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MACHINE LEARNING & DEEP LEARNING PRODEGREE

In Collaboration with IBM, a Global Leader in Technology-Driven Solutions

145+ Hour Program, Covering Machine Learning, Deep Learning, Python and IBM Watson

Seven Industry Projects and One Capstone Project for Hands-On Learning

Free Access to IBM’s Cloud Platforms featuring Cognitive Classes and IBM Watson

Delivered in Classroom or Online Instructor-Led Format

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Imarticus Learning is an EdTech Partner of:

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Classroom Training
र 1,00,000/-

Online Instructor-Led Training
र 80,000/-

Online Self Paced Videos

Machine Learning Certification Course

The Machine Learning & Deep Learning Prodegree, in association with IBM as the EdTech Partner, is a first-of-its-kind 145+ hour certification course providing in-depth exposure to Data Science, Big Data, Machine and Deep Learning. The rigorous industry-aligned curriculum offers comprehensive understanding of Python and Data Science for careers in Machine Learning and Big Data. The program also features seven industry projects, numerous case studies and periodic interaction with industry leaders in the Machine Learning ecosystem.

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Industry-Endorsed

Cutting-edge, future-ready program designed and delivered in collaboration with IBM

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Project Based

Seven projects covering various machine learning algorithms using Python and IBM Watson

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Virtual Labs

Access to IBM Cloud Platforms and Virtual Labs for 24/7 hands-on learning and practice

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Placement Assistance

Extensive support via resume building, interview prep, mentorship and interview opportunities

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Industry-Endorsed

Cutting-edge, future-ready program designed and flexible delivery in collaboration with IBM

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Project Based

Seven projects covering Python, Hadoop and IBM Watson in healthcare, BFSI, Social Media and many more!

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Access to IBM Platforms

Access to IBM Cloud Platforms featuring IBM Watson and other software for 24/7 practice

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Flexible Delivery

by Imarticus in two delivery modes: Classroom or Online Instructor led training

Curriculum

The Prodegree features a cutting-edge curriculum designed in consultation with IBM that aligns to globally-recognized standards, global trends and best practices.  The curriculum places special emphasis on building programming skills through hands-on practice, with a 1:4 ratio of theoretical sessions and programming practice.

Horizontal, Flat

26%
Introduction to Machine Learning
20%
Data Analysis with Python
14%
Deep Learning Application
11%
Statistics
9%
Job Readiness
8%
Basic of Python
6%
Introduction to ML
6%
IBM Watson

ML Spectrum & Journey

  • Intro To Modeling Lifecycle
  • Intro To Supervised Learning
  • Descriptive Statistics
  • Intro To Unsupervised Learning

Big Data and Hadoop

  • Big Data and its Sources
  • Popular Tools Used for Big Data
  • RDBMS vs Hadoop
  • Hadoop Architecture and Ecosystem
  • HDFS Design and Architecture Overview
  • When to Use & Not Use Hadoop?

Introduction to Python

  • Spyder IDE
  • Jupyter Notebook
  • Floats and Strings
  • Simple Input & Output
  • Variables
  • Single and Multiline Comments

Control Structures

  • Booleans and Comparisons
  • IF and ELSE statements
  • Operator Precedence/li>
  • Lists – Operations and Functions

Functions and Modules

  • Function Arguments
  • Comments and Doc Strings
  • Functions as Objects
  • Modules
  • Standard Lib and Pip

Exceptions and Files

  • Exception Handling
  • Raising Exceptions
  • Assertions
  • Working with Files

Basic Probability and Terms

  • Events and their Probabilities
  • Rules of Probability
  • Conditional Probability and Independence
  • Permutations and Combinations
  • Bayer’s Theorem
  • Descriptive Statistics
  • Compound Probability
  • Conditional Probability

Probability Distributions

  • Types of Distributions
  • Functions of Random Variables
  • Probability Distribution Graphs
  • Confidence Intervals

Data Transformations and Quality Analysis

  • Merge, Rollup, Transpose and Append
  • Missing Analysis and Treatment
  • Outlier Analysis and Treatment

Exploratory Data Analysis

  • Summarizing and Visualizing the Important Characteristics of Data
  • Hypothesis Testing
  • Visualizations
  • Univariates & Bivariates
  • Crosstabs
  • Correlation

Pandas

  • Introduction To Pandas
  • IO Tools
  • Basics Of Numpy
  • Numpy Functions
  • Pandas – Series and Dataframes

Data Visualization

  • Basics of Data Visualization
  • Line Plots
  • Bar Charts
  • Pie Charts
  • Histograms
  • Scatter Plots
  • Parallel Coordinates

