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Machine Learning Course in Kanpur

80,121 Ratings

Our machine learning course in Kanpur by Intellipaat is curated by experienced ML experts to help you master Regression, Git, Tableau, PySpark, TensorFlow, etc. This machine learning certification course will be taught by top industry experts while working on real-time projects and case studies.

Ranked #1 Data Science Program by India TV

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ML Course Key Features

50+ Live sessions across 7 months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from Top Industry Practitioners
1:1 with Industry Mentors
Resume Preparation and LinkedIn Profile Review
24*7 Support
No-cost EMI Option
Trustpilot 3332
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Machine Learning Course in Kanpur Overview

What machine learning skills will you acquire in this machine learning course in Kanpur?

The following concepts are covered in this machine learning course in Kanpur:

  • Deploying Machine Learning models on Clouds ( MLOps)
  • Data analysis, project life cycle, and Data Science in the real world
  • Python with Data Science
  • Techniques of evaluation, experimentation, and project deployment
  • Natural Language Processing and its applications
  • Git, Storytelling
  • Machine Learning algorithms
  • Data visualization with Tableau
  • Data Science at scale with PySpark, AI with TensorFlow
  • Analysis segmentation using clustering and the technique of prediction
  • Microsoft Excel for data analysis and data transformation

This machine learning training in Kanpur is well-suited for the following professionals:

  • Analytics professionals
  • Data Scientists
  • BI professionals
  • Graduates who are aiming to pursue a career in the ML domain

There are no prerequisites for learning from this course. Anyone with an interest in upgrading their skills in this field can take up the best machine learning course in Kanpur.

  • The average annual pay of a Machine Learning Engineer in UP is ₹688,000 p.a. – PayScale
  • The demand for Machine Learning Engineers is likely to increase by 60% in the next few years – Forbes
  • Kanpur has 200+ ML jobs open for certified professionals – Indeed
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Machine Learning engineers rank among the top emerging jobs. - LinkedIn
The global Machine Learning market is expected to reach USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% - MarketsandMarkets

Career Transition

57% Average Salary Hike

$1,14,000 Highest Salary

12000+ Career Transitions

300+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Meet the Machine Learning Mentors

Skills Covered

Python

Data Science

Data Analysis

AI

GIT

MLOps

Data Wrangling

SQL

Story Telling

Machine Learning

Prediction algorithms

NLP

PySpark

Model

Data visualization

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Machine Learning Tools Covered

python jupyter Scipy numpy pandas matplotlib tensorflow SQL tableau excel git
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Machine Learning Course Fees in Kanpur

Online Classroom Preferred

  • Everything in Self-Paced Learning, plus
  • 50+ Live sessions across 7 months of Instructor-led Training
  • One to one doubt resolution sessions
  • Attend as many batches as you want for Lifetime
  • Job Assistance
27 Apr

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

04 May

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

11 May

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

18 May

SAT - SUN

08:00 PM TO 11:00 PM IST (GMT +5:30)

$1,229 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

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Machine Learning Course in Kanpur Curriculum

Live Course Industry Expert Academic Faculty

Module 1 – Preparatory Session - Python

Preview

Python 

  • Introduction to Python and IDEs – The basics of the Python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
  • Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
  • Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
  • Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding. 

Module 2 – Data Wrangling with SQL

Preview

SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables 

Advanced SQL –  

  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

  • Types of UDFs, Inline table value, multi-statement table. 
  • Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions.

Hands-on exercise: 

Writing comparison data between the past year and the present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Extract Transform Load

  • Web Scraping, Interacting with APIs

Data Handling with NumPy

  • NumPy Arrays, CRUD Operations, etc.
  • Linear Algebra – Matrix multiplication, CRUD operations, Inverse, Transpose, Rank, Determinant of a matrix, Scalars, Vectors, Matrices.

Data Manipulation Using Pandas

  • Loading the data, data frames, series, CRUD operations, splitting the data, etc.

Data Preprocessing

  • Exploratory Data Analysis, Feature engineering, Feature scaling, Normalization, standardization, etc.
  • Null Value Imputations, Outliers Analysis and Handling, VIF, Bias-variance trade-off, cross-validation techniques, train-test split, etc.

