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Machine Learning with Python Training in Kolkata, 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
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Description

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

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

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

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

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

Why Learn Machine Learning from Knowledgehut?

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

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

What is Machine Learning?

Machine Learning is an application of Artificial Intelligence that allows machines and computers to learn automatically to predict outcomes from examples and experiences, without there being any need for explicit programming. As the name suggests, it gives machines and computers the ability to learn, making them similar to humans.

The concept of machine learning is quite simple. Instead of writing code, data is fed to a generic algorithm. The generic algorithm/machine will build a logic which will be based on the data provided. The provided data is termed as ‘training data’ as they are used to make decisions or predictions without any program to perform the task.

Practical Definition from Credible Sources:

1) Stanford defines Machine Learning as:

“Machine learning is the science of getting computers to act without being explicitly programmed.”

2) Nvidia defines Machine Learning as:

“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”

3) McKinsey & Co. defines Machine Learning as:

“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”

4) The University of Washington defines Machine Learning as:

“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”

5) Carnegie Mellon University defines Machine Learning as:

“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”

Origin of Machine Learning through the years

Today, algorithms of machine learning enable computers and machines to interact with humans, write and publish sport match reports, autonomously drive cars, and find terrorist suspects as well. Let’s peek through the origins of machine learning and its recent milestones.

1950:
Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.

1952:
The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.

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

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

1981:
Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.

1990:
The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.

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

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

2010:
The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.

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

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

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

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

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

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

How does Machine Learning work?

The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.

The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.

There are various other factors and steps involved as well. This is just an example of the process.

Advantages of Machine Learning

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

Industries using Machine Learning

Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors.

  1. Financial services:

Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as well as identify clients with high-risk profiles.

  1. Government:

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

  1. Health Care:

Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real time, which might lead to improved treatment or diagnosis.

  1. Oil and Gas:

There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc.

  1. Retail:

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

What is the future of Machine Learning?

Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.

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

What You Will Learn

1. Statistical Learning

Understand the behavior of data as you build significant models

2. Python for Machine Learning

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

3. Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning.

4. Optimization Techniques

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

5. Machine Learning Algorithms

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

6. Dimensionality Reduction

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

7. Neural Networks

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

8. Ensemble Learning

Learn to use multiple learning algorithms to obtain better predictive performance

PREREQUISITES

For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.

Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey, and earn an industry-recognized Machine Learning Certification from KnowledgeHut upon successful completion of the Machine Learning course.

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

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Who Should Attend?

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

Knowledgehut Experience

Instructor-led Live Classroom

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

Curriculum Designed by Experts

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

Learn through Doing

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

Mentored by Industry Leaders

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

Advance from the Basics

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

Code Reviews by Professionals

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

Certification Process

Learn Machine Learning in Kolkata
Machine Learning Algorithms
Machine Learning Engineer Salary in Kolkata
Machine Learning Conference in Kolkata
Machine Learning Engineer Jobs in Kolkata
Machine Learning with Python Kolkata

Curriculum

Learning Objectives:

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

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

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

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

Hands-on: No hands-on

Learning Objectives :

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

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

Hands-on: No hands-on

Learning Objectives:

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

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

Hands-on: No hands-on

Learning Objectives:

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

Topics:
  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • KNN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study
Hands-on:
  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
  • This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
  • Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.
Learning Objectives:

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

Topics:
  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study
Hands-on :
In marketing, if you're trying to talk to everybody, you're not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns. 
Learning Objectives:

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

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

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

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

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

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

View Profile

Projects

Predict Property Pricing using Linear Regression

With attributes describing various aspects of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.

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

This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.

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

Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set contains 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable

Read More

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

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Read More

Predict quality of Wine

Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

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

Learn Machine Learning

Learn Machine Learning in Kolkata, India

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

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

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

According to Angellist, there are over 250+ startups in Kolkata, such as TaxMantra, Sweethandi, Santa Delivers, Zeroinfy, etc. It is also home to several leading companies, such as Oracle, Sforce, Wipro, TCS, Cognizant, etc. In 2018, Infosys started its software development centre in Kolkata investing around Rs 100 crore in the first phase. Machine learning is helping all these companies in finding patterns and automate value extraction in many areas. 