Linear Regression

  • Implementing Simple & Multiple Linear Regression with Python
  • Making Sense of Result Parameters
  • Model Validation
  • Handling Outliers, Categorical Variables, Auto-Correlation, Multi-Collinearity,
  • Heteroskedasticity
  • Prediction and Confidence Intervals
  • Use Cases

Logistic Regression

  • Implementing Logistic Regression with Python
  • Wald Test, Likelihood Ratio Test Statistic, Chi-Square Test
  • Goodness of Fit Measures
  • Model Validation: Cross Validation, Roc Curve, Confusion Matrix
  • Use Cases

Decision Trees

  • Implementing Decision Trees using Python
  • Homogeneity
  • Entropy
  • Information Gain
  • Gini Index
  • Standard Deviation Reduction
  • Vizualizing & Prunning a Tree

Random Forests

  • Implementing Random Forests using Python
  • Random Forest Algorithm
  • Important Hyper-Parameters of Random Forest for Tuning the Model
  • Variable Importance
  • Out of Bag Errors

Time Series

  • Handling Time Series Data
  • Holt-Winters Model
  • ARIMA Model
  • ACF/PACF Functions

Hands-on Project Work

  • Project #1: Real Estate Price Prediction using Linear Regression
  • Project #2: Bankruptcy Prediction using Logistic Regression
  • Project #3: Identifying Good and Bad Customers for Granting Credit Using Decision Trees
  • Project #4: Forecasting and Predicting the Sales of Furniture of the Superstore

Introduction to Machine Learning

  • Machine Learning Modelling Flow
  • How to Treat Data in ML
  • Parametric & Non-Parametric ML Algorithm
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting
  • Bootstrap Sampling
  • Bagging Aggregation
  • Boosting

SciKit Learn

  • Introduction to SciKit Learn
  • Load Data into SciKit Learn
  • Run ML Algos for Both Unsupervised and Supervised Data
  • Supervised Methods: Classification & Regression
  • Unsupervised Methods: Clustering, Gaussian Mixture Models
  • Decide What‘s the Best Model for Every Scenario

Optimisation Techniques

  • Constant Learning Rate Procedures
  • Adaptive Learning Procedures
  • Batch Gradient Descent
  • Mini-Batch Gradient Descent
  • Stochastic Gradient Descent
  • Nesterov Accelerated Gradient
  • Root Mean Squared Propagation
  • Adaptive Moment Estimation Procedure

ML Algorithms – Supervised Learning

  • Linear Regression with Stochastic Gradient Descent
  • Logistic Regression with Stochastic Gradient Descent
  • K-Nearest Neighbour
  • Eager Methods Vs. Lazy Methods
  • Nearest Neighbor Classification
  • Building Kd-Trees
  • Support Vector Machine
  • Perceptron Algorithm

ML Algorithms – Unsupervised Learning

  • What is Clustering?
  • K-Means Algorithm
  • Types of Clustering
  • Evaluating K-Means Clusters

Ensemble Algorithms

  • Ensemble Techniques
  • Bootstrap Aggregation
  • Random Forest
  • Boosting

Neural Networks

  • Understanding Neural Networks
  • The Biological Inspiration
  • Perceptron Learning & Binary Classification
  • Backpropagation Learning
  • Object Recognition

IBM Watson Developer

  • Fundamentals of IBM Watson
  • Advantages of IBM Watson
  • Use Cases of Cognitive Services
  • Applications on IBM Watson
  • Administering Watson Applications

KERAS

  • Keras for Classification and Regression in Typical Data Science Problems
  • Setting up Keras
  • Different Layers in Keras
  • Creating a Neural Network
  • Training Models and Monitoring
  • Artificial Neural Networks

ANN on KERAS

  • Case Study – Credit Default Using ANN on Keras
  • Description – This Research is Aimed at the Case of Customers’ Default Payments in Taiwan. From the Perspective of Risk Management, the Result of Predictive Accuracy of the Estimated Probability of Default will be More Valuable than the Binary Result of
  • Classification – Credible or Non-Credible Clients.

Tensorflow

  • Introducing Tensorflow
  • Neural Networks using Tensorflow
  • Debugging and Monitoring
  • Convolutional Neural Networks
  • Unsupervised Learning

CNN on Tensorflow

  • Case Study – Digit Recognition using Tensorflow
  • Description – The MNIST Database (Modified National Institute of Standards and Technology Database) is a Large Database of Handwritten Digits that is Commonly used for Training Various Image Processing Systems. We are using One Such MNIST Dataset to Illustrate the Convolutional Neural Network (CNN) using Tensorflow in Python.

