Comprehensive Introduction to Machine Learning
Data-Science Workshop: (ML-100)
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The journey of a thousand miles begins with a single step: join this workshop to cultivate in-depth hand-on expertise in Data-science! |
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$400 discount for self-employed, students, or currently unemployed |
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Greetings! the Summer 2021 session of the Comprehensive Introduction to Machine Learning workshop is here!
Starts: Tuesday, June 15th, 2021 from 7:00 PM.
This is a gentle but in-depth introduction to the field of Machine Learning, with equal emphasis on understanding the theoretical foundations, as well as hands-on experience with real-world data analyses on the Google cloud platform. The workshop spans over 100 hours of training, comprising theory sessions, guided lab sessions, quiz reviews, research paper readings, and project guidance.
AT SUPPORTVECTORS CAMPUS VS REMOTE PARTICIPATION
Participants are encouraged to attend in person at SupportVectors so that they can form strong learning teams and participate in the dynamic learning community.
However, it is equally possible to attend all the sessions remotely. The workshops are designed in such a manner as to ensure that remote participants are equally a part of the learning experience.
Each session is live over zoom, and very interactive. A group of teaching assistants will monitor the stream of questions from students, and moderate their participation in the live discussions with other students and the professor during the sessions. Each session is also live broadcast over a dedicated private YouTube link, so that participants may choose to participate live on their TV.
Through the dedicated course portal, the participants will find a wealth of educational resources associated with the subject and the workshop, including recordings of all the sessions, the solutions to the labs and quizzes, etc.
Format
This is a 10-week workshop and will cover a total of 40-sessions spanning a total of approximately 100 hours of training. Each week, there will be the following sessions:
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THEORY SESSION EVERY TUESDAY EVENING: This will be a 3-hour lecture session. This session can be attended in person at SupportVectors. For those who are unable to attend in person, you can attend it remotely over Zoom, as well as live on Youtube (on the SupportVectors channel, over a special link).
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GUIDED HANDS-ON LAB SESSION EVERY THURSDAY EVENING: This will be a 3-hour hands-on lab session. Besides the professor, there will also be a group of TAs (teaching assistants) giving personal attention when needed to the participants. These sessions too can be attended remotely.
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WEEKLY QUIZ EVERY SATURDAY NOON: Every week, a quiz will be released on Thursday evening. The solutions to the quiz will be reviewed every Saturday at noon. This session is optional to attend -- those participants wanting to keep their weekends free can always watch the Youtube recordings later when convenient.
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WEEKLY RESEARCH PAPER READING EVERY SUNDAY NOON: (OPTIONAL) One of the essential skills to develop as a data scientist is to read the important research papers that emerge, in order to keep abreast of the latest developments in the field. The weekly research paper reading, where we carefully explain a paper in simplified terms, is very popular among the SupportVectors participants and alumni. Many consider it among the most valuable part of the learning experience.
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WEEKLY PROJECT: Each week, the participant will build one or more machine-learning models around a significant dataset, and created a detailed jupyter notebook around it. In order to do this project, the participants will work in small teams comprising of up to 4 other participants. The teaching assistants will provide continual guidance and help in doing the project. The participant teams are strongly encouraged to reach out to the teaching assistants for close engagement and feedback on the project.
Target Audience
This workshop is specifically targeted toward those who are aspiring to be expert data scientists. Data science has rapidly emerged as the new essential literacy. Every sphere of our 21st century is being deeply molded by data science/machine learning.
Therefore, this workshop is deliberately crafted to address a diverse audience. The conceptual foundations would be greatly relevant to managers and aspiring data scientists alike, whereas the hands-on data labs will inculcate in-depth expertise in those wanting to pursue data science as a career.
Prerequisites
There are no prerequisites to this workshop beyond an elementary familiarity with high-school math, with concepts such as mean, median, standard deviation, etc.
Programming Languages: Some familiarity with any one programming language in the past is desirable, but not absolutely necessary. Many students have come in the past without any programming background at all: they have easily adapted to the workshop and handled the pace without undue stress.
Programming Languages
In the course of the workshop, the student will become familiar with Python and R languages and their data-science libraries. Python labs and solutions will be considered mandatory, though the students will be encouraged to optionally work out the labs in R too, and be provided the guidance accordingly. The hands-on labs and projectswill inculcate in-depth skills in machine learning and investigating large and complex datasets.
