Courses ×

Back

Corporate Training Explore Courses
Intellipaat collaboration image

Machine Learning Training Course in New Jersey

4.8 (578 Ratings)

The Machine Learning course in New Jersey by Intellipaat is prepared by experts considering the demand for a Machine Learning Engineer. It also covers concepts like classification techniques, NLP, Git, PySpark etc. This Machine Learning certification training in New Jersey can help you become a certified ML expert.

Key Features

50+ Live sessions across seven months
218 Hrs Self-paced Videos
200 Hrs Project & Exercises
Learn from IIT Madras Faculty & Industry Practitioners
1:1 with Industry Mentors
3 Guaranteed Interviews by Intellipaat
24*7 Support
No Cost EMI Option

Machine Learning Course in New Jersey Overview

Why should I learn Machine Learning?

  • As per Indeed, the average income of Machine Learning Engineers is about US$140,579 per annum in the United States
  • There are over 7332 Machine Learning jobs available on LinkedIn in India alone
  • The growth rate for Machine Learning jobs is about 350%!
  • Automation is the trending face of technology

In the world we live in today, ML has proved itself to be among the hottest and demanding technologies available out there.

Hence, by leveraging Intellipaat’s Machine Learning online training, you will be exposed to numerous high-paying job opportunities.

The ML course content is taught by the trainer from scratch. Therefore, anyone can sign up for this Machine Learning online course in New Jersey.

This Machine Learning classes in New Jersey will strengthen your conceptual base of Machine Learning by the implementation of every concept. In this best Machine Learning course, you will acquire skills such as:

  • Theoretical knowledge of Machine Learning concepts
  • Mathematics and logic behind the Machine Learning techniques
  • Problem-solving skills with the help of Machine Learning
  • Experience in handling real-time projects

Today, most of the organizations implement automation for making effective and fast business processes. This leads to a great demand for Machine Learning Engineers and makes it the best career choice. Therefore, pursuing a Machine Learning certification in New Jersey is worth it.

The topics included in one of the best Machine Learning courses in New Jersey are linear and logistic regression, decision tree, random forest, unsupervised Learning techniques, SVM, Naive Bayes, neural networks, and Natural Language Processing. This Machine Learning course instructor will teach you the concepts by the implementation to provide you hands-on experience.

You will be working on real-life projects and case studies with the Machine Learning course instructor of Intellipaat, one of the leading Machine Learning Institutes in New Jersey. The case study includes insurance cost prediction, diabetes classification, Principal Component Analysis, etc. The projects comprise of Customer Churn Classification and Movie Recommendation System.

View More

Talk To Us

We are happy to help you 24/7

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

Meet Your 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

View More

Tools Covered

tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop tool-desktop
View More

Course Fees

Self Paced Training

  • 218 Hrs e-learning videos
  • 3 Guaranteed Interviews by Intellipaat
  • 24*7 Support

$211

Online Classroom Preferred

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

SAT - SUN

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

28 May

SAT - SUN

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

04 Jun

SAT - SUN

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

11 Jun

SAT - SUN

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

$1,492 10% OFF Expires in

Corporate Training

  • Customized Learning
  • Enterprise grade learning management system (LMS)
  • 24x7 Support
  • Enterprise grade reporting

Contact Us

Machine Learning Course Curriculum in New Jersey

Live Course Self Paced

Module 1 – Preparatory Session - Linux and 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. 

Linux

  • Introduction to Linux  – Establishing the fundamental knowledge of how linux works and how you can begin with Linux OS. 
  • Linux Basics – File Handling, data extraction, etc.
  • Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux. 

Module 2 – Data Analysis With MS-Excel

Preview

Excel Fundamentals 

  • Reading the Data, Referencing in formulas , 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

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, triggers, etc. 

SQL Optimization and Performance

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

Version Control 

  • What is version control, types, SVN.

GIT 

  • Git Lifecycle, Common Git commands, Working with branches in Git
  • Github collaboration (pull request), Github Authentication (ssh and Http)
  • Merging branches, Resolving merge conflicts, Git workflow

Descriptive Statistics – 

  • Measure of central tendency, 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.

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, dataframes, 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

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, 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 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.

Performance Metrics

  • Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc. 
  • Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model. 
  • r2, adjusted r2, mean squared error, etc.

