Advanced Certification in Machine Learning and Cloud
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INTRO VIDEO
In association with
Industry Experts from
Online
Format9th August, 2020
All India Aptitude Test-- Months
Duration Recommended 12-15 hrs/week12,600+
Students EmpoweredRank 1
NIRF Ranking300+
Hiring Partners100 Learners*
Exclusive BatchOption of a M.Sc.
Globally Accepted30th Sep, 2020
Batch Start Date16th August, 2020
Application DeadlineProgram Overview
Key Highlights
“We are excited to partner with upGrad and launch this course on Machine Learning and Cloud. We are looking forward to teach and train the young, talented minds enrolling into the program," - Prof. Janakiram D.
Career Impact
Meet The Batch
Programming Languages and Tools
- IIT Madras, India's no. 1 Ranked Engineering Institute by NIRF Ranking 5th consecutive year
- Widely recognised Advanced Certification in Machine Learning and Cloud
- Network with Machine Learning and Cloud professionals across all industries
Industry Projects
IMDb Movie Analysis
Analyze movie data from the past hundred years and find out various insights to determine what makes a movie do well. Use data manipulation, data slicing, and various other data-frame operations to successfully find usable insights from the movies.
Uber
Analysis of the Uber dataset to understand the demand and supply of cab services using data visualization
Celebrity Recognition
As part of this case study, students must use the Boto3 SDK and AWS services to build an application that scans an image stored in S3 for celebrities.
Stack Overflow
Using EDA, help Stack Overflow implement the features in its web application such as sending notifications to relevant users if a question is raised and calculate the approximate time within which the user will receive an answer. Performing a time series to analyse the trend of data science and non data science related tags.
Data Warehousing
Similar to the BFSI or the healthcare industry, the telecommunications industry deals with big data situations. Now, these situations involve collecting, storing and analyzing data to generate insights. This case study involves usage of tools such as sqoop, hive and oozie for various jobs such as ingestion analysis and scheduling.
E-commerce Sales EDA Case Study
Analyze a public click stream dataset of a cosmetics store to extract valuable insights which generally data engineers come up with in an e-retail company Use effective querying, query optimization, file partitioning techniques for enhancing the performance of your queries.
Twitter HashTag Analysis
Perform the real time hashtags analysis of tweets using the Twitter APIs Get the real time tweets from API and then filter out the hashtags using Spark Streaming.
Recommendation System using ALS
Using the ALS algorithm, predicted the top 20 recommendation to each user who have rated certain movies based on his/her liking or disliking. The recommendation system is built on MovieLens dataset using a collaborative filtering technique i.e. ALS algorithm.
Market Basket Analysis
Implementation of market basket analysis using Spark with the help of Apriori algorithm. The assignment tests the business acumen by implementing multiple strategies over the results of the Apriori algorithm.
A/B Testing
An e-commerce company is evaluating its customer experience. They run a survey among the customers to understand their feedback. The feedback shows that late delivery is one of the most frequent problems faced by the customer. However, the consumer experience manager says that the late delivery percentage is similar to the industry average. Hypothesis Tesing is used to establish that the organization has a poor record in delivery. In order to solve this issue, the company has come up with a solution. They decide to build an intelligent system that predicts the delivery date by taking a lot of factors into account. Once the algorithm is built, it is rolled out for 10% of the customers to check whether it is successful. Use A/B Testing to compare the late delivery complaint percentage before and after the algorithm was rolled out.
House Price Prediction
A common challenge in the real estate industry includes predicting the price of a flat given various factors such as area, bedrooms, parking, etc. Machine learning enables you to answer this question by making use of the relationship between these variables.
NYC Taxi Fare Prediction
Build a pricing model to predict the fare of a taxi ride given set of attributes such as date-time, coordinates for pickup and drop off. Data understanding, outlier analysis, and effective feature engineering are essential in any modelling exercises.
Telecom Churn Prediction
The objective of the case study is to predict whether a customer leaves the network or not. A logistic regression model using the scikit learn library is built to predict the churn rates of individual customers. The data is divided in three different tables which need to be combined. Lot of importance is give to feature section, and the metrics used to evaluate the model.
Click-Through Rate Prediction
The dataset contains information about advertisements shown to customers. The complete dataset is anonymised for privacy reasons. The objective of the case study is to build a logistic regression model which can predict whether an advertisement will be click or not. The case study includes model building a logistic model in pyspark. To do this EDA, and all the pre-processing is done on pyspark itself. Insights about handling anonymised data are provided. Business implication for section of hyperparameter (threshold) is also done. Performance optimization for spark is also discussed.
Credit Card Application Approval Assignment
Learn how predictive analytics can be used to decide the creditworthiness of customers and whether they can be issued a credit card or not. Incorporating business relevant techniques and taking business driven decisions are crucial while building models especially in Finance Industry
Income Prediction
You will be required to build a tree to predict the income of a given population, which is labelled as <= 50K and >50K. The attributes (predictors) are age, working-class type, marital status, gender, race, etc. Build a decision tree in python. Visualization and hyperparameter tuning included.
