled data (supervised learning) and the aim of the algorithm becomes predicting a class for an object based on its k nearest surroundings.
  • Some examples of the uses of KNN are:
    • Searching in documents containing similar topics.
    • Detect patterns in credit card usage.
    • Vehicular number plate recognition.

Keep these points in mind to choose the right algorithm:

  • Understanding your data: First and foremost comes your data upon which you have to apply your algorithm, so to find the correct algorithm you first have to understand your data.
    • Visualize your data by plotting graphs.
    • Find correlation in the data.
    • Clean your data and missing data.
  • Get the intuition about the task: You need to see which kind of learning will help your model 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: The best models and algorithms work on high-end machines and require high data storage and manipulation resources. Constraints can be in the form of hardware or software as well.
    • Data storage capacity limits the amount of data that we can store for training and testing phases.
    • Hardware constraints allow us to choose algorithms which run according to the hardware available to us. 
    • Depending upon time constraints, whether we can allow training phase to be of long duration or not, or testing phase to be short or long is an important choice that we need to make before choosing the model
  • Find available algorithms: The last step is the implementation of the algorithm.

Designing and implementing a Machine Learning algorithm involves the following steps:

  • Select a programming language: Choose the programming language that you wish to use in order to undertake your implementation.
  • Select the algorithm that you wish to implement: Decide on all of the specifics of the algorithm and be as decisive and precise as possible.
  • Select the problem you wish to work upon: Move on to the selection of the canonical problem set that you are going to use in order to test and validate the efficiency and correctness of your algorithm implementation. 
  • Research the algorithm that you wish to implement: Read through books, research papers, libraries, websites and blogs that contain descriptions of your algorithm, its implementation, conceptual understanding etc.
  • 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. 

Basic concepts are of vital importance in Machine Learning. We recommend you familiarize yourself with them first thing. Here are some others you should learn:

  • Decision Trees: A Decision tree is a type of a supervised learning algorithm that is used for classification problems. Decision trees aid the system in deciding on which features are to be chosen and which conditions are to be used in order for splitting. Advantages of decision tree methods:
    • They are easy to understand, visualize and interpret.
    • They are able to implicitly perform feature selection as well as variable screening.
    • Decision trees are not affected by non-linear relationships between parameters.
    • Decision trees require minimal efforts in the direction of data preparation from the user.
  • Support Vector Machines: Support Vector Machines are a type of classification methodologies that provide a higher degree of accuracy in classification problems. Some of their benefits include the following:
    • Owing to the nature of convex optimizations, Support Vector Machines provide guaranteed optimality in the solutions that they provide. The solution is not a local minimum, but a global minimum, thereby guaranteeing its optimality.
    • They are useful in both, Linearly Separable (also known as Hard margin) as well as Non-linearly separable (also known as Soft Margin) data.
    • Feature Mapping, which used to be a huge burden on the computational complexity of an algorithm, is a reduced burden owing to the ‘Kernel Trick’ provided by Support Vector Machines, which are able to undertake the process of feature mapping by carrying out simple dot products.
  • Naive Bayes: The Naive Bayes algorithm is a classification technique that is based on Bayes' theorem. It assumes that the presence of a particular feature in a said sample of data is independent of and unrelated to the presence of any other feature in that particular sample of data. Some advantages of the Naive Bayes algorithm are:
    • It is very simplistic technique of classification - all that the system is doing is performing a bunch of counts. 
    • It requires relatively less training data.
    • It is highly scalable.
    • It converges quicker than other traditional discriminative models.
  • Random Forest algorithm: The Random Forest algorithm is a supervised learning algorithm. It creates a forest of decision trees and randomizes the inputs so as to prevent the system from identifying any pattern in the input data owing to its order. Some advantages are:
    • Used for both regression and classification problems.
    • It is easy to view the relative importance that a random forest assigns to the input features
    • It is a very easy to use and handy algorithm.
    • The number of hyper parameters included in a random forest are not high and are relatively easy to understand.