It simply works

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

It is used in a wide range of applications today

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

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

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

  • Garner better job opportunities- Machine Learning enables organizations to become more efficient thereby getting more work done in the same amount of time. This leads to the net worth of Artificial Intelligence guided organizations to soar making them more profitable than ever. With more and more companies shifting to digital marketing analysis, job opportunities for anyone with Machine Learning experience will go up exponentially opening doors to unlimited opportunities. Currently, there are more than 50 Machine Learning jobs available in Kolkata.
  • Better salary and remunerations- It is obvious when we think of it that with the highest grossing companies using Machine Learning tools, and limited number of Artificial Intelligence professionals out there, the net worth of such professionals will be much more than in any other job. According to Glassdoor, the average salary of a Machine Learning experts in India is somewhere between 60-90K per month. The average salary for a Machine Learning Engineer is Rs 48,310 per month in Kolkata, West Bengal. 
  • Demand curve for Machine Learning experts is steadily going up and will keep going up- It is no surprise that the Machine learning jobs are on the increase. The gap between the demand and availability of professionals is on the increase in most leading companies. AI is the future and offers enough job security that IT professionals should think of getting certified in Machine Learning soon.
  • Industries from all sectors are slowly incorporating Machine Learning in their businesses- By 2020 the number of data-related jobs is projected to grow by nearly 364,000 listings to approximately 2,720,000. Availability of tons of free data at the disposal of companies makes it imperative to be used for business purposes. Implementing Machine Learning to work efficiently and competently will put companies ahead of their competitors. 

Machine Learning is constantly evolving and developing itself everyday due to the immense amount of possibilities it offers. While one can always opt for certified courses or diplomas, self-learning ML can also be equally effective if one has the motivation to keep working forward. The few things to keep in mind are-

  • Machine Learning is a practical medium that requires hands-on experience rather than theoretical knowledge. Your ability will depend on your successful use and manipulation of available data for the development of a company.
  • The best way to increase your chances of getting employed is by taking up as many projects during your self learning days as you can.

You can follow these steps to learn machine learning:

  1. Preparations- The best way to make the best use of your time is to read through all the relevant documents and research papers on the subject. Make a schedule of the important aspects that needs to be learnt first and what can be left to be learned later.
  2. Prerequisite- Decide on the programming language that you would like to learn and brush up on your statistical and mathematical skills since ML deals with statistical data.
  3. Learning- Proceed with reading up on ML according to the schedule you created in Step 1 and make sure not to lag behind. Constantly refer to books and online materials from sites like Coursera, KnowledgeHut or edX to clear any doubts that you might have. 
  4. Implementation of knowledge- No learning is complete without the successful implementation of what you have learnt in real life situations. ML is no different. There are various online platforms which provide interesting datasets to test your skills on. Practicing on them will provide people with the ability of quick thinking and finding unusual, effective solutions to age old problems. You can also look for boot camps in Kolkata to get some hands-on experience on machine learning. 

Follow these 5 steps:

  • Make up your mind: Understand what is holding you back in achieving your ML goals. Convince yourself that it is easy and not as complicated as most people claim. 

Realize that ML is like any other creative process where as you practice more the better you get.

  • Find your ML community: This means that you should look for a group of people or a community of ML enthusiasts whether online or offline in Kolkata who will help you and guide you when necessary in your ML journey.
  • Find your rhythm and stick to it: Everybody has their own pace for learning as long as it is a structured and systematized plan of working through problems. So find your own pace and stick to it.
  • Pick a tool: Select a programming tool that corresponds well with your ML skills and include it in your schedule:
    • For beginners Weka Workbench is the recommended tool.
    • For intermediate learners it is Python ecosystem.
    • For advanced learners R programming is the best tool to master.

