Machine Learning

BIG DATA AND MACHINE LEARNING PRODEGREE

In collaboration with IBM, a global leader in technology-driven solutions

160 hour course, covering Deep and Machine Learning, Hadoop, Spark and Python

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

Delivered in Online Formats

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Imarticus Learning is an EdTech Partner of:

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Classroom Training

Online delivery
$ 1190

Online Self Paced Videos

Big Data and Machine Learning Course

The Big Data and Machine Learning Prodegree, in association with IBM as the EdTech Partner, is a first-of-its-kind 160 hour certification course providing in-depth exposure to Data Science, Big Data, Machine Learning and Deep Learning. The rigorous industry-aligned curriculum offers comprehensive understanding of Python, Spark and Hadoop for careers in Machine Learning and Big Data. The program also features seven industry projects 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 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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Program Delivery

Flexible training delivered via Instructor-led Online Mode

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Industry-Endorsed

Industry-Endorsed Cutting-edge, future-ready program designed and delivered 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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Program Delivery

Program delivered by Imarticus in two delivery modes: Classroom or online training

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Access to IBM Platforms

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

Big Data and Machine Learning Course Curriculum

The Prodegree features a cutting-edge curriculum designed in consultation with IBM that aligns to globally-recognized standards, global trends and best practices.

PGIBM explains the curriculum and program coverage

Horizontal, Flat

12.5%
Data science Basics
37.5%
Python
12.5%
Machine Learning
12.5%
Deep Learning
12.5%
Hadoop and Spark
12.5%
IBM Watson

Basics of Statistics

  • Fundamentals
  • AND / OR
  • Descriptive Statistics
  • Compound Probability
  • Conditional Probability

Bayer’s Rules & Random Variables

  • Permutations and Combinations
  • Bayer’s Theorem
  • Random Variables
  • Expected Value

Probability Distributions

  • Binomial Distributions
  • Functions of Random Variables
  • PD Graphs
  • Confidence Intervals
  • Covariance and Correlation

Sampling

  • Sampling Distributions
  • T-distributions
  • Hypothesis Testing
  • Exploratory Data Analysis

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
  • 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

Linear Regression

  • Simple Linear Regression
  • Analysing Output
  • Additional Regression Concepts
  • Prediction and Confidence Intervals

Multiple Regression

  • Multiple Regression
  • Dummy and Time-lagged Intervals
  • Transformations
  • ANOVA

Decision Analysis

  • Decision Trees
  • Random Forests

Pandas

  • Introduction to Pandas
  • IO Tools
  • Basics of NumPy
  • Series and Data Frames

SciKit Learn

  • Introduction to SciKit Learn
  • Classification, Regression and Clustering algorithms
  • Supervised Methods
  • Unsupervised Methods

Hands-on Project Work

  • Project 1 – Real Estate Price Prediction using Linear Regression
  • Project 2 – Bankruptcy Prediction using Logistic Regression
  • Project 3 – Facebook Post Count using Decision Tree

Machine Learning

  • Machine Learning 101
  • Evolution and Trends
  • Application of Machine Learning
  • Best Practices

Machine Learning Algorithms

  • Classification
  • Regression
  • Collaborative Filtering
  • Clustering
  • Principal Component Analysis

Neural Networks

  • Perceptron Learning
  • Backpropagation Learning
  • Learning Feature Vectors for Words
  • Object Recognition

Hands-on Project Work

  • Project 4 – Credit Default using ANN on Kera
  • Project 5 – Handwriting/Facial recognition  using CNN on TensorFlow

KERAS

  • Setting up KERAS
  • Creating a Neural Network
  • Training Models and Monitoring
  • Artificial Neural Networks

Tensorflow

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

Big Data/Hadoop

  • Big Data and its Sources
  • RDBMS vs Hadoop
  • Hadoop Architecture and Ecosystem
  • HDFS Design and Architecture Overview

MapReduce Framework

  • Design and Execution
  • High level MapReduce Pipeline
  • Strategies for Debugging
  • Architecture
  • MapReduce I/O Formats

Hive and Spark Ecosystem

  • Hive and Spark 101
  • Data Sharing in MapReduce
  • Data Types and Validation in Hive
  • Spark Configuration
  • Cluster Modes

Spark Machine Learning Lifecycle

  • Spark Transformers
  • Spark ML Lib
  • Spark ML Pipeline
  • Model Accuracy in Spark

Machine Learning in 2020

  • Virtual Agents
  • Deep Learning Platforms
  • Biometrics
  • Robotics Process Automation

IBM Watson Developer

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

Hands-on Project Work

  • Project 6 – Machine Learning using Spark
  • Project 7 – Build a chatbot using IBM Watson

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

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 hands-on practice of tools and techniques

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, Apache Spark, Hadoop, IBM Watson

IBM Cognitive Class

  • Learn on a state-of-the-art IBM Cloud Platform, with 24/7 access to IBM’s Congnitive classess, IBM Waston with Program related software and pre-loaded datasets.
  • 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 Big Data and Machine Learning to master the technology behind Netflix, Google Search among 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: Facebook Post Count using Decision Tree
  • Based on Facebook posts, predict how many comments the post will receive
Project 4: Credit Default using ANN on Keras
  • Calculate the estimated probability of default to manage the risk of a Taiwanese bank
Project 5: Handwriting/Facial Recognition using CNN on TensorFlow
  • Build a model using Convolutional Neural Network to recognize handwriting/faces
Project 6: Machine Learning using Spark
  • Use Spark ML Lib to recognize business trends and solve business problems
Project 7: IBM Watson Developer
  • Build a Chatbot for tracking the status of an online order using IBM Watson

Career

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

$127,500+

Average salary of Machine Learning Engineer – Kaggle 2017

Top Hiring Companies

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

21,200+

Job openings for Machine Learning – Indeed July 2018

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RESUME BUILDING

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 Analytics Prodegree. Faculty are very experienced and very helpful they will guide you on everything from domain knowledge to personality development.”