RNN

  • Introducing Recurrent Neural Network
  • Application Areas
  • Case Study

Mentorship

  • Knowledge Sharing, Q&A, and Guest Lectures
  • Dedicated Industry Mentor
  • 1:1 Mentorship Calls
  • Career Guidance

Resume Building

  • What Makes an Effective Resume
  • Polishing your CV
  • Action Verbs for CV
  • Review and Critique

Mock Interviews

  • Interview Preparation
  • Mock Interviews with Industry Experts on Domain

Capstone Project Presentation

  • Students Present their Capstone Project to a Panel of Industry Experts, Who Will Provide Constructive Feedback and Critique

Build valuable hands-on development experience which can be showcased to future recruiters.

  1. Linear Regression – Boston Dataset – Using Sklearn Linear Model & Gradient Descent Model
  2. Logistic Regression – Iris Dataset – Sklearn Logistic Model & Stochastic Average Gradient Descent
  3. Decision Tree & Random Forest – Bank Marketing Dataset – Decision Tree Classifier, Random Forest Classifier, Adaboost Classifier & Bagging Classifier
  4. KNN – Breast Cancer Dataset – KNN Classifier & How to Choose the K Value
  5. SVM – Default of Credit Card Clients Dataset – SVM Classifier using Different Kernels (Linear, Polynomial, Radial Basis Function)
  6. K-Means Clustering – Cars Dataset
  7. Neural Network – Predict Close Value of Stock – Dow Jones Industrial Average (DIJA) Dataset

Training Methodology

The Prodegree is delivered using an experiential learning methodology that blends theoretical concepts with hands-on practical learning to ensure a holistic understanding of the subject.

Self-Paced Videos to Understand Key Concepts

Conceptual

Conceptual

Virtual Labs for 24/7 Access to Python for Hands-On Practice

Guest Lectures by Industry Leaders

In-Depth Projects for Each Tool/Technique

Application

Application

HANDS-ON LEARNING ON CLOUD-BASED VIRTUAL LABS
Tools Covered: Python, IBM Watson

Virtual Labs and Coding Platform

  • Learn on a state-of-the-art virtual lab, with 24/7 access to all required software and datasets pre-installed.
  • Agnostic of machine configuration, with no installation and compatibility issues; learn anytime, anywhere!

Hands-on Projects

The Prodegree features seven hands-on projects on various domains of Machine Learning and Deep Learning to master the technology behind Netflix, Google Search and other new-age solutions. Project reviews by our experienced faculty and training assistants provide deep analysis of a student’s code and project, along with constructive criticism for further improvement.

Project #1: Real Estate Price Prediction using Linear Regression
  • Predict the price of new real estate properties basis historical data
Project 2: Bankruptcy Prediction using Logistic Regression
  • Use financial ratios to predict if a company is going to be bankrupt
Project #3: Identifying Good and Bad Customers for Granting Credit using Decision Trees
  • Use decision trees to analyze characteristics and attributes of lenders into good or bad credit risk
Project #4: Forecasting the Sale of Furniture of a Superstore
  • Using daily sales data of various products at a store, use time series to predict future sales
Project #5: Credit Default using ANN on Keras
  • Calculate the estimated probability of default to manage the risk of a Taiwanese bank
Project #6: Digit Recognition using CNN on TensorFlow
  • Build a model using Convolutional Neural Network to recognize handwritten digits
Project #7: IBM Watson
  • Automate searching your network’s hyperparameter space to ensure the best model performance

Predicting Purchase Behaviour on E-Commerce Dataset

Goal: Use the data of GroceryKart customer orders over time to predict which previously purchased products will be in a user’s next order.
Using multiple data sets, students are to use ML algorithms to determine:

  • When do customers order the most?
  • What are the top 5 products that are reordered?
  • What is the reorder ratio for each department?
  • Build a model to predict which previously purchased products will be in a user’s next order.

Careers

The Imarticus Careers Assistance Services (CAS) team provides a rigorous industry mentorship process that is customized to your needs. We prepare you to be job-ready with interview preparation, resume building workshops and 1-1 mock interviews with industry experts.

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Tons of companies are going all out to hire competent engineers, as ML is gradually becoming the brain behind business intelligence. Through it, businesses are able to master consumers’ preferences thereby increasing profits.