    •  Practice, practice and more practice: With knowledge at our fingertips, it is unfair not to use it to our advantage. There are a number of datasets available in various platforms which should be used to practice data collection and manipulation.
    • Create your portfolio: Once you are confident in your skills of ML, the next stage is to develop your portfolio which will demonstrate the kind of skills that you have picked up. 

    Companies like IBM, Wipro are looking for expert machine learning engineers in Kolkata. In order to be an ML expert, the following technical skills are mandatory to learn and imbibe in your projects-

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

    Below are the steps to execute an ML project with Python:

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

    Algorithms are an integral part of Machine Learning. Here’s how you can effectively learn them:

    1. Make a list of all ML algorithms: Every algorithm is useful and unique in its own way. Thus, it is important to list the algorithms you want to learn in the beginning of your Machine Learning journey. Writing down the general category under which an algorithm falls provides you with an idea of the different classes and types of algorithms available and prepares you for what lies ahead.
    2. Implement the Machine Learning algorithms you listed: Algorithms do not exist in isolation from the data. While having an in-depth idea of the theory behind any algorithm is expected, unless you can successfully implement the algorithms in processing data, your skill set remains only half full. Practicing the algorithms with different types of data provides you with the confidence to work in any given situation. Start building up an intuition for the various Machine Learning algorithms such as Support Vector Machines, decision trees etc. 
    3. Describe the Machine Learning algorithms: Once you are well accustomed to the various algorithms, the next step is to explore what you have already learnt. Being able to describe and analyze every algorithm will increase your knowledge of algorithms. Continue adding more information to these descriptions as you go.
    4. Use the Machine Learning algorithms: By implementing the algorithm yourself, you will be able to understand the micro decisions involved in the implementation of Machine Learning concepts. The implementation of algorithms will help you get a feeling about the workings of an algorithm as well as understand the mathematical extensions and descriptions of the algorithm. 
    5. Experiment on Machine Learning algorithms: Once you have a strong grasp of the concepts of Machine Learning algorithms, it is time to experiment with them. Understanding the different parameters that are used during the working on an algorithm prepares you to customize the algorithms to suit your needs in the future.

    Machine Learning Algorithms

    The K Nearest Neighbours (KNN) algorithm is a simplistic Machine Learning algorithm. The aim of KNN is to predict outcome of new data instance. It trains to find either the K-nearest instance of the new data instance or the K number of instances that are most similar to the new instance. The prediction or output of one of two things;

    • The mode or the most frequent class in a classification problem
    • The means of the outcomes, in a regression problem

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

    Your intention and future goals with Machine Learning determines the necessity of learning algorithms-

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

    Machine Learning algorithms are basically of three types:

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

    The simplest algorithm in Machine Learning enables one to solve the simplest ML problems. According to this criteria the algorithm has to be:

    • Easy to comprehend.
    • Easy to use and recognizing the underlying patterns.
    • Simple algorithms take less time and resources to train and test data as compared to high-level algorithm.

    K-nearest neighbor algorithm is the most simplest and widely used ML algorithms for solving basic but important real life problems. The reason for that is-

    • It is a classification algorithm though it 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 labeled data (supervised learning) and the aim of the algorithm becomes predicting a class for an object based on its k nearest surroundings where k defines the number of neighbors.
    • Some practical and real-life examples where KNN is used are:
      • When searching in documents containing similar topics.
      • Used to detect patterns in credit card usage.
      • Vehicular number plate recognition.

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

    • Understanding your data: Even before deciding on the appropriate algorithm, one needs to understand the data in front of him/her. Understanding the data well is important to decide which algorithm will be ideal. 
    • Get the intuition about your task: There are instances when the ultimate aim of a task is lost in the process and that is why ML is so important to solve the problem. After realizing the need of a process, you can decide the kind of learning required to complete the task at hand. There are 4 types of learning in general.
      • Supervised learning
      • Unsupervised learning
      • Semi-supervised learning
      • Reinforcement learning
    • Understand your constraints: A lot of times we overestimate ourselves and do not apply constraints to our planning and find the best tools and algorithms out there. This is not the right approach. Most of the best models and algorithms are for high end machines which require high level storage. Understanding hardware and software constraints thus become important. 