Data Visualization

  • Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc. with Python matplotlib.
  • Regression plots, categorical plots, area plots, etc, with Python seaborn.

Descriptive Statistics – 

  • Measure of central tendency, the measure of spread, five points summary, etc. 

Probability 

  • Probability Distributions, Bayes’ theorem, central limit theorem. 

Inferential Statistics –  

  • Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Introduction to Machine Learning 

  • Supervised, Unsupervised Learning.
  • Introduction to scikit-learn, Keras, etc.

Regression 

  • Introduction classification problems, Identification of a regression problem, dependent and independent variables.
  • How to train the model in a regression problem.
  • How to evaluate the model for a regression problem.
  • How to optimize the efficiency of the regression model.

Classification 

  • Introduction to classification problems, Identification of a classification problem, and dependent and independent variables.
  • How to train the model in a classification problem.
  • How to evaluate the model for a classification problem.
  • How to optimize the efficiency of the classification model.

Clustering 

  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  • How to train the model in a clustering problem.
  • How to evaluate the model for a clustering problem.
  • How to optimize the efficiency of the clustering model.

Supervised Learning 

  • Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
  • Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
  • Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
  • Random Forest – Creating random forest models for classification problems in a supervised learning approach.
  • Support Vector Machine – SVM or support vector machines for regression and classification problems.
  • Gradient Descent – Gradient descent algorithm that is an iterative optimization approach to finding the local minimum and maximum of a given function.
  • K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
  • Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Unsupervised Learning 

  • K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
  • Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
  • Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
  • Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Artificial Intelligence Basics 

  • Introduction to keras API and TensorFlow

Neural Networks

  • Neural networks
  • Multi-layered Neural Networks
  • Artificial Neural Networks 

Deep Learning

  • Introduction to Deep Learning
  • Deep neural networks
  • Convolutional Neural Networks 
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, Restricted Boltzmann machine

The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

  • Extracting, loading and transforming data into usable format to gather insights. 
  • Data manipulation and handling to pre-process the data.
  • Feature engineering and scaling the data for various problem statements. 
  • Model selection and model building on various classification, regression problems using supervised/unsupervised Machine Learning algorithms.
  • Assessment and monitoring of the model created using the Machine Learning models.

Electives:

Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data ,M Query and Hierarchies in Power BI.

DAX 

  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics  

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium

Hands-on Exercise:

Creating a dashboard to depict actionable insights in sales data.

  • Job Search Strategy
  • Resume Building
  • Linkedin Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.

Excel Fundamentals 

  • Reading the Data, Referencing in formulae , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
  • Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
  • VBA Macros, Ranges and Worksheet in VBA
  • IF conditions, loops, Debugging, etc.

Excel For Data Analytics 

  • Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.

Data Visualization with Excel

  • Charts, Pie charts, Scatter and bubble charts
  • Bar charts, Column charts, Line charts, Maps
  • Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Excel Power Tools 

  • Power Pivot, Power Query and Power View

Classification Problems using Excel

  • Binary Classification Problems, Confusion Matrix, AUC and ROC curve
  • Multiple Classification Problems  

Information Measure in Excel

  • Probability, Entropy, Dependence
  • Mutual Information

Regression Problems Using Excel

  • Standardization, Normalization, Probability Distributions
  • Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
  • Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

Hands-on Exercise:

Classification problem using excel on sales data, and statistical tests on various samples from the population.

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Machine Learning Training in Kanpur Projects

Career Services

Career Services
resume

Career Oriented Sessions

Throughout the course

Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions that will help you stay on track with your upskilling.

linkedin

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage

interview

Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

expert

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learner’s educational background, past experience, and future career aspirations.

guaranteed

Placement Assistance

Upon movement to the Placement Pool

Placement opportunities are provided once the learner is moved to the placement pool upon clearing Placement Readiness Test (PRT)

job_portal

Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Machine Learning Certification in Kanpur

Machine-Learning-Course Click to Zoom

These machine learning courses in Kanpur aim to make you an expert through various projects and assignments, which will give you exposure to real-world industry scenarios. This way, you can expedite your career effortlessly.

Machine learning certification course from Intellipaat will be issued once you successfully work on the projects assigned to you during the ML training in Kanpur (after review) and score at least 60% in the quiz.