Top Uses of Machine Learning

  • Research
  • Consumer Behaviour Analysis
  • Fraud Detection
  • Market Projection / Sales Forecasting
  • Internet/IT Security Monitoring
  • Office Automation

Diverse Job Roles

Machine Learning Engineer Data Scientist Artificial Intelligence Engineer Data Analyst Machine Learning Architect Big Data Engineer

Top Hiring Companies

Facebook, LinkedIn, Google, Netflix, Amazon, Deloitte, FlipKart, Visa, Mu Sigma, Latent View, Fractal Analytics, Walmart Labs, BookMyShow

High Paying Salaries

Big Data:
INR 9.93 Lakh
Machine Learning:
INR 10.43 Lakh
Big Data and Machine Learning:
13.94 Lakh

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RESUME REVIEW

Refining and polishing the candidate’s resume with insider tips to help them land their dream job

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INTERVIEW PREP

Preparing candidates to ace HR and Technical interview rounds with model interview questions and answers

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MOCK INTERVIEWS

Preparing candidates to face interview scenarios through 1:1 and panel mock interviews with industry veterans

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ACCESS TO OUR PLACEMENT PORTAL

Access to all available leads and references from open and private networks on our placement portal

The Imarticus Careers Assistance Services (CAS) team provides a rigorous career and industry mentorship process that is customized to your needs.

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“I consider myself fortunate enough to be a part of this reputed institute. I was enrolled at Imarticus learning for the Analytics Prodegree. Faculty are very experienced and very helpful – they will guide you on everything from domain knowledge to personality development.”

-Mr. Bhumsen Singh

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“I was enrolled at Imarticus learning for Data Science Prodegree. The quality of teaching by all faculty was really good. The topic was covered in detail and concepts were cleared right there. Staff/admin team are always there to help you with all your queries. Highly recommend to all who want to do this course. I am glad that I made my decision to choose Imarticus. Cheers guys, good platform to start your career.”

-Mr. Mahesh Salvi

Industry Advisors


The program is developed in consultation with senior industry experts to ensure a high degree of relevance in accordance to the needs and demands of the industry.

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IQBAL KAUR

Ex- Target and Genpact

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AMIT KHANNA

Managing Partner – YDatalytics (Antuit & Y Group)

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AKASH BHATIA

Founder And CEO, Infinite Analytics, Kyazoonga

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DIPAYAN SARKAR

Consultant & Sr. Data Scientist – Apple, Maersk

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Admissions

The Prodegree is ideal for aspirants and professionals who are interested in working in the analytics industry and are keen on enhancing their technical skills with exposure to cutting-edge practices.

Admission-Entrepreneurs

Recent Post Graduates

Bachelors or Masters in Science, Math, Statistics or Computer Applications/IT

Admission-Entrepreneurs

Experienced Professionals in Programming or IT

Looking to up-skill or change career paths

Admission-Entrepreneurs

Individuals Looking for Global Certifications

To enhance their resumes & build a portfolio of demonstrable work

Admission-Entrepreneurs

Recent Post Graduates

Bachelors or Masters in Science, Math, Statistics or Computer Applications/IT

Admission-Entrepreneurs

Experienced Professionals in Programming or IT

Looking to up-skill or change career paths

Admission-Entrepreneurs

Individuals Looking for Global Certifications

Those looking to enhance their resumes & build a portfolio of demonstrable work

To enroll for the Machine Learning & Deep Learning Prodegree, please click below:

IBM as Education Technology Partner

The program is developed in consultation with senior industry experts to ensure a high degree of relevance in accordance to the needs and demands of the industry.

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Cognitive Class

Get access to IBM’s state-of-the-art content on their own delivery platform. Made and delivered by the experts

IBM Platforms

Aspirants are provided access to IBM Cloud Platforms featuring IBM Watson and other software for 24/7 practice

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IBM Certification

All candidates earn IBM Badges on completion of the Prodegree, with an option of additional IBM certification like CAD, WAD

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Cognitive Class

Get access to IBM’s state-of-the-art content on their own delivery platform. Made and delivered by the experts

IBM Platforms

Aspirants are provided access to IBM Cloud Platforms featuring IBM Watson and other software for 24/7 practice

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IBM Certification

All candidates earn IBM Badges on completion of the Prodegree, with an option of additional IBM certification like CAD, WAD

About IBM

IBM is the industry leader in cloud and cognitive computing with operations in 170 countries and over 380,000 employees worldwide with revenues of $81.8 billion globally (2015).