Machine Learning Engineer Salary in Bangalore, India

The median salary of Machine Learning Engineer in Bangalore is ₹8,00,000/yr. The range differs from ₹3,59,000 to as high as ₹20,00,000.

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

Bangalore is called the Silicon Valley of India. Technologically, it is the most developed city in India. Moreover, it is the home to most of the tech companies and as per reports, machine learning engineering is one of the fastest growing jobs. The reason being technology needs machine learning and this is the reason for such a massive demand. Therefore the demand for qualified Machine Learning Engineers is quite high in Bangalore.

Having the most desirable job among engineers in the ‘Silicon city of India’ has its own perks - 

  • High payout - This one is obvious due to high demand.
  • Heavy Bonus - Apart from the high pay, they can also earn a huge bonus depending on the success rate.

The one thing which is unique in terms of Bangalore is the massive opportunity machine learning presents. Apart from the high package, machine learning engineering offers a great insight into the world of technology due to the ability to make predictions through algorithms, hence offering greater knowledge. Moreover, due to the increasing use of AI in companies’ operations, Machine learning engineers in Bangalore have better networking opportunities.

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

  • Tata Consultancy Services
  • Deloitte
  • Infosys
  • Microsoft
  • Robert Bosch India
  • Citrix
  • Subex
  • Smart Joules

Machine Learning Conference in Bangalore, India

S.NoConference NameDateVenue
1.TESTCON 2019, BangaloreJuly 4, 2019, to July 5, 2019Novotel Bengaluru Outer Ring Road
2.Introduction To Data Science and Artificial Intelligence, BangaloreJune 23, 2019Learnbay, Bangalore
3.

Practical Foundation on AI & Deep Learning, Bangalore

June 30, 2019

OpenCube Labs, North Bangalore

4.

Understanding Blockchain Workshop, Bangalore

June 22, 2019

Opencube Labs, Bhuvaneswari Nagar, Dasarahalli, Bengaluru

5.Practical IoT Workshop, Bangalore

June 16, 2019

Opencube Labs, Bhuvaneswari Nagar, Dasarahalli, Bengaluru
6.

6th Annual IoT & AI Summit 2019, Bangalore

July 3, 2019

Sterlings Mac (Matthan Hotel) Bangalore

7.

A.I. Deep-Dive Technology Workshop, Bangalore

July 25, 2019 to July 27, 2019

IKP Eden, Near Forum Mall, Koramangala, Stage 2 16, Bhuvanappa Layout, Tavarekere Main Rd, Domlur, Bengaluru

8.