-Amber H

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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.”

-Robert J

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

Graduates or Masters in Various Disciplines

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

With less than 4 years experience, 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

To enroll for Big data the Machine 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

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 Big Data and Machine 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

DIPAYAN

Dipayan Sarkar

Dipayan has 16 years of Machine Learning experience managing enterprise-wide Analytics solutions for Fortune 10 clients such as Apple and Maersk. At Maersk Group, Dipayan worked as a Senior Data Scientist responsible for CRM Analytics and Machine Learning. He has also previously worked at Apple as a Business Analytics and Intelligence Consultant delivering solutions for Data Warehousing and Business Intelligence.

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He has extensive expertise in Predictive Modeling & Data Mining in the Retail & Container Shipping domain, and hands-on expertise with statistical tools like R, Python and SPSS with applications in Machine Learning and Deep Learning. Dipayan has a Post Graduate Diploma in Business Analytics from IIT Stuart School of Business and Diploma in Business Finance from ICFAI University.
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Mani

Mani Kanteswara Rao Garlapati

Mani has over 10 years of rich corporate experience at Tier 1 firms such as Tata Consultancy Services, Mu Sigma, WalmartLabs India and JP Morgan Chase. With cross-functional domain experience in E-commerce, Tele-communications and Insurance, Mani has in-depth, hands-on exposure to Machine and Deep Learning, Text Mining, NLP & Social Media Analytics, Statistical Modelling,

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Classification Systems, Forecasting and Latent Aspect Rating Analysis. He has worked on high-profile projects such as Sales Driver, Segmentation and Profiling and is also an expert in Python, R, SAS, SPSS, Hive, Spark, Shell Scripting, VBA and SQL. Mani holds a MSC. in Tech Finance from Birla Institute of Technology and Science.
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Satya

Satya Srinivas

Satya has 25 years of experience aligning multi-million dollar Information Technology deployments with business strategy and operational processes for Fortune 1000 companies. In the past he has been a management consultant and a negotiator and has consulted in the areas of performance management in enterprise architecture,

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data mining & analytics, machine learning, pattern recognition, social media analytics and Big Data management & analytics for several start-ups as well as major corporate houses like Infosys and IBM. Satya is a BE – Electronics and Communication from University of Mysore and a MS – Computer Engineering from Florida Atlantic University.
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Dr. Yogesh Parte

Yogesh is a research engineer with over 14 years of experience in  machine learning, artificial intelligence, IoT, “digital twins”, approximations in high dimensions, multidisciplinary design optimisation, exploratory data analysis, Nutri-analytics using Python, R, MATLAB, C/C++. He is the Founder of Y P Consulting Services,

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which provides specialized services and software solutions in the field of innovation engineering and technology applications. Previously, he has worked as post-doctoral researcher at University of Paul Sabatier and as a research & development engineer at Modartt S. A. in Toulouse, France. Yogesh holds a PhD. in Applied Mathematics from University of Paul Sabatier, France and has won over 30 awards for academic excellence.
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Big Data and Machine Learning Training Videos

Industry Connect – Big Data & Machine Learning Prodegree by Rav Ahuja

Industry Connect – IBM on Collaboration with Imarticus by Seema Kumar.

Big Data and Machine Learning Prodegree in Collaboration with IBM.

Big Data and Machine Learning Prodegree in Collaboration with IBM.

FAQs

What is the format of the Big Data & Machine Learning course?
The Prodegree is delivered in the Online learning modes to cater to your learning preferences which includes 120 hours of live Instructor-led Virtual Classes (Webinars) with expert faculty for real-time learning and interaction with batch mates. Class times are fixed and you are required to be available for your classes at a predefined time each week. The program also comes with 40 hours of engaging Instructor videos that you can watch as per your convenience before attending your lecture.

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 tools like Apache Spark, Hadoop, Python, and IBM’s own Artificial Intelligence platform, Watson. 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 Big Data and Machine Learning Prodegree offers in-depth and hands-on learning of the following topics and tools:

  • Data Science
  • Machine Learning
  • Deep Learning
  • Apache Spark
  • Hadoop
  • Python
  • IBM Watson

What study material will be provided to us for the Big Data & Machine Learning Course?
The Big Data and Machine Learning course is taught through 136 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

Your study material will be available to you on IBM’s Cloud Platform 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.

Will I have to install any software at home?
We realize that not everyone has the infrastructure to install Hadoop and other tools at home, so fret not. We offer you 24/7 access to IBM’s own virtual lab, the IBM Cloud Platform, which will have Hadoop, Spark, Python and IBM Watson pre-installed for you. You will have access to this virtual lab for a period of 7 months on the IBM Cloud Platform.

What certification will I receive on completion?
You will receive the industry-endorsed Big Data and Machine 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

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

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