Libsvm
LibSVM dataset is used to represent data that contains numerous missing values in a compact form. Build a regression decision tree in spark using spark libraries.
Heart Disease Prediction
To correctly predict whether the person has heart disease or not based on: id, age, gender, height, weight, ap_hi, ap_lo, cholesterol, gluc. smoke, alco, active, cardio. Build a decision tree in python using sklearn. Also, requires hyperparameter tuning and visualizing the tree.
Credit Card Fraud Detection
To correct predict whether a transaction is fraud or not based on the previous records. Requires building a classification decision tree.
Chicago Crime SaaS Case Study
Use of Random forest model to recommend an FBI code based on the different attributes of the crime for the Chicago police.
PUBG Strategy Demystification
Analyse the data of different matches to understand what kind of strategy works better to improve the ranking.
Customer Segmentation Analysis
In this case study, we have built a clustering model for customer segmentation. The data contains various attributes such as InvoiceDate, UnitPrice, CustomerID etc. On the basis of this data, the supermarket store wants to segment its customers into clusters based on their shopping patterns.
Recommendation System using PCA and KNN
Implemented the movie recommendation system (Item based filtering) by reducing the dimensionality of the movies vs tags dataset using PCA algorithm. PCA act as dimensionality reduction technique and using this we are able to compress the data in 100 dimensions instead of 1000 dimensions (tags) in the original dataset. Now using these 100 dimensions, we can find the top nearest 5 movies corresponding to a particular movie which came out to be very effective item based recommendation system.
Top Skills You Will Learn
Job Opportunities
Who Is This Program For?
Minimum Eligibility
300+ Hiring Partners
Instructors
Learn from India's leading Machine Learning faculty and industry leaders
Dr. Janakiram D.
Ujjyaini Mitra
Mirza Rahim Baig
S. Anand
Vinod Seshadri
Vaidehi Takalikar
Curriculum
Pre-Program Preparation
- Introduction to Python6hr
- Python for Data Science10hr 10min
- Data Visualisation in Python5hr 5min
Introduction to Machine Learning and Cloud
- Exploratory Data Analysis
- Introduction to Machine Learning
- Introduction to Cloud
- Cloud Infrastructure using AWS
- Course Assignment
Data Warehousing and Data Management
- Introduction to HDFS
- Introduction to SQL
- Advanced SQL
- Hive Querying
- Course Assignment
Big Data Processing using Spark
- Introduction to Spark
- PySpark Programming
- Dataset Processing using PySpark
- Course Assignment
Machine Learning - I
- Inferential Statistics
- Hypothesis Testing
- Linear Regression - I
- Linear Regression - II
- Logistic Regression
- Course Assignment
Machine Learning - II
- Decision Trees
- Random Forest Trees
- Course Assignment - I
- Principal Component Analysis
- Clustering
- Course Assignment - II
- Implementation: Regression + Model Selection on SageMaker
- Course Assignment - III
Deep Learning and NLP Fundamentals
- Introduction to TensorFlow
- Introduction to Neural Nets
- Convolutional Neural Nets
- NLP Fundamentals
- Sequence Models
- Assignment
NLP and Computer Vision
- Case Study on Computer Vision
- Case Study on applications of Deep Learning in NLP
- Implementation: Regression + Model Selection on SageMaker
- Assignment
Deployment
- Deploying Machine Learning Model
- Capstone Project
The upGrad Advantage
Regular 1-on-1 interaction with Industry Experts
- Discussions on career growth and industry expectations
- Feedback on project work and assignments
Dedicated Student Success Mentors
- Help in learning plans ensuring consistent progress
- Resolve non-academic queries for smooth learning experience
Doubt Resolution on Q&A Forums & WhatsApp Groups
- Timely doubt resolution by qualified teachers
- Regular live Q&A sessions with experts
Networking with Industry Experts & Peers
- Live sessions by industry experts on relevant topics
- Interaction with experts and peers in online and offline events translating into new job opportunities
Job Placement Assistance
- Personalised resume building exercise
- Mock interviews with our hiring experts
- Job Interviews with 250+ hiring partners
Empowering learners of tomorrow
Over 2,300 students have completed this course and started working at their dream job, whats stopping you?
Student Reviews
Argho Chatterjee
Deepesh Kothari
Bhaskar Shankarling
Vignesh Ramakrishnan
Frequently Asked Questions
Program Details
What type of learning experience should I expect?
The content will be a mix of interactive lectures from industry leaders as well as world-renowned faculty. Additionally, the program comprises live lectures dedicated to solving your academic queries and reinforcing learning. Offline upGrad BaseCamps will also facilitate peer-to-peer interactions.
