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
Imarticus Learning is an EdTech Partner of:
Learn More About The Program
Online Instructor-Led Training
Online Self Paced Videos
Cutting-edge, future-ready program designed and delivered in collaboration with IBM
Seven projects covering various machine learning algorithms using Python and IBM Watson
Access to IBM Cloud Platforms and Virtual Labs for 24/7 hands-on learning and practice
Extensive support via resume building, interview prep, mentorship and interview opportunities
CurriculumThe 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.
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
- Single and Multiline Comments
- 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
- Standard Lib and Pip
Exceptions and Files
- Exception Handling
- Raising Exceptions
- 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
- 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
- Univariates & Bivariates
- Introduction To Pandas
- IO Tools
- Basics Of Numpy
- Numpy Functions
- Pandas – Series and Dataframes
- Basics of Data Visualization
- Line Plots
- Bar Charts
- Pie Charts
- Scatter Plots
- Parallel Coordinates
- Implementing Simple & Multiple Linear Regression with Python
- Making Sense of Result Parameters
- Model Validation
- Handling Outliers, Categorical Variables, Auto-Correlation, Multi-Collinearity,
- Prediction and Confidence Intervals
- Use Cases
- 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
- Implementing Decision Trees using Python
- Information Gain
- Gini Index
- Standard Deviation Reduction
- Vizualizing & Prunning a Tree
- Implementing Random Forests using Python
- Random Forest Algorithm
- Important Hyper-Parameters of Random Forest for Tuning the Model
- Variable Importance
- Out of Bag Errors
- 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
- 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
- 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 Techniques
- Bootstrap Aggregation
- Random Forest
- 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 ** Project can
- be added here
- Applications on IBM Watson
- Administering Watson Applications
- 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.
- 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.
- Introducing Recurrent Neural Network
- Application Areas
- Case Study
- Knowledge Sharing, Q&A, and Guest Lectures
- Dedicated Industry Mentor
- 1:1 Mentorship Calls
- Career Guidance
- What Makes an Effective Resume
- Polishing your CV
- Action Verbs for CV
- Review and Critique
- 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.
- Linear Regression – Boston Dataset – Using Sklearn Linear Model & Gradient Descent Model
- Logistic Regression – Iris Dataset – Sklearn Logistic Model & Stochastic Average Gradient Descent
- Decision Tree & Random Forest – Bank Marketing Dataset – Decision Tree Classifier, Random Forest Classifier, Adaboost Classifier & Bagging Classifier
- KNN – Breast Cancer Dataset – KNN Classifier & How to Choose the K Value
- SVM – Default of Credit Card Clients Dataset – SVM Classifier using Different Kernels (Linear, Polynomial, Radial Basis Function)
- K-Means Clustering – Cars Dataset
- Neural Network – Predict Close Value of Stock – Dow Jones Industrial Average (DIJA) Dataset
Training MethodologyThe 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
Virtual Labs for 24/7 Access to Python for Hands-On Practice
Guest Lectures by Industry Leaders
In-Depth Projects for Each Tool/Technique
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 ProjectsThe 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.
CareersThe 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
- Consumer Behaviour Analysis
- Fraud Detection
- Market Projection / Sales Forecasting
- Internet/IT Security Monitoring
- Office Automation
Diverse Job Roles
Top Hiring Companies
High Paying Salaries
INR 9.93 Lakh
INR 10.43 Lakh
Big Data and Machine Learning:
Refining and polishing the candidate’s resume with insider tips to help them land their dream job
Preparing candidates to ace HR and Technical interview rounds with model interview questions and answers
Preparing candidates to face interview scenarios through 1:1 and panel mock interviews with industry veterans
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.
“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
“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
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.
AdmissionsThe 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.
Recent Post Graduates
Bachelors or Masters in Science, Math, Statistics or Computer Applications/IT
Experienced Professionals in Programming or IT
Looking to up-skill or change career paths
Individuals Looking for Global Certifications
To enhance their resumes & build a portfolio of demonstrable work
IBM as Education Technology PartnerThe 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.
Get access to IBM’s state-of-the-art content on their own delivery platform. Made and delivered by the experts
Aspirants are provided access to IBM Cloud Platforms featuring IBM Watson and other software for 24/7 practice
All candidates earn IBM Badges on completion of the Prodegree, with an option of additional IBM certification like CAD, WAD
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).
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.“
CertificationOn 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.
What is the format of the program?
- 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?
What tools will be taught in the program?
What topics will be covered?
- Data Science
- Machine Learning
- Deep Learning
- Apache Spark
What study material will be provided to us for the program?
- 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?
- 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?
What is the Placement Assistance feature?
- 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?
- Classroom Training: ₹ 1,00,000/-
- Online Instructor-Led Training: ₹ 80,000/-
Which cities do you offer the Machine Learning classroom training course in?