Machine Learning with Python Training in Kolkata, India

Build Python skills with varied approaches to Machine Learning

  • Study Supervised & Unsupervised Learning and various optimization techniques 
  • Enhance your skills on ML algorithms like KNN, Decision Trees and Clustering 
  • Learn about Bootstrap Sampling and its advantages 
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Grow your Machine Learning skills

Join KnowledgeHut’s 4-week Machine Learning with Python course and strengthen your fundamentals of Machine Learning using Python. Get familiar with techniques related to data exploration and build a solid foundation through machine learning approaches like supervised and unsupervised learning, regression, and classifications, and others.

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Highlights

  • 48 Hours of Live Instructor-Led Sessions

  • 80 Hours of Assignments and MCQs

  • 45 Hours of Hands-On Practice

  • 10 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

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Why learn Machine Learning with Python in Kolkata

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Machine Learning jobs are the most sought-after jobs across the world, including Kolkata. Organizations are looking for skilled data analysts with strong machine learning capabilities in the post-COVID world. As Machine Learning and AI take the spotlight in today’s world, the demand for data engineers and data scientists is exponentially increasing.

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Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on. 

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Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

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Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

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Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

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Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for Machine Learning with Python training

  • Sufficient knowledge of at least one coding language is required.
  • Minimalistic and intuitive, Python is best-suited for Machine Learning training.

Who should attend the Machine Learning with Python Course?

Anyone interested in Machine Learning and using it to solve problems

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Machine Learning with Python Course Schedules for Kolkata

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What you will learn in the Machine Learning with Python course

Python for Machine Learning

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

Fundamentals of Machine Learning

Learn Machine Learning with Python, including Supervised and Unsupervised Machine Learning.  

Optimization Techniques

Learn to use optimization techniques to find the minimum error in your Machine Learning model.

Supervised Learning

Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers.

Unsupervised Learning

Study K-means Clustering  and Hierarchical Clustering.

Ensemble techniques

Learn to use multiple learning algorithms to obtain better predictive performance .

Neural Networks

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

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Skills you will gain with the Machine Learning with Python course

Advanced Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Distribution of data: variance, standard deviation, more

Calculating conditional probability via Hypothesis Testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Logistic Regression models

K-means Clustering and Hierarchical Clustering

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for both regression and classification

Hyper-parameter tuning like regularisation

Ensemble techniques: averaging, weighted averaging, max voting

Bootstrap sampling, bagging and boosting

Building Random Forest models

Finding optimum number of components/factors

PCA/Factor Analysis

Using Apriori Algorithm and key metrics: Support, Confidence, Lift

Building recommendation engines using UBCF and IBCF

Evaluating model parameters

Measuring performance metrics

Using scree plot, one-eigenvalue criterion

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Machine Learning with Python Training Curriculum

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1.

Learning objectives
In this module, you will learn the basics of statistics including:

  • Basics of statistics like mean (expected value), median and mode 
  • Distribution of data in terms of variance, standard deviation, and interquartile range; and explore data and measures and simple graphics analyses  
  • Basics of probability via daily life examples 
  • Marginal probability and its importance with respect to Machine Learning 
  • Bayes’ theorem and conditional probability including alternate and null hypotheses  

Topics

  • Statistical Analysis Concepts  
  • Descriptive Statistics  
  • Introduction to Probability 
  • Bayes’ Theorem  
  • Probability Distributions  
  • Hypothesis Testing and Scores  

Hands-on

  • Learning to implement statistical operations in Excel 
2.

Learning objectives
In the Python for Machine Learning module, you will learn how to work with data using Python:

  • How to define variables, sets, and conditional statements 
  • The purpose of 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 with Python 
  • Data Visualization using Python libraries like matplotlib, seaborn and ggplot 

Topics

  • Python Overview  
  • Pandas for pre-Processing and Exploratory Data Analysis  
  • NumPy for Statistical Analysis  
  • Matplotlib and Seaborn for Data Visualization  
  • Scikit Learn 
3.

Learning objectives
Get introduced to Machine Learning via real-life examples and the multiple ways in which it affects our society. You will learn:

  • Various algorithms and models like Classification, Regression, and Clustering.  
  • Supervised vs Unsupervised Learning 
  • How Statistical Modelling 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 and Underfitting  
4.