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

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

    The following are some essential topics of Machine Learning that every learner should be acquainted with:

    • Decision Trees: Used for classification problems, a Decision tree is a type of a supervised learning algorithm. 
    • Support Vector Machines: It is a type of classification methodology that provides a higher degree of accuracy in classification problems. 
    • Naive Bayes: It is based on Bayes' theorem and is a classification technique. 
    • Random Forest algorithm: The Random Forest is basically a collection of randomized decision trees that are trained with the bagging method.

    Machine Learning Engineer Salary in Kolkata, India

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

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

    The city of joy is one of the most developed and technologically advanced cities. As per LinkedIn, there are at least 1800 jobs available in the ML sector and the pace at which its growing, the numbers are likely to increase. It is due to the fact that industries are in need of skilled professionals who deeply understand machine learning. Reports suggest that since the sector is fairly new, it will take some amount of time before the demand is met, and even after that the growth that it has shown is a reflection of how huge this sector will be and the demand it attracts.

    Being the dream work for the engineering graduates in 2018, an occupation of a Machine learning engineer in Kolkata offers different advantages, for example, - 

    • High Pay - According to Indeed, machine learning engineer is the best job of 2019 due to growing demand and high salaries.
    • High demand- According to International Data Corporation (IDC), spending on AI and ML will grow from $12B in 2017 to $57.6B by 2021. As an ever-increasing number of organizations in Kolkata are embracing ML and AI, it is normal for this field to extend exponentially.

    The perks of ML engineer in Kolkata apart from the high salary are as follows - 

    • Acknowledgement 
    • Opportunity
    • Promising career
    • Exponentially high incentives.

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

    • Quantiphi
    • Tata Consultancy Services
    • Accenture
    • Microsoft
    • DemandMatrix
    • Difference-engine.ai
    • Depasser Infotech

    Machine Learning Conference in Kolkata, India

    S.NoConference NameDateVenue
    1.

    UpGrad Bootcamp

    July 20, 2019 to August 10, 2019

    Offbeat CCU, 36/F Topsia Road On EM Bypass, (Erstwhile Landmark Hotel), Kolkata, India

    2.

    Summer Training Hunt

    July 1, 2019 to August 1, 2019

    Offbeat CCU, 36/f Topsia Road, (erstwhile Landmark Hotel), Kolkata 700 039, India

    3.

    The IEEE Region 10 Symposium

    June 7, 2019 to June 9, 2019

    Kolkata, India

    1. UpGrad Bootcamp, Kolkata
      1. About the UpGrad Bootcamp conference: It is a boot camp organised by UpGrad to discuss scope and career opportunities related to Python and Machine learning.
      2. Event Date: July 20, 2019 to August 10, 2019
      3. Venue: Offbeat CCU, 36/F Topsia Road On EM Bypass, (Erstwhile Landmark Hotel), Kolkata, India
      4. Days of Program: 20
      5. Timings: 9:00 AM to 5:00 PM
      6. Registration cost: Rs. 10999
    2. Summer Training Hunt, Kolkata
      1. About the Summer Training Hunt conference: The event will have a discussion on Full Stack Web Design, Machine Learning with Python, Android App Development, Internet of Things.
      2. Event Date: July 1, 2019 to August 1, 2019
      3. Venue: Offbeat CCU, 36/F Topsia Road On EM Bypass, (Erstwhile Landmark Hotel), Kolkata, India
      4. Days of Program: 1 month
      5. Timings: 10:00 AM to 5:00 AM
      6. Purpose: To make the attendees aware of the technologies used in the field of Full Stack Web Design, Machine Learning with Python, Android App Development and Internet of Things
      7. Registration cost: Rs. 3999
    3. The IEEE Region 10 Symposium, Kolkata
      1. About the IEEE Region 10 Symposium conference: The theme of the conference is Technological Innovation for Humanity and the aim is to bring together professionals from different workplaces and gather their ideas.
      2. Event Date: June 7, 2019 to June 9, 2019
      3. Venue: Kolkata, India
      4. Days of Program: 3 Days
      5. Timings: 10:00 AM to 5:00 PM
      6. How many speakers: 2 
      7. Speakers & Profile:
        • Professor Lawrence O. Hall, Fellow IEEE, Department of Computer Science and Engineering
        • Prof. Subhas Chandra Mukhopadhyay, FIEEE (USA), FIEE (UK), FIETE (India), Distinguished Lecturer - IEEE Sensors Council, School of Engineering
    S.NoConference NameDateVenue
    1.7th International Conference On Pattern Recognition And Machine Intelligence18 August, 2017