Industry Speak

Seema Kumar Industry Speak

Seema Kumar
Country leader (developer ecosystem and startups) at IBM India and South Asia

“IBM is proud to be associated with Imarticus as the Delivery Partner for this Prodegree. This partnership is reflective of India’s importance in the tech ecosystem, but also the growing need for trained machine learning engineers in the country. We have meticulously designed the course curriculum, keeping in mind the needs of the industry as well as embedded globally-aligned case studies and use cases throughout the program. Lastly, participants will also have access to our cloud-based Watson and Data Science platforms for the duration of the Prodegree.“

Certification

On completion of the Machine Learning & Deep Learning Prodegree, aspirants will receive an industry endorsed Certificate of Achievement, which is co-branded by IBM and Imarticus Learning.

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Faculty

Y Laxmi Prasad

Python, ML, Deep Learning and R Programming

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Vinay Borhade

Python, ML, Deep Learning and R Programming

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Tharangini Vijay Kumar

Basic and Advanced Statistical Techniques

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FAQs

What is the format of the program?
The Machine Learning & Deep Learning Prodegree is delivered in two modes: Classroom and Online learning modes to cater to your learning preferences while ensuring maximum learning efficacy.

  • Classroom batches: Classroom training by expert faculty at our Imarticus centers across India.
  • Online batches: Live Instructor-led Virtual Classes (Webinars) with expert faculty for real-time learning and interaction with batch mates

Class times for both formats are fixed and you are required to be available for your classes at a predefined time each week. Both formats come with approx. 5 hours of engaging Instructor videos that you can watch as per your convenience before attending your lecture (be it in class or virtually).

What is Machine Learning?
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. You can learn machine learning using various analytical tools such as Python, R and SAS.

What tools will be taught in the program?
The Prodegree will enabling learning on basic and advanced concepts in Machine and Deep Learning using Python. You do not need to have mastery over these tools as the course will walk you through the basics before advancing to more complex subjects.

What topics will be covered?
The Machine Learning & Deep Learning Prodegree offers in-depth and hands-on learning of the following topics and tools:

  • Data Science
  • Machine Learning
  • Deep Learning
  • Apache Spark
  • Python

What study material will be provided to us for the program?
The Machine Learning & Deep Learning course is taught through 140 hours of Instructor-led, live training sessions at designated times on weekends over the span of 4 months. You will receive additional study material including:

  • Pre-selected cognitive classes from IBM
  • Powerpoint presentations
  • Case studies and use cases
  • Seven industry projects and data sets
  • Recordings of previous virtual classes (if you enroll for online delivery format)

Your study material will be available to you on Imarticus’s Learning Management System, which is a fully integrated state-of-the-art learning management system for an extended duration of 7 months. You will need to log in to the learning portal using the credentials provided and navigate through the portal as required.

What is IBM’s involvement in the Prodegree?
Imarticus Learning is an EdTech Partner of IBM. IBM’s involvement includes:

  • Curriculum Design: The curriculum has been designed in consultation with IBM leadership to ensure you are learning only the very latest and most relevant subjects for careers in the booming ML space.
  • Sharing of Case Studies: IBM leadership has shared real-world caselets and scenarios that you will work on during your program.
  • Free Access to IBM Platforms: IBM has provided free access to IBM Cloud Platform for 24/7 cloud-based access to all tools and techniques covered in the Prodegree. Aspirants also receive exclusive Cognitive Classes on Machine Learning, Deep Learning and Python, developed by IBM experts for self study.

What certification will I receive on completion?
You will receive the industry-endorsed Machine Learning & Deep Learning Prodegree certification, which will be co-branded with IBM. This is subject to at least 60% attendance throughout the program and completion of all mandated projects or assignments.
What is the Placement Assistance feature?
The Career Assistance team at Imarticus provides 100% placement assistance throughout the program to guide and help navigate ample career options. This includes:

  • Refining and polishing the candidate’s resume with insider tips to help land their dream job
  • Preparing candidates to ace HR and Technical interview rounds with model interview Q&A
  • Conducting rigorous 1:1 mock interviews with industry veterans
  • Providing access to leads and references from open and private networks on our placement portal for 3 months

Please note as per policy, Imarticus Learning does not guarantee placements but acts as an enabler.

What are the Machine Learning course fees?
The fees for the Machine Learning Course vary based on the mode of delivery.

  • Classroom Training: ₹ 1,00,000/-
  • Online Instructor-Led Training: ₹ 80,000/-

Which cities do you offer the Machine Learning classroom training course in?
Imarticus’ Machine Learning Prodegree program is conducted at our training institutes across eight Indian cities: Mumbai, Thane, Pune, Delhi, Gurgaon, Hyderabad, Bangalore, and Chennai.

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