Get an opportunity to work with us in Smart city, Health Care, Telecom, and Agricultural sectors
Jobs First And Pay Later After You Earn Min 5L-7L Per Annum
Industry-Driven Project-Based Learning on Data Science/AI /ML /IoT
Opportunities in Dell, Microsoft, NetApp, Amazon, E&Y, VMware, Chase, HP, Shell
Program 1 - Cross-Industry ML/AI Foundation Program for New Aspirants
Industry-Driven Project-Based Learning on AI /ML /IoT
For Final Year Pursuing and Freshers and Pass out with gap with no prior knowledge on ML/AI
- Course Name: Industry Driven Project-Based Learning on AI /ML /IoT
- Initiative: Job First, Pay Later after you earn Min 5L-7L Per Annum in 6 Installment with no interest
- Duration: 4 months
- Internship /Placement Assistance: Yes
- Who Can Join: Pursuing Final Year B.Tech/M. Tech in CSE /IT & ECE and Freshers pass out in 2022, 2021 & 2020
- Selection Process: 1: Written-test 2: PI
Now Increase your chance of availing a SCHOLARSHIP!!
- Earn a 100% scholarship for the top 10 selected candidates.
- Earn up to 75%, 50%, and 25% scholarship exemption for the next 20 candidates based on their performance.
Training options
Trainees have to choose any one option for payment of the fees.
- Rs 5900 of the total fees will be paid during the time of registration and rest amount of the total fees just after the placement in 6 EMI. A fee for this mode is Rs 59000.
- The base Fee is Rs 59000, In the case of the trainee, doesn’t want to avail of the placement offer after training and wants to choose the placement through a college campus or through off-campus. . Trainee can pay the fees in 6 EMI.
- Trainee has to sign training enrollment agreement form.
Terms & Conditions
- First party/eleve8 is not giving any assurance or guarantee of job during or after completion of training but First party/eleve8 is giving assurance & guarantee to provide the best training and to provide the written and interview opportunities in companies during the training and after the completion of training and till the trainees get the placement in core companies.
- The Second Party/Trainee expressly agrees to make a payment of 10% of the total fee for the course/program opted by the trainee at the moment of signing the agreement and the trainee will pay the remaining fees in 6 monthly installments after being benefited from the services so availed from the first party/eleve8 which may reflect from the Second Party/Trainee getting hike/appraisal either in his/her current job profile or the salary/emoluments etc.
- Second Party/Trainee has to pay the complete base fees after the training in 6 monthly installments, In case the trainee has completed training and placed through a college campus or off-campus, or doesn't want to take placement through first-party/eleve8.
- If the second party/trainee left in between training, then in that case no registration fee will be refunded to the second party by the first party which is paid by the second party at the time of registration for the respective course/program and second party has to pay complete fees in 3 installments.
Program Prerequisite
-
Basic understanding of the following:
Python (Numpy, Pandas, Scikit Learn), SQL, Linear Algebra, Statistics, Probability, Unix Shell scripts -
Basic H/W Configuration:
Laptop/Desktop with minimum 8GB RAM with i3 processor. (To setup virtual Big Data environment)
Program Fees
- Please register to get the details about the course fees.
Program Duration
- Total duration of online program - 14 weeks. Weekly one session of 2-3 hours.
Program Outcomes
- Hands-on experience on end-to-end Machine Learning projects (Data Extraction to Deployment )
- Business Interpretation and Visualizations of the model through executive dashboards
- Understand and mitigate real challenges faced while building machine learning solutions
- Chance to work as an intern in a US-based organization
- Job Assistance in Data Science domain
Program 1 - Cross-Industry ML/AI Foundation Program for New Aspirants
Session 1 - Empowering cross-domain industries to realize their vision with ML/AI an enabler (Week 1)
- Recognize the need for machine learning in the cross-domain industry business model
- Identifying the building blocks of data-driven business strategies
- How to interpret client requirements and needs
- Applications of Machine Learning in specific industry domain (examples)
- Converting business problem to Analytical problem
- Demonstrating gap between industry expectation and gained knowledge
Session 2 - Gear-up for the Journey– 1 (Week 2)
- Data collection and its challenges
- Statistical Inferences – Business Case Study
- Industry-wide employed Data Exploration Techniques
- How to identify and mitigate data balance and Imbalance issues
- Intuitionfails in high dimension
Session 3 - Gear-up for the Journey – 2 (Week 3)
- Regression and its shrinkage
- Clustering the distribution
- Classify the classes
- Common Pitfalls in Machine Learning ( Resampling Bias and Variance, Feature Selection Leakages)
- Top 10 data Mining Algorithms
Session 4 - Project 1 – IoT (AI-based preventive maintenance solution for automobiles) (Week 4)
- Importance of IoT solutions in the industry
- IoT fundamentals
- Understanding of Automobile parameters, Impact and use cases
- Big Data architecture to host and store sensor data
- Real-time sensor data processing through Spark
Session 5 - Playing with real-time sensor data (Week 5)
- Working in data collection adapters/APIs
- Transforming wealth of data to intelligent features
- Inductive Reasoning -Exploratory Analysis
- Correlation Does Not Imply Causation
- Important features extraction from relevant parameters
Session 6 - Learn Many Models, Not Just One (Week 6)
- Data Alone Is Not Enough
- Intuition Fails in High Dimensions (PCA)
- Theoretical Guarantees Are Not What They Seem
- Train and Test set
- Overfitting/Underfitting Has Many Faces
Session 7 - Art of Model Identification and Evaluation (Week 7)
- Evaluating the models and their results
- Complicated does not equal better
- Selection of best fit based on a business problem
Session 8 - Demonstration of business perspectives derived from model (Week 8)
- Mapping model outcome inferences with preventive KPIs
- Converting inference to business insights
- Creating executive and operational dashboard
Session 9 - Cloud Implementation and Deployment (Week 9)
- Introduction to a cloud-based platform
- Development and deployment of a model
- Challenges of deploying large scale solutions
- Operationalization of overall solutions
Session 10 - Project 2 – Telecom (Anomaly Detection and Forecasting on Mobile Network KPI) (Week 10)
- Telecom domain fundamentals
- Understanding of Telecom network parameters, Impact and use cases
- Big Data architecture to host and store network data (routing, switching, firewall, etc.)
- Real-time network data processing through Spark/Nifi
Session 11 - Playing with real-world network data (Week 11)
- Working in data collection adapters/APIs
- Transforming wealth of data to intelligent features
- Inductive Reasoning - Exploratory Analysis
- Important features extraction from relevant parameters
Session 12 - Learn Many Models, Not Just One (Week 12)
- Data Alone Is Not Enough
- Intuition Fails in High Dimensions (PCA)
- Theoretical Guarantees Are Not What They Seem
- Train and Test set
- Overfitting/Underfitting Has Many Faces
- Selection of best fit
Session 13 - Art of Model Identification and Evaluation (Week 12)
- Evaluating the models and their results
- Complicated does not equal better
- Selection of best fitbased on business problem
Session 14 - Demonstration of business perspectives derived from model (Week 13)
- Mapping model outcome inferences with actual KPI of business problem
- Converting inference to telecom business insight
- Creating executive and operational dashboard
Session 15 - Cloud Implementation and Deployment (Week 14)
- Development and deployment of a model
- Challenges of deploying large scale solutions
- Operationalization of overall solutions