    203, B.T. Road, Kolkata

    1. 7th International conference on pattern recognition and machine intelligence, Kolkata
      1. About the conference: Started in 2005 by Machine Intelligence Unit (MIU) of Indian Statistical Institute (ISI), Kolkata, this conference is being organized every alternate year and the invitees are some of the best people in the field of AI and Machine Learning.
      2. Date : 18 August, 2017
      3. Venue: 203, B.T. Road, Kolkata
      4. Number of days: 1
      5. Purpose: The primary goal of this conference was to present the state of the art scientific results, encourage academic as well as industrial interaction, and promote collaborative research activities in fields such as Pattern Recognition, Machine Intelligence and other related fields, involving scientists, engineers, professionals, researchers and students across the globe.
      6. Registration cost: ₹8000
      7. Major sponsor: Springer
      8. With whom you can network: You could network with some of the best scholars and experts in the field of Machine Learning and Artificial Intelligence
      9. Speakers :
        • David Zhang- Hong Kong Polytechnic University, Hong Kong
        • Vineet Bafna- University of California, San Diego, USA

    Machine Learning Engineer Jobs in Kolkata, India

    Machine Learning is a vast field. As a result, Machine Learning Engineers have to be responsible for a lot of things including:

    • Exploring data to understand it
    • Design systems required for performing data analysis
    • Implementing machine learning algorithms to this data
    • Build data and model pipelines
    • Performing tests and experiments on the data
    • Research and execute best practices to enhance the existing machine learning infrastructure

    Companies in Kolkata are starting to understand the importance of data in making marketing decisions. This has resulted in increase in demand of Machine Learning Engineers in Kolkata. They create frameworks and systems that facilitate the data analysis process. Also, to gain insights from the data, you need to apply Machine Learning algorithms to it.

    A Machine Learning Engineer based in Kolkata can find a job in one of the following companies:

    • EdeXcare Learning Services
    • ICS Consultancy Services
    • Sibia Analytics
    • Rebaca

    Here are the top professional groups for Machine Learning Engineers in Kolkata:

    In 2019, the following ML jobs are most in demand:

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

    Here are a few methods through which you can network with other Machine Learning Engineers in Kolkata:

    • Meetups
    • Conferences
    • Tech talks
    • LinkedIn and other online platforms

    The average annual income of a Machine Learning Engineer based in Kolkata is Rs 5,79,720.

    https://www.indeed.co.in/salaries/Machine-Learning-Engineer-Salaries,-Kolkata-WB  

    Machine Learning with Python Kolkata, India

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

    1. Download and install the Python SciPy Kit for Machine learning and install all useful packages.
    2. Go through all the tools, this will you get an idea of all the functionalities.
    3. Load a dataset and understand its structure and workings with the help of statistical summaries and data visualization.
    4. Gain a better understanding of the concepts by practicing on some of the most commonly used and popular datasets.
    5. Start small and work your way to bigger and more complicated projects.
    • Scikit-learn: Useful for Data mining, data analysis and in data science.
    • Numpy: Useful for N-dimensional arrays.
    • Pandas: Useful for high-level data structures, data extraction and preparation.
    • Matplotlib: Helps in the plotting of graph in 2D.