Learning objectives
Gain an understanding of various optimisation techniques such as:

  • Batch Gradient Descent 
  • Stochastic Gradient Descent 
  • ADAM 
  • RMSProp

Topics

  • Maxima and Minima  
  • Cost Function  
  • Learning Rate  
  • Optimization Techniques  
5.

Learning objectives
In this module you will learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies

  • Hyper-parameters tuning like learning rate, epochs, momentum, and class-balance 
  • The concepts of Linear and Logistic Regression with real-life case studies 
  • How KNN can be used for a classification problem with a real-life case study on KNN Classification  
  • About Naive Bayesian Classifiers through another case study 
  • How Support Vector Machines can be used for a classification problem 
  • About hyp

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

  • Build a regression model to predict the property prices using optimization techniques like gradient descent based on attributes describing various aspect of residential homes 
  • Use 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 based on the health metrics 
  • Use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham 
  • Build models to study the relationships between chemical structure and biodegradation of molecules to correctly classify if a chemical is biodegradable or non-biodegradable 
6.

Learning objectives
Learn about unsupervised learning techniques:

  • K-means Clustering  
  • Hierarchical Clustering  

Topics

  • Clustering approaches  
  • K Means clustering  
  • Hierarchical clustering  
  • Case Study 

Hands-on

  • Perform a real-life case study on K-means Clustering  
  • Use K-Means clustering to group teen students into segments for targeted marketing campaigns
7.

Learning objectives
Learn the ensemble techniques which enable you to build machine learning models including:

  • Decision Trees for regression and classification problems through a real-life case study 
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID 
  • Basic ensemble techniques like averaging, weighted averaging and max voting 
  • You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting 
  • Random Forest, with a real-life case study, and how it helps avoid overfitting compared to decision trees 
  • The Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis 
  • The comprehensive techniques used to find the optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • PCA/Factor Analysis via a case study 

Topics

  • Decision Trees with a Case Study 
  • Introduction to Ensemble Learning  
  • Different Ensemble Learning Techniques  
  • Bagging  
  • Boosting  
  • Random Forests  
  • Case Study  
  • PCA (Principal Component Analysis)  
  • PCA 
  • Its Applications  
  • Case Study

Hands-on

  • Build a model to predict the Wine Quality using Decision Tree (Regression Trees) based on the composition of ingredients 
  • Use AdaBoost, GBM, and Random Forest on Lending Data to predict loan status and ensemble the output to see your results 
  • Apply Reduce Data Dimensionality on a House Attribute Dataset to gain more insights and enhance modelling.  
8.

Learning objectives
Learn to build recommendation systems. You will learn about:

  • Association Rules 
  • Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift 
  • UBCF and IBCF including how they are used in Recommender Engines 

Topics 

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

Hands-on

  • Build a Recommender System for a Retail Chain to recommend the right products to its customers 

FAQs on Machine Learning with Python Course in Kolkata

Machine Learning with Python Training

KnowledgeHut’s Machine Learning with Python workshop is focused on helping professionals gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals land lucrative jobs across industries. At the end of the course, you will be able to: 

  • Build Python programs: distribution, user-defined functions, importing datasets and more 
  • Manipulate and analyse data using Pandas library 
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot 
  • Build data distribution models: variance, standard deviation, interquartile range 
  • Calculate conditional probability via Hypothesis Testing 
  • Perform analysis of variance (ANOVA) 
  • Build linear regression models, evaluate model parameters, and measure performance metrics 
  • Use Dimensionality Reduction 
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics 
  • Perform K-means Clustering and Hierarchical Clustering  
  • Build KNN algorithm models to find the optimum value of K  
  • Build Decision Tree models for both regression and classification problems  
  • Use ensemble techniques like averaging, weighted averaging, max voting 
  • Use techniques of bootstrap sampling, bagging and boosting 
  • Build Random Forest models 
  • Find optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • Perform PCA/Factor Analysis 
  • Build Apriori algorithms with key metrics like Support, Confidence and Lift 
  • Build recommendation engines using UBCF and IBCF 

The program is designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the course covers everything you need to know, whether you’re a novice or an expert. 

To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities. 

This immersive and interactive workshop with an industry-relevant curriculum, capstone project, and guided mentorship is your chance to launch a career as a Machine Learning expert. The curriculum is split into easily comprehensible modules that cover the latest advancements in ML and Python. The initial modules focus on the technical aspects of becoming a Machine Learning expert. The succeeding modules introduce Python, its best practices, and how it is used in Machine Learning.  