Artificial Intelligence Basics 

  • Introduction to keras API and tensorflow

Neural Networks

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

Deep Learning 

  • Deep neural networks
  • Convolutional Neural Networks 
  • Recurrent Neural Networks
  • GPU in deep learning
  • Autoencoders, restricted boltzmann machine 

Text Mining, Cleaning, and Pre-processing

  • Various Tokenizers, Tokenization, Frequency Distribution, Stemming, POS Tagging, Lemmatization, Bigrams, Trigrams & Ngrams, Lemmatization, Entity Recognition.

Text classification, NLTK, sentiment analysis, etc  

  • Overview of Machine Learning, Words, Term Frequency, Countvectorizer, Inverse Document Frequency, Text conversion, Confusion Matrix, Naive Bayes Classifier.

Sentence Structure, Sequence Tagging, Sequence Tasks, and Language Modeling

  • Language Modeling, Sequence Tagging, Sequence Tasks, Predicting Sequence of Tags, Syntax Trees, Context-Free Grammars, Chunking, Automatic Paraphrasing of Texts, Chinking.

AI Chatbots and Recommendations Engine 

  • Using the NLP concepts, build a recommendation engine and an AI chatbot assistant using AI. 

RBM and DBNs & Variational AutoEncoder

  • Introduction rbm and autoencoders
  • Deploying rbm for deep neural networks, using rbm for collaborative filtering
  • Autoencoders features and applications of autoencoders.

Object Detection using Convolutional Neural Net

  • Constructing a convolutional neural network using TensorFlow
  • Convolutional, dense, and pooling layers of CNNs
  • Filtering images based on user queries

Generating images with Neural Style and Working with Deep Generative Models

  • Automated conversation bots leveraging
  • Generative model, and the sequence to sequence model (lstm).

Distributed & Parallel Computing for Deep Learning Models

  • Parallel Training, Distributed vs Parallel Computing
  • Distributed computing in Tensorflow, Introduction to tf.distribute
  • Distributed training across multiple CPUs, Distributed Training
  • Distributed training across multiple GPUs, Federated Learning
  • Parallel computing in Tensorflow

Reinforcement Learning

  • Mapping the human mind with deep neural networks (dnns)
  • Several building blocks of artificial neural networks (anns)
  • The architecture of dnn and its building blocks
  • Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Deploying Deep Learning Models and Beyond

  • Understanding model Persistence, Saving and Serializing Models in Keras, Restoring and loading saved models
  • Introduction to Tensorflow Serving, Tensorflow Serving Rest, Deploying deep learning models with Docker & Kubernetes, Tensorflow Serving Docker, Tensorflow Deployment Flask.
  • Deploying deep learning models in Serverless Environments
  • Deploying Model to Sage Maker
  • Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow

Introduction to MLOps 

  • MLOps lifecycle
  • MLOps pipeline 
  • MLOps Components, Processes, etc

Deploying Machine Learning Models 

  • Introduction to Azure Machine Learning 
  • Deploying Machine Learning Models using Azure

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

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.

Introduction to Big Data And Spark

  • Apache spark framework, RDDs, Stopgaps in existing computing methodologies

RDDs 

  • RDD persistence, caching, General operations: Transformation, Actions, and Functions.
  • Concept of Key-Value pair in RDDs, Other pair, two pair RDDs
  • RDD Lineage, RDD Persistence, WordCount Program Using RDD Concepts
  • RDD Partitioning & How it Helps Achieve Parallelization

Advanced Concepts & Spark-Hive

  • Passing Functions to Spark, Spark SQL Architecture, SQLContext in Spark SQL
  • User-Defined Functions, Data Frames, Interoperating with RDDs
  • Loading Data through Different Sources, Performance Tuning
  • Spark-Hive Integration
View More

Free Career Counselling

We are happy to help you 24/7

Machine Learning Projects

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, make new friends — the possibilities are endless and our community has something for everyone!

Intellipaat
Intellipaat
Intellipaat
Intellipaat
Intellipaat

Career Services

Career Services
Resume

Career Oriented Sessions

Throughout the course

Over 20+ 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 and that will help you stay on track with your up skilling objective.

Resume

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 profile shortlisting stage

Resume

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.

Resume

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 learners’ educational background, past experience, and future career aspirations.

Resume

Assured Interviews

After 80% of the course completion

Assured Interviews upon submission of projects and assignments. Get interviewed by our 500+ hiring partners.

Resume

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 New Jersey

As part of this ML program, you will be engaged in various projects and assignments, which include real-world industry scenarios. This way, you can expedite your career effortlessly.