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

    1. Gather Data: The basic and the most important step is the gathering of data that is appropriate for a particular project. The quality and quantity of data will determine the performance of your ML model.
    2. Cleaning and preparing data: Next step is to clean the data. This is done so that the data corresponds to the model of ML that is being used. 
    3. Visualize the data: This step helps in understanding the data in our hands, helping in selecting the correct model.
    4. Choosing the correct model: The kind of ML model that will be ideal for harvesting the given data is important. It will help determine the performance of the algorithm.
    5. Training and testing: The training data set is passed through the model to train it with the new set of data, then the accuracy of the model is checked by passing the test data.
    6. Adjust parameters: After determining the accuracy of the model, the parameters are fine tuned. 

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

    1. Consistency is key: Practice everyday. Consistency is really important when you are learning a programming language.
    2. Write it out: This might seem like an age old myth but writing and taking notes as you read is an excellent way to retain knowledge in our memory. 
    3. Get interactive: The interactive Python shell is a really helpful learning tool whether you are writing codes or getting to know dictionaries, lists, strings or debugging an application. Just open your terminal and type Python/Python3 to initiate Python shell.
    4. Do not let the bugs frustrate you: It is inevitable that you will run into bugs. The best way to pick up basic Python programming skills is to sit down and solve the bugs on your own. 
    5. Surround yourself with other people who are learning: Coding may seem like a solitary activity, but it actually brings out the best results when it is done in a collaborative manner. Join meetups or boot camps in Kolkata to meet people from the same field.
    • Scikit-learn
    • SciPy
    • Numpy
    • Keras
    • TensorFlow
    • Pandas
    • Matplotlib
    • Pytorch

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    Faqs

    The Course

    Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data. Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!

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

    By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and be able to build applications models. This will help you land lucrative jobs as a Data Scientist.

    There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

    Yes, KnowledgeHut offers this training online.

    On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

    Your instructors are Machine Learning experts who have years of industry experience.

    Finance Related

    Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details.

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

    The Remote Experience

    In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

    Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.

    Have More Questions?

    Mail Us support@knowledgehut.com

    Machine Learning with Python Course in Kolkata

    Machine Learning with Python Training in Kolkata

    Kolkata, earlier known as Calcutta in English, is the capital of West Bengal. The city continues to develop in urban courses as it is being presented to industrialization, information innovation, amongst other developments. Seeing the present market situation, the city is in a profound need for programming engineers. KnowledgeHut is offering e-learning in machine learning training using python in Kolkata to enterprises for viable improvement of activities and improving efficiency. The course offers complete information on data analysis and machine learning which are perfect for programming engineers.

    What is Machine Learning course all about?

    This course will introduce the learner to manage large volumes of data that needs to be analyzed using the machine learning techniques in Python. Offering a host of example and real-life cases brings ease to proceed with the machine learning using python course in Kolkata while gaining real-time knowledge through an on-going project. The course will begin with a dialogue about how machine learning and data analysis using python course in Kolkata is not the same as graphic measurements with an introduction to the skit learn toolbox. Touching on the issue of data dimensionality and the errands of clustering data, this course will also help evaluate those bunches.

    Benefits of the Machine Learning certification in Kolkata

    Before the finish of this course, aspirants will have the capacity to distinguish the contrast between a managed and unsupervised method, recognize which procedure they have to apply for a specific dataset and need, engineer components to address that issue, and compose python code to complete an analysis. The KnowledgeHut Way The cost of the machine learning and data analysis training using python in Kolkata is very nominal. The way that the sessions are directed online using web-strategies makes it advantageous for all learners making the most of the expertise of the trained and certified workforce.