You would be glad to know that Intellipaat’s Machine Learning certification is well-recognized by more than 500 top MNCs, including Cisco, Ericsson, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, IBM, Infosys, Genpact, TCS, Hexaware, and more.

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Machine Learning Course in Kanpur FAQs

Can Intellipaat provide machine learning course near me in Kanpur?

Intellipaat has been serving ML enthusiasts from every corner of the city. You can be living in any locality in Kanpur, be it Arya Nagar, Swaroop Nagar, Ashok Nagar, Harsh Nagar, Tilak Nagar, Sarvodaya Nagar, Acharya Nagar, Jawahar Nagar, Pandu Nagar, Gandhi Nagar, Civil Lines, Vikas Nagar or anywhere. You can have full-access to our machine learning online course sitting at home or office 24/7.

Our teaching assistant team comprises SMEs, who help trainees in diverse aspects, including assignment sessions, project evaluation, and doubt clearance.

If you miss a class, then you can contact our customer support team, who will assist you in scheduling another class for the same topic so that you can catch up with the rest.

Besides, all the sessions are recorded and shared with all participants in the LMS (learning management system), so you can refer to these recorded sessions to make up for the missed class.

Intellipaat provides comprehensive teaching in machine learning through hands-on projects and case studies. A few of the many reasons for choosing Intellipaat ML certification course includes the following:

  • You will learn various concepts such as ML using Python, classification techniques, linear algebra behind linear regression along with logistic regression, supervised and unsupervised learning, and more.
  • After successfully completing the lectures, you will be awarded Intellipaat’s certificate, which holds merit in 100+ MNCs across the world.
  • This program covers real-time ML projects and step-by-step tasks that are highly relevant in the corporate world. It also includes an extensive curriculum, created by industry experts.
  • Our certification training will allow you to compete for some of the best positions in the world’s leading MNCs for higher salaries.

We provide lifetime access to videos, resources and their free upgrades to the latest version, and 24/7 learning support.

Machine learning is basically the process to collect real-world data, extract useful information from it, and then take actions to perform certain tasks without manual programming. It helps systems improve over time on their own by exploring various types of real-world data. It also allows organizations to improve their business strategies by knowing the insights that are extracted from the given business data.

No doubt, machine learning is in high demand, and at the same time, employers need professionals who have the right skills for building applications for the future.

Here, at Intellipaat, we create our program by taking into consideration our learners come from varied backgrounds. So, we curate it from the basic level and gradually increase the difficulty level for you to easily grasp all the concepts taught as part of the program. Further, we make sure that, by the end of the program, your skills would be equivalent to 6-month experience in this technology.

You can access all the lectures and assignments the moment you enroll in this program. Also, Intellipaat provides lifelong access to the complete material for you to refer to it as and when required.

Yes, Intellipaat offers several group discounts for our classroom program, depending on the group size and type. To avail of the group discount for our machine learning advance course, you need to contact our course advisors, who will help you with all your doubts regarding the discount offers we provide.

Intellipaat offers the facility of integrated labs that act as a platform for you to execute our industry-based projects. You will be guided through the steps so that you can easily deploy all the necessary tools and further execute the hands-on exercises successfully.

Intellipaat’s Machine Learning program provides material that comprises all the modules that are necessary to learn this popular technology. These pre-recorded video lectures and material are extremely effective, as they allow you to complete the whole program at your own pace and take your time to learn the concepts thoroughly.

After completing this certification training, you will be awarded the certificate from us, which is valid for a lifetime.

Intellipaat’s certificate is recognized by over 500 top MNCs across the world, including companies such as Sony, Mu Sigma, Cisco, IBM, Standard Chartered Bank, TCS, Infosys, Ericsson, Genpact, Cognizant, etc.

Intellipaat offers query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail yourself of email support for all your queries. We can also arrange one-on-one sessions with our support team If your query does not get resolved through email. However, 1:1 session support is given for 6 months from the start date of your course.

Intellipaat provides placement assistance to all learners who have completed the training and moved to the placement pool after clearing the PRT (Placement Readiness Test). More than 500+ top MNCs and startups hire Intellipaat learners. Our alumni work with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

Apparently, no. Our job assistance is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final hiring decision will always be based on your performance in the interview and the requirements of the recruiter.

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