Machine Learning and Deep Learning Day, Bangalore

September 12, 2019Sterlings Mac Hotel, Hal Old Airport Road, Kodihalli, Bengaluru
9.Open Data Science Conference India 2019, BangaloreAugust 8, 2019Sheraton Grand Bangalore Hotel At Brigade Gateway, 26/1 Dr. Rajkumar Road,, Malleswaram-rajajinagar, Bengaluru
10.WORLD RPA, COGNITIVE AND AI SUMMIT, Bangalore
August 30, 2019
Doubletree Suites By Hilton Hotel Bangalore, Amblipura, PWD Quarters, 1st Sector, HSR Layout, Bengaluru, India
  1. TESTCON 2019, Bangalore
    1. About the TESTCON 2019 Bangalore conference: The conference will deliver cutting-edge Test Automation technologies
    2. Event Date: Thu, 4 July 9:00 AM - Fri, 5 July 5:00 PM
    3. Venue: Novotel Bengaluru Outer Ring Road
    4. Days of Program: 2
    5. Timings: Thu, 4 July 9:00 AM - Fri, 5 July 5:00 PM
    6. Purpose: Learn about popular testing frameworks, AI-based test-bots, Web application testing, advanced analytics and algorithms, explore tools for continuous testing and automated mobile testing using Azure, Selenium (Visual regression) and Appium too.
    7. How many speakers: 5
    8. Speakers & Profile:
      1. Pradeep Soundararajan - Moolya Testing & AppAchhi CEO
      2. Renu Rajani, Former VP - Infosys
      3. Vikas Mittal, Global President for Digital Consulting & Assurance - Xebia
      4. Tamaghna Basu, CTO/Co-Founder - Neoeyed
      5. Anil Kumar Mishra, Senior Tech Manager - TechMahindra
      6. Mohan Satyaranjan, CTO - Taqanal Energy Pvt Ltd
      7. Venkateswaran, Director - Comcast
      8. Kiran Marri, Chief Scientist, Digital Practice - CSS Corp
      9. Surojeet Sengupta, Director QA - GE
      10. Ajay Balamurugadas, AVP-Delivery - Qapitol
      11. Chinmaya Jena, VP and Chief Solution Architect – Accenture
      12. Divya Vaishnavi, Senior Program Manager - Microsoft
      13. Shiva Jayagopal, Director - Winvinaya Infosystems
    9. With whom can you Network in this TESTCON 2019 Bangalore Conference: Network with startup founders as well as established companies, professors and experienced millennials.
    10. Registration cost: Rupees 13000
    11. Who are the major sponsors: Clavent Technologies
  1. Introduction To Data Science and Artificial Intelligence, Bangalore
    1. About the Introduction To Data Science and Artificial Intelligence conference: It is best suited for professionals interested in Machine Learning, Artificial Intelligence, and data science. 
    2. Event Date: Sun, 23 June
    3. Venue: Learnbay, Bangalore
    4. Days of Program: 1
    5. Timings: 1:00 PM - 4:00 PM
    6. Purpose: To introduce individuals to Data Science and Artificial Intelligence
    7. Registration cost: Free
    8. Who are the major sponsors: Learnbay Data Science
  1. Practical Foundation on AI & Deep Learning, Bangalore
    1. About the Practical Foundation on AI & Deep Learning conference: It is a one day event on fundamentals of AI. Learn about AI specialities like Machine Learning, Neural Networks & Deep Learning.
    2. Event Date: Sun, 30 June, 2019
    3. Venue: Opencube Labs , North Bangalore
    4. Days of Program: 1
    5. Timings: 10:00 AM - 4:00 PM
    6. Purpose: The conference will help you get a grasp on various fundamentals of Artificial Intelligence. Workshop includes fundamentals of Python programming required for AI, mathematics along with packages like Numpy, Matplotlib, Scikit, Tensorflow, Keras, etc.
    7. Registration cost: Rupees 999
    8. Who are the major sponsors: Opencube Labs
  1. Understanding Blockchain Workshop, Bangalore
    1. About the Understanding Blockchain Workshop conference: The workshop is focussed on the fundamentals of blockchain.
    2. Event Date: 22nd June, 2019
    3. Venue: Opencube Labs, Bhuvaneswari Nagar, Dasarahalli, Bengaluru, India
    4. Days of Program: 1
    5. Timings: 3:00 PM onwards
    6. Purpose: Start conceptualizing your ideas with the power of Blockchain and earn an open badge. Open badges are similar to digital certificates. 
    7. With whom can you Network in this Conference: Working professionals, Suraj Jana, Founder, Opencube Labs and startup founders. 
    8. Registration cost: INR 699
  1. Practical IoT Workshop, Bangalore
    1. About the Practical IoT Workshop conference: The conference will provide a practical approach to quickly design and build IoT applications.
    2. Event Date: 16th June, 2019
    3. Venue: Opencube Labs, Bhuvaneswari Nagar, Dasarahalli, Bengaluru, India
    4. Days of Program: 1
    5. Timings: 10:00 AM to 4:00 AM
    6. Purpose: Learn about the IoT technology and its applications and start working on your own IoT Projects.
    7. Registration cost: 
      1. General Registration without kit - Rupees 899
      2. General Registration with kit - Rupees 1500
  1. 6th Annual IoT & AI Summit 2019, Bangalore