How do I know if this program is right for me?
Experience in areas of Data Analytics / Software Development / Cloud Management / Database Management / Machine Learning, etc.
Knowledge of at least one of the programming language such as R/Python/Java/C/C++
Undergraduate student in disciplines such as engineering, Maths/Statistics.
How will this Program benefit me?
The program will benefit you in different ways depending on your prior experiences:
The program will familiarise you with the advancements in ML and Cloud. It will also help you understand how machine learning models are deployed using cloud so that you can transition to a Data Scientist / Machine Learning Engineer / Data Engineer / Data Analyst (using Cloud) roles.
What can I expect out of this program?
This program is designed for working professionals looking to pick up skills in advanced concepts such as Machine Learning, Cloud, Data Warehousing, Data Management, Big Data Processing, and Deployment of Machine Learning Models. This program demands consistent work and time commitment over the entire duration of 12 months.
Certification
Is there any certification granted at the end of the Program?
After you successfully complete the program, you will receive an Advanced Certification in Machine Learning and Cloud from IIT Madras.
Selection Process
What is the selection process for this program?
upGrad, IIT Madras' world-class faculty, and many industry leaders have committed a lot of time in conceptualising and creating this program to make sure that the candidates can receive the best possible learning experience. Hence, we want to make sure that the participants of this program also show a very high level of commitment and passion for Machine Learning and Cloud.
The applicants will undergo profile evaluation, mandatory selection test designed to check mathematical and programming abilities, and a personal interview.
Refund Policy/Financials
Is there any deferral or refund policy for this Program?
Refund Policy: (Programs with prep-session component)
- Student must pay an amount of ₹25,000 as caution money for the enrollment of the course. This will be adjustable against the total course fee payable by the student.
- You can claim a refund for the course at any time before the cohort start date by sending an email request to your Student Mentor and Admissions Counselor with reasons listed. The prep login will be activated immediately post completion of the 'payment of the 1st block amount (caution money)' or on the 'Specified date' as communicated by upGrad. Processing fee of ₹10,000 will be levied in case refund is claimed.
- Student has to pay the full fee within 7 days of payment of block amount or cohort start date, whichever is earlier, otherwise the admission letter will be rescinded and processing fee of ₹10,000 will be levied.
- Once the student pays block amount, "any" refund shall be subject to deduction of ₹10,000 processing charges.
- Any taxes paid by the student shall not be refundable, and the amount to be withheld will be higher of taxes paid or 10,000 processing fees.
- There shall be no refund applicable once the program has started. This is applicable even for those students who could not complete their payment and could not be enrolled in the batch opted for. However, the student can avail pre-deferral as per the policy defined below for the same.
- Refund shall be processed to an eligible student within 30 working days from the date of receipt of refund form from him/her in this regard.
- Refund shall be subject to deduction of ₹10,000 processing charges or the GST paid, whichever is higher as mentioned above.
Deferral Policy: (Post Program Commencement)
- If a student is facing severe issues in dedicating time to the course, we provide the opportunity for the student to defer to another batch.
- A student can request for deferral only once and from the batch start date of initial batch the student enrolled for.
- The student will be required to pay a deferral fees of 10% of the total course amount fee + Taxes if any along with the differential program fees between the two cohorts.
- The deferral request will be approved once the deferral fee is paid.
- Till this is completed, the student will be assumed to be continuing in the same cohort.
- The student has 7 days (including holidays and weekends) from the date of deferral request to make the payment of the deferral fee post which the deferral request will expire, and the student will continue as part of the current cohort.
- If the student completes the deferral payment, the student's login will be disabled, the student will leave the deferred cohort and the student will start learning on the new cohort from the point of the last assignment that was graded in the deferred cohort. All grades and progress until that point will be carried forward as it is to the new cohort. For clarification, the grades of the graded assignments will be carried forward, whether or not the student had submitted these grading during the initial stage.
- The deferral can only be requested during the batch for which the student has enrolled is ongoing. Once the batch has completed, deferral requests shall not be entertained. For clarification, the batch completion here shall mean the "last grace deadline" as communicated by upGrad.
Deferral Policy: (Pre Program Commencement)
- If a student, due to unavoidable circumstances is unable to commence with the cohort and requests for a deferral before the cohort starts, we provide the opportunity for the student to defer to another batch.
- However, the student will be required to pay 50% of the total course fee amount (inclusive of taxes) before the deferral can be approved. Till this is completed, the student will be assumed to be continuing in the same cohort.
- A student can request for deferral only once and from the batch start date of initial batch the student enrolled for.
- The student has time till the current cohort launch date to make the payment of the 50% program fee, post which the deferral request will expire. Once the deferral window expires and the student now asks for a refund, the above-mentioned applicable refund policy will apply.
- The fee applicable to the deferred student will be as per prevailing fee for the batch student as opted to defer to (No additional deferral fee is required to be paid).