The necessary training in the AI toolset of the Google Cloud Platform is integrated into the workshop.
Syllabus
In the course of the workshop, the following topics will be covered in considerable depth, and be the context of intensive hands-on labs:
- Google Cloud Platform basics for data-science exploratory notebooks
- Linear, Polynomial, and Non-Linear Regression, along with power-transforms and basis changes to linearize datasets, Principal Components Regression
- Regularization: Ridge and Lasso for Regression
- Classifiers: Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis
- Clustering: Agglomerative, K-Means family of clusterers, Density-based clusterers (DBScan, Optics, Denclue), and Expectation Maximization
- Dimensionality Reduction with Principal Components Analysis, and Matrix Factorization, t-SNE and UMAP
- Sampling, Bootstrapping, and Cross-Validation
- Geometrical background and intuition behind the major algorithms
- Bias-Variance Trade-off, model vs data complexity, and hyperparameter tuning through grid-search
- Getting started with Deep Neural Networks
- A survey of Support Vector Machines, RandomForest, Decision Tree and XGBoost
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$400 discount for self-employed, students, or currently unemployed. |
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DATE AND TIME
STARTS ON TUESDAY 15th, 2021, 7:00 PM
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Venue
SupportVectors Big-Data AI Lab
46540 Fremont Blvd, Suite 506
Fremont, CA 94538
Ph. : 1-855-LEARN-AI
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Questions?
Contact us with your questions, and we would be happy to guide you:
Phone: 1-855-LEARN-AI
You can also make an appointment to discuss your training and career goals:
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Student Feedback
"Rating: 5/5 Support Vectors' "Machine Learning with R Introductory Course" offers a very thorough, intensive, and yet remarkably beginner friendly way for students of varying expertise to study Machine Learning. Asif Qamar, with his decades of experience in both Machine Learning and mathematics, masterfully teaches difficult and intricate topics in a way that students can easily understand complex real-world applications. Asif reaches this due to his inextinguishable passion for both the field and academia. He has created resources that are excellent for reference far beyond the scope of the class. There is very little doubt in that I would recommend Asif to a friend and will definitely be continuing on to the higher course." - Abel
"As a beginner, I did not know how and where to start learning "machine learning." I came across Dr. Asif's machine learning workshop. I am extremely happy that I took this workshop. It's an eye opener! I would not have this level of understanding without the class" - Pathak
"...really helped in understand the technical jargon in machine-learning books and documentation"
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Clinic Hours for Personalized Help
As part of this workshop, you can schedule a direct 1-1 meeting with the instructors once a week to clarify your doubts, or help with the lab.
There is also facility for groups of students to study and discuss together.
Lifetime Access to Help
As a member of this workshop, you will be part of the perpetual discussion-group centered the topics covered.
So long after you have graduated, should you have a question or need a clarification, you can still out to us for help through the discussion-group.
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Instructor: Asif Qamar
Asif has been an educator, teaching various subjects in AI, Machine-Learning, and Physics for the last 30 years. He has been honored with various teaching excellence awards in the universities and tech companies.
Currently, he leads R&D efforts in large-scale AI projects, as a Vice President and Chief Architect.
He has taught at the University of California, Berkeley Extension, University of Illinois, Urbana-Champaign (UIUC), and Syracuse University. He has also given many workshops, seminars, and talks at technical companies.
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Additional Benefits
Each trainee has access to free help clinic hours with the instructor and teaching assistants(by appointment), where you can get individualized help and technical guidance with the contents of the workshop.
The training takes place at a start of the art training facility with climate control, spacious ergonomic seating, expansive desk spaces, lecture rooms equipped with high-end audio-video equipment, and expansive blackboards.
The Break-room is stocked with generous amounts of free fruits and refreshments. Freshly ground Starbucks coffee is brewed and served free, along with a host of other hot and cold beverages.
There is a lounge for trainees to indulge in social learning and collaboration, and a break-room well stocked with refreshments.
The facility also has a large Big-Data AI cluster of massively powerful servers, that students use for their more advanced exercises.
The entire facility is ADA compliant for students with special needs or disabilities.
We make every effort to create a welcoming, safe and hospitable environment conducive to learning and the free flow of ideas.
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