    1. About the 6th Annual IoT & AI Summit 2019 conference: The conference will help you understand the fundamentals of IoT & AI.
    2. Event Date: 3rd July 2019, Bangalore
    3. Venue: Sterlings Mac (Matthan Hotel) Bangalore
    4. Days of Program: 1
    5. Timings: 9:00 AM to 5:30 PM
    6. Purpose: The purpose of the summit is to explore the potential of the technology across multiple sectors and industries acting as a catalyst between policy makers, government, market movers, and thought leaders providing high-level think tank leadership platform.
    7. How many speakers: 26
    8. Speakers & Profile:
      1. Tulika Pandey, Scientist ‘F’ & Director, Ministry of Electronics & Information Technology Government of India
      2. Abhinav Biswas, Alt. CISO at Electronics Corporation of India Limited (ECIL), Dept. of Atomic Energy, Govt. of India, National Cyber Defence Research Centre
      3. Shefali Bansal, Program Director & WW Practice & Client Success Leader for Watson IoT Manufacturing & Building Solutions, IBM Software Labs
      4. Ravichandran Mahadevan, Vice President IoT - Moving Assets, SAP
      5. Lux Rao, Director & Leader, Digital Solutions, Digital Transformation & Consulting, Dimension Data
      6. Krithika Surianarayanan, Vice President, Standard Chartered Bank
      7. Vinod BoggarapU, Country Leader - Smarter Cities, IBM Watson IOT
      8. Ravi Ramaswamy, Sr. Director & Head - Health Systems, Philips
      9. Vikram Sharma, Director, Intel
      10. Dinesh Pillai, CEO & Managing Director, Acquisory (Former CEO, Mahindra Special Services Group)
      11. Alex Jojo Joseph, Program Director, IBM Software Labs
      12. Bibekananda Roy, Senior Information Security Consultant, Mercedes-Benz Research and Development
      13. Akta Jain, Director, Enterprise Mobility Engineering, Dell
      14. Sunil David, Regional Director - IOT (India and ASEAN region), AT&T
      15. Anurag Joshi, Director - Technical Support, Cisco
      16. Alok SharmA, Head - IoT Practice, Manufacturing, Infosys
      17. Jophy Varghese, APAC Head – Strategic SIs & Alliances, Verizon
      18. Sirish Batchu, VP - Digital Technology, Ather Energy
      19. Monishwaran Maheswaran, Math Research, Harvard University (USA) Keynote Speaker, TEDx
      20. Balakumaran Jayapal, Principal Architect (Data Analytics and Artificial Intelligence), Panasonic India Innovation Centre
      21. Bipin Pradeep Kumar, Co-Founder, Gaia Smart Cities
      22. Harsha Urlam, Exp Data Scientist, Boeing
      23. Mustameer Ahmed Khan, Product Manager, SAP
      24. Babu Jayaraj, Principal Technical Architect, Reliance Jio
      25. Suhas Shivanna, Distinguished Technologist and Strategist, Hewlett Packard Enterprise
      26. Syam Madanapalli, Global Lead - IoT Delivery, NTT Data
    9. Registration cost
      1. Early bird - Rupees 8260
      2. Standard: Rupees 10000
    10. Who are the major sponsors: Associate Sponsor - Revealer