Intellipaat’s certificate will be issued once you successfully work on the project (after expert review) and score at least 60 percent in the quiz.

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

Machine Learning Training Reviews in New Jersey

4.8 ( 2,503 )

Our Alumni Work At

Master Client Desktop

FAQ’s on Machine Learning Certification Course

Why should I learn Machine Learning Course in New Jersey from Intellipaat?

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

  • You will learn various concepts such as ML using Python, classification techniques, linear and 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.

Intellipaat is the leading course provider in New Jersey. Our E-learning Institute provides top courses in Artificial Intelligence, Data Science, R programming language, and others to help you gain full control of your ML career path.

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.

Intellipaat’s Machine Learning certification course is curated by industry experts who cover both basic and advanced concepts of this trending technology. Further, this program covers all the topics with the help of several real-life examples, which are extremely useful, especially for beginners.

Here, you will learn mathematics, statistics, etc. and then go on gaining in-depth knowledge of ML. So, if you are a beginner in this field and are aiming to pursue a career in it, then this is the best platform for you.

Among various other institutes that provide the python Machine Learning course online, Intellipaat offers it at the most reasonable and affordable rate. It provides two modes of training, namely, self-paced and online instructor-led.

Intellipaat’s self-paced Machine Learning program costs about US$160, where you will be able to learn from pre-recorded video lectures at your own pace. The online instructor-led one, on the other hand, costs about US$265. In this, you will attend live lectures from trainers, along with which you will receive access to the self-paced program as well. Hence, this program is extremely beneficial.

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.

We select instructors who are top SMEs in the industry with a minimum of 8 to12 years of experience in the field of Machine Learning. They are all extremely qualified trainers in the field of Machine Learning and Artificial Intelligence. They are selected after going through a rigorous process, where they are tested for their domain knowledge and training ability.

Intellipaat’s teaching assistant team consists of technical experts who help learners in various aspects, including doubt clearance, assignment sessions, and project evaluation.

As part of career services, we conduct mock interviews and help in developing your resume and a LinkedIn profile. Besides, we have a dedicated job portal where you can interact with recruiters and apply to job openings. Our job portal has more than 500 recruiters on-board, including some of the top companies such as Amazon, Flipkart, etc.

Yes, Intellipaat offers several group discounts for our classroom program, depending on the group size and type. To avail 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 accepts all major debit cards and credit cards. Besides, it offers all EMI options available for you to choose from as per your convenience. You can speak to our advisors to get more details about different EMI options as well.

If you end up missing a class, then you can contact our support team. Our team will further assist you in scheduling another class for the same topic so that you can catch up with the rest.

Also, all the sessions are recorded and shared with all participants in the LMS (Learning Management system). You can also refer to these recorded sessions for the missed class.

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.

You can speak to our advisors for detailed information about ML, and they will help you select the best-suited one for you based on your skill set and work experience.

After successfully completing the entire program and executing all the given industry-based exercises and projects, you will receive the ML certificate from Intellipaat.

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.

As a part of our online certification training, we provide practice tests and assignments that will help you prepare for real-life examinations and jobs. These tests also let you assess yourself on what you have learned and how much you have grasped.

At Intellipaat, you can enroll in either the instructor-led online training or self-paced training. Apart from this, Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience, and they have been actively working as consultants in the same domain, which has made them subject matter experts. Go through the sample videos to check the quality of our trainers.

Intellipaat is offering 24/7 query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail of email support for all your queries. If your query does not get resolved through email, we can also arrange one-on-one sessions with our support team. However, 1:1 session support is provided for a period of 6 months from the start date of your course.

Intellipaat is offering you the most updated, relevant, and high-value real-world projects as part of the training program. This way, you can implement the learning that you have acquired in real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.

You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.

Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this, we are exclusively tied-up with over 80 top MNCs from around the world. This way, you can be placed in outstanding organizations such as Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation as well.

You can definitely make the switch from self-paced training to online instructor-led training by simply paying the extra amount. You can join the very next batch, which will be duly notified to you.

Once you complete Intellipaat’s training program, working on real-world projects, quizzes, and assignments and scoring at least 60 percent marks in the qualifying exam, you will be awarded Intellipaat’s course completion certificate. This certificate is very well recognized in Intellipaat-affiliated organizations, including over 80 top MNCs from around the world and some of the Fortune 500companies.

Apparently, no. Our job assistance program is aimed at helping you land in 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 decision on hiring will always be based on your performance in the interview and the requirements of the recruiter.

View More
Select Currency