The final modules deep dive into Machine Learning and take learners through the algorithms, types of data, and more. In addition to following a practical and problem-solving approach, the curriculum also follows a reason-based learning approach by incorporating case studies, examples, and real-world cases.

Yes, our Machine Learning with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. 

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Machine Learning with Python course is ideal for:
  1. Anyone interested in Machine Learning and using it to solve problems  
  2. Software or Data Engineers interested in quantitative analysis with Python  
  3. Data Analysts, Economists or Researchers 

There are no prerequisites for attending this course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below

Hardware requirements 

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors 
  • 4 GB RAM 
  • 10 GB of free space  

Software Requirements  

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox  

System Requirements 

  • 32 or 64-bit Operating System 
  • 8 GB of RAM 

On adequately completing all aspects of the Machine Learning with Python course, you will be offered a course completion certificate from KnowledgeHut.  

In addition, you will get to showcase your newly acquired Machine Learning skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects. 

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.  

Workshop Experience

The Machine Learning with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.  

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.    

The Machine Learning with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Machine Learning. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects. 

Our Machine Learning course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.  

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it. 

Schedules for our upcoming workshops in Machine Learning with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.   

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.  

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing. 

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger. 

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us on support@knowledgehut.com and we will be happy to get back to you. 

Additional FAQs on Machine Learning with Python Training in Kolkata

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:

    • StepUp Analytics Kolkata – Learn Data Science
    • Kolkata Artificial Intelligence & Deep Learning Online
    • Analytics Vidhya Kolkata
    • Big Data & Business Analytics – Asia Pacific, 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

    What learners are saying

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    Shifa Al Kiyumi RTO Engineer
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    A useful course, I acquired knowledge about Python, Machine Learning Modeling Flow, Treating Data, Statistical Learning and other topics. I will use this training and even the recorded videos and materials from knowledge Hut for future projects.

    Attended Machine Learning with Python workshop in July 2021

    N
    Neil Radia Enterprise Sales Executive
    5

    5 stars, What a totally awesome Data Science bootcamp! I tried learning on my own through text books and online material, but it was such a struggle as I had no one to clear my doubts. Knowledgehut has brought out a totally different and interactive, comprehensive, logical systematic approach to the subject that made it super fun to learn. Love all your courses(This is my fifth!).

    Attended Data Science Bootcamp workshop in July 2021

    B
    Ben Johnson Developer
    5

    The Backend boot camp is a great, beginner-friendly program! I started from zero knowledge and learnt everything through the learn-by-doing method. 

    Attended Back-End Development Bootcamp workshop in July 2021

    M
    Madeline R Developer
    5

    I know from first-hand experience that you can go from zero and just get a grasp on everything as you go and start building right away. 

    Attended Back-End Development Bootcamp workshop in April 2021

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    Madeline R Developer
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    I know from first-hand experience that you can go from zero and just get a grasp on everything as you go and start building right away. 

    Attended Front-End Development Bootcamp workshop in April 2021

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    I am glad to have attended KnowledgeHut's training program. Really I should thank my friend for referring me here. I was impressed with the trainer who explained advanced concepts thoroughly and with relevant examples. Everything was well organized. I would definitely refer some of their courses to my peers as well.

    Attended PMP® Certification workshop in May 2020

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    The course materials were designed very well with all the instructions. The training session gave me a lot of exposure to industry relevant topics and helped me grow in my career.

    Attended PMP® Certification workshop in June 2020

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

    Attended Agile and Scrum workshop in January 2020

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    Machine Learning with Python Certification Training in Kolkata

    About Kolkata 

    Kolkata, earlier known as Calcutta, is the capital of West Bengal. The city continues to develop in industrialization, information technology and innovation amongst other developments. Seeing the present market situation in the city, Kolkata profoundly needs programming engineers with strong skills in Machine Learning.  

    What is Machine Learning with Python course all about? 

    This course will teach you how to manage large volumes of data and analyze it using Python. Offering a host of real-world examples and real-life cases, you will learn all your fundamentals easily while gaining real-time knowledge through an on-going project. The course touches on the issue of data dimensionality and the techniques of clustering data. 

    Benefits of the Machine Learning certification in Kolkata 

    KnowledgeHut is offering e-learning courses in Machine Learning using Python in Kolkata where participants will get continual learning support. The course features a curriculum designed by industry experts covering the best practices in Machine Learning and Data Analysis.  

    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. 

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