    1.  A.I. Deep-Dive Technology Workshop, Bangalore
      1. About the A.I. Deep-Dive Technology Workshop conference: The conference will unveil the basic methods and provide insights into the applications. 
      2. Event Date: 25th July, 2019 to 27th July, 2019
      3. Venue: IKP Eden, Near Forum Mall, Koramangala, Stage 2 16, Bhuvanappa Layout, Tavarekere Main Rd, Domlur, Bengaluru 560029, India
      4. Days of Program:  3
      5. Timings: 9:00 AM to 5:00 PM
      6. Purpose: The purpose of the conference is to understand the complete know-how of the world of AI/Machine Learning.
      7. Registration Cost: Rs. 35,000/- (including GST) 
    1. Machine Learning and Deep Learning Day, Bangalore
      1. About the Machine Learning and Deep Learning Day conference: It is a one day event on machine learning and deep learning.
      2. Event Date: 12th September, 2019
      3. Venue: Sterlings Mac Hotel, Hal Old Airport Road, Kodihalli, Bengaluru, India
      4. Days of Program: 1
      5. Timings: 8:30 AM to 5:30 PM
      6. Purpose: To have a discussion on Machine learning and deep learning.
      7. Registration cost:
        1. Individual (Early Bird): Rs. 9000
        2. Individual (Standard): Rs. 10000
        3. Group of 3 or more (Early Bird): Rs. 7500
        4. Group of 3 or more (Standard): Rs. 8000
    1. Open Data Science Conference India 2019, Bangalore
      1. About the Open Data Science Conference India 2019 conference: Hosted by leading data science, machine learning, and analytics practitioners, it is an open data science conference for 2 days. 
      2. Event Date: 8th August, 2019 to 9th August, 2019
      3. Venue: Sheraton Grand Bangalore Hotel At Brigade Gateway, 26/1 Dr. Rajkumar Road, Malleswaram-rajajinagar, Bengaluru, India
      4. Days of Program: 2
      5. Timings: 8:30 AM to 6:00 AM
      6. Purpose: Learn about the latest trends, tools, and best practices from leading data science and machine learning experts.
      7. Registration cost: ODSC India 2019 Pass - Rs. 15000
    1. WORLD RPA, COGNITIVE AND AI SUMMIT, Bangalore

      1. About the WORLD RPA, COGNITIVE AND AI SUMMIT conference: This event covers the array of process automation technologies including, machine learning, AI, Digital Process Automation, Digital Transformation, RPA and more. 
      2. Event Date: 30th August, 2019
      3. Venue: Doubletree Suites By Hilton Hotel Bangalore, Amblipura, PWD Quarters, 1st Sector, HSR Layout, Bengaluru, India
      4. Days of Program: 1
      5. Timings: 8:00 AM to 5:00 PM
      6. Purpose: To make individuals aware of artificial intelligence associated with the cognitive abilities
      7. With whom can you Network in this World RPA, Cognitive And AI Summit Conference: Network with the founder of different startups as well as established companies, professors and experienced millennials.
      8. Speakers & Profiles
        1. Peter Gartenberg, Managing Director & President, Indian Subcontinent
        2. Rob King, Co-Founder and Director Wzard Innovation Ltd
        3. Amit Arora, Vice President – Artificial Intelligence Genpact
        4. Kiriti Rambhatla Head of Operations Intelligence LinkedIn
        5. Murali Chandrasekaran, Director, Strategy & Special Projects, UiPath
        6. Vinaya Chandran, Director - Alliances Kryon
        7. Surojeet Sengupta, Director, GE Digital, GE
        8. Sandeep Bharadwaj, Global Strategic Alliances Kofax
        9. Vishal Naik, Global Lead - Applied Transformation Accenture
        10. Rittick Banerjee, Associate Director - BFS Cognizant Technology Solutions
        11. Riteja Premy Assistant Director EY
        12. Gaurav Rai Associate director EY
        13. Nishant Goel CEO & Founder BOT Mantra
      9. Registration cost: 
        1. Individual (Early Bird): Rs. 9000
        2. Individual (Standard): Rs. 10000
        3. Group of 3 or more (Early Bird): Rs. 7500
        4. Group of 3 or more (Standard): Rs. 8000

    S.No

    Conference nameDateVenue
    1.Workshop: Machine Learning as a Service, BangaloreJuly 25, 2017, to July 26, 2017Thought Factory, Bangalore
    2.The Fifth Elephant 2017, BangaloreJuly 28, 2017MLR Convention Centre, Bangalore
    3.Deep Learning and Machine Learning for Computer Vision, BangaloreNovember 4, 2017Innov8 Coworking, Bengaluru
    4.Hacker Math for Machine Learning, Bangalore
    November 25, 2017 to November 26, 2017
    IKP EDEN, Bengaluru
    5.Introduction to Recommendation Systems, Bangalore
    December 16, 2017
    IKP EDEN, Bengaluru
    6.Deep Learning Bootcamp, Bangalore
    March 10, 2018 to March 11, 2018IKP EDEN, Bangalore
    IKP EDEN, Bangalore
    7.Machine Learning with Amazon SageMaker, Bangalore
    April 4, 2018
    IKP EDEN, Bangalore
    8.Building Data products at Uber, Bangalore
    June 15, 2018
    HasGeek House, Bangalore
    9.Role of data science in fraud detection, Bangalore
    June 23, 2018
    WalmartLabs, Bangalore
    10.Machine Learning with Amazon SageMaker, Bangalore
    July 26, 2018
    Auditorium 3, NIMHANS, Bangalore
    1. Workshop: Machine Learning as a Service, Bangalore
      1. About the Machine Learning as a Service conference: The focus of the conference was to solve the most common obstacles faced in the field of machine learning and data science.
      2. Event Date: 25-26 July, 2017
      3. Venue: Thought Factory, Bangalore
      4. Days of Program: 2 days
      5. How many speakers: 3
      6. Speakers & Profile:
        1. Amit Kapoor- Founder, amitkaps.com
        2. Anand Chitipothu- Founder, anandology.com
        3. Bargava Subramanian, Ex-Cisco, Ex-Red Hat. Deep Learning Engineer
    2. The Fifth Elephant 2017, Bangalore
      1. About the The Fifth Elephant 2017 conference: The conference was based on data science, engineering and machine learning.
      2. Event Date: July 28, 2017
      3. Venue: MLR Convention Centre, Bangalore
      4. Days of Program: 2 days
      5. Timings: 9:30 - 5:40
      6. Purpose: The conference focussed on the architecture decisions, building data pipelines and data engineering. Topics like IoT and data analytics, Data visualization, Data in government were also discussed.
      7. Who were the major sponsors: Go Jek, MathWorks, Aerospike, PayU, Walmart, intuit, Qubole, iMerit, IQLECT, Uber, Razorpay.
    1. Deep Learning and Machine Learning for Computer Vision, Bangalore.
      1. About the Deep Learning and Machine Learning for Computer Vision conference: The conference allowed to get hands-on opportunity on various machine learning problems.
      2. Event Date: 4 Nov, 2017
      3. Venue: Innov8 Coworking, Bengaluru
      4. Days of Program: 1
      5. Timings: 9:30 AM to 6:00 PM
      6. How many speakers: 4
      7. Speakers & Profile:
        1. Sumod Mohan, CTO of Digital Aristotle and heads the Computer Vision and Machine Learning at Soliton Technologies
        2. Shivarajkumar Magadi, Leads the 3D Vision team at Soliton Technologies
        3. Dhivakar Kanagaraj, Computer Vision and Machine Learning Engineer at Soliton Technologies
        4. Senthil Palanisamy, Computer Vision and Machine Learning Engineer in Soliton Technologies
    1. Hacker Math for Machine Learning, Bangalore
      1. About the Hacker Math for Machine Learning conference: The conference focussed on the prerequisites required for Machine Learning.
      2. Event Date: 25-26 November, 2017
      3. Venue: IKP EDEN, Bengaluru
      4. Days of Program: 2
      5. Timings: 10:00 AM to 5:00 PM
      6. Purpose: Get a good grasp on calculus, linear algebra, & statistics.
      7. How many speakers: 2
      8. Speakers & Profile:
        1. Amit Kapoor, Crafting Visual Stories with Data
        2. Bargava Subramanian, Senior Data Scientist
    1. Introduction to Recommendation Systems, Bangalore.
      1. About the Introduction to Recommendation Systems conference: The conference focussed on how machine learning can be used for building recommendation systems.
      2. Event Date: 16 December, 2017
      3. Venue: IKP EDEN, Bengaluru
      4. Days of Program: 1
      5. Timings: 10:00 AM to 5:00 PM
      6. How many speakers: 2
      7. Speakers & Profile:
        1. Amit Kapoor, Crafting Visual Stories with Data
        2. Bargava Subramanian, Senior Data Scientist
    1. Deep Learning Bootcamp, Bangalore
        1. About the Deep Learning Bootcamp conference: The event focussed on theory and practical concepts of building a deep learning solution in the space of computer vision and natural language processing. 
        2. Event Date: 10-11 Mar, 2018
        3. VenueIKP EDEN, Bangalore
        4. Days of Program: 2
        5. Purpose: The objective was to learn and implement an end-to-end deep learning models for computer vision (image recognition and generation) and natural language processing (text classification and generation).
        6. How many speakers: 2
        7. Speakers & Profile:
          1. Amit Kapoor, Crafting Visual Stories with Data
          2. Bargava Subramanian, Senior Data Scientist
    1. Machine Learning with Amazon SageMaker, Bangalore
      1. About the Machine Learning with Amazon SageMaker conference: The workshop helped individuals in developing machine learning models.
      2. Event Date: 04 April, 2018
      3. Venue: IKP EDEN, Bangalore
      4. Days of Program: 1
      5. Purpose: To teach the individuals, how to build, train and deploy the different machine learning models.
      6. How many speakers: 1
      7. Speakers & Profile: Atanu Roy, AI Specialist Solutions Architect
      8. Who were the major sponsors: AWS
    1. Building Data products at Uber, Bangalore
      1. About the Building Data products at Uber conference: It was a brainstorming session on how to legitimize user data with user experience.
      2. Event Date: 15 June, 2018
      3. Venue: HasGeek House, Bangalore
      4. Days of Program: 1
      5. Timings: 07:30 PM - 09:00 PM
      6. Purpose: To align the data with the user experience.
    1. Role of data science in fraud detection, Bangalore
      1. About the Role of data science in fraud detection conference: The conference focussed on what role does data science play in the detection of fraud.
      2. Event Date: 23 June, 2018
      3. Venue: WalmartLabs, Bangalore
      4. Days of Program: 1
      5. How many speakers:4
      6. Speakers:
        1. Vamsi Varanasi, Product Manager, Ola Credit
        2. Vinayak Hegde, CTO, Zoomcar
        3. Vivek Mehta, Revlo.in
        4. Nirmal J M, Head Payments Risk and Compliance at Ola Money
      7. Timings: 11:15 AM - 2:00 PM
      8. Who were the major sponsors: Walmart
    1. Machine Learning with Amazon SageMaker, Bangalore

      1. About the Machine Learning with Amazon SageMaker conference: The conference focused on how to build, train and deploy machine learning models efficiently and at scale. 
      2. Event Date: 26 July, 2018
      3. Venue: Auditorium 3, NIMHANS, Bangalore
      4. Days of Program: 1
      5. Timings: 10:00 AM to 01:30 PM
      6. Purpose: Learn how to use Amazon SageMaker to build, train and host machine learning models.
      7. How many speakers: 1
      8. Speakers & Profile: Praveen Jayakumar, Solutions Architect
      9. With whom can you Network in this Conference: Network with the individuals of your age as well as the experiences individuals.
      10. Who are the major sponsors: AWS

    Machine Learning Engineer Jobs in Bangalore, India

    As a Machine Learning engineer, you will be responsible for the following:

    • Deploying machine learning solutions into production
    • Optimizing solutions for performance and scalability
    • Cleaning and analyzing data
    • Designing and Developing ML systems
    • Conducting tests and experiments
    • Carrying out the statistical analysis

    Bangalore is one of the best cities to live in if you want to work in the IT sector. IT firms in Bangalore employ about 75% of India's pool of 2.5 million IT professionals. The city is filled with startups and big corporations hiring Machine Learning engineers. There are 387 Artificial Intelligence startups in Bangalore. As more and more companies are starting to use ML and AI, the demand of machine learning engineers has also gone high in this city.

    Here are a few companies with Machine Learning open positions in Bangalore:

    • Accenture
    • IBM
    • Cisco Systems
    • Amazon
    • Oracle
    • HP Inc.
    • Microsoft

    Some of the in-demand ML job roles include:

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

    To help build your network with other Machine Learning Engineers in Bangalore, you can try one of the following:

    • Platforms like LinkedIn
    • Machine Learning meetups
    • Machine Learning conferences

    Machine Learning with Python Bangalore, India

    Follow these steps to use Python for mastering Machine Learning:

    1. Adjust your mindset to use Machine Learning Concepts.
    2. Download and install the Python SciPy Kit for Machine learning and install all useful packages.
    3. Take a tour of the tool to get an idea of all the functionalities available and their uses.
    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 most commonly used and popular datasets so as to gain a better understanding of the concepts.
    6. Start small and work your way 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.

    These are some of the most useful libraries when it comes to Python and ML:

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

    Follow these steps to execute a Machine Learning project as a professional in Bangalore:

    • Gathering data: This is the most important step as the performance of your model is based on the quality and quantity of your data.
    • Cleaning and preparing data: Clean and prepare the data. Feature engineering is used to prepare the data. In this, the data is first converted into the data that our model is expecting and then divided into two parts – testing and training data.
    • Visualize the data: Visualization helps in understanding the kind of data that we have in our hands and help to make a good selection of model accordingly.
    • Choosing the correct model: This step involves selecting the model and the algorithm suited for the data. It will highly determine the performance of your project. 
    • 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.
    • Adjust parameters: The last step involves fine-tuning the parameters.

    These tips will help you learn basic Python skills:

    • Consistency is Key: Code every day. Consistency is very important when you are learning a new programming language. 
    • Write it out: Studies have proven over the past several years that writing down a particular thing with your own hands, is the key to long term retention of the concept.
    • Go interactive!: The interactive Python shell is one of the best learning tools, irrespective of whether you are writing code for the first time, learning about Python data structures such as dictionaries, list, strings etc or debugging an application. 
    • Bug Bounty Hunter: It is inevitable that you will run into bugs. Take up the challenge as a means of learning Python in the best possible way and take pride in becoming a Bug Bounty Hunter.
    • Community learning: Surround yourself with other people who are learning Python as well as this not only gives you a boost and keeps you going, but also helps you receive helpful tips and tricks from other, along the way.

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

    • Scikit-learn: Used majorly for data mining, data analysis and in data science as well.
    • SciPy: This library has packages for Mathematics and engineering.
    • Numpy: Provides efficiency and much more. 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. 

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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 Bangalore

    Learn Machine Learning with Python in Bangalore

    Join our 5-day intensive machine learning training with python in Bangalore and get hands-on expertise on the use and benefits of Python programming language. This Data analysis and machine learning class training in Bangalore have been designed to give maximum learning benefit to beginners who want to get proficient in Python programming language. On joining this course you will receive 40 hours of intensive training from trained and certified experts, downloadable course book and course completion certificate issued by KnowledgeHut. You will get a chance to get your hands dirty with our intensive lab exercises that will teach you the full functionality and working of Python.  By the end of this course, you would have mastered Python to build your own Python packages.

    For busy professionals who are unable to attend our classroom training, we bring online classes that represent e-learning at its best and will give you all the advantages of a regular course.

    Machine Learning with Python certification in Bangalore

    So many programming languages are now being used and each has its own advantages and disadvantages. But Python has stood out among all others because of the numerous advantages it offers. First of all, it does away with many of the redundancies and repetitions in code. Python is fairly easy to master and has a syntax that is clear and readable. Also, its object-oriented nature allows code to be re-used. Its other advantages are that it is free, is cross-platform so can run on major operating systems like Windows, Linux and Mac is widely supported by a strong community of developers and is extensible and safe.

    All these make it a very likable programming language and many organizations like their programs to be built on Python. There is a huge demand for Python professionals and hence KnowledgeHut has created this machine learning certification training in Bangalore on successful completion of which you will receive the machine learning certification in Bangalore.

    Our machine learning with python course in Bangalore will teach you advanced data structures and algorithms, using libraries such as SciPy, NumPy, and Pandas to create data frames, grouping, processing data and performing numerical and scientific analysis and creating classifiers and clusters. This is a complete workshop and at the end of this course, you would have completely mastered Python.

    About Bangalore

    Bangalore is a great city and has created a reputation for itself as being India’s IT capital. Python programmers are much in demand and the right skills can have a huge impact on your career. Join today for great coaching and great classroom environment