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TechDev Academy

A Silicon Valley-based Startup Company founded by young entrepreneurs and professionals from different backgrounds including education, IT, science, business administration, and marketing in 2019.

Your Ultimate Guide to Machine Learning

Your Ultimate Guide to Machine Learning

Machine Learning

Machine learning or ML refers to the field of study that enables computers to make predictions and/or decisions based on what they have learned from the given data. Here, different types of advanced techniques are utilized to help a machine learning system (computer) perform. These days, machine learning has become extremely important. A lot of industries are now developing more powerful machine learning systems that can analyze more complex datasets and deliver more accurate results quickly. This enables businesses to identify strategic business opportunities, as well as, potential risks quickly. The availability of almost countless volumes of data and rapid advancements in the machine learning field has compelled industries, which depend on massive amounts of data, to embrace the technology to improve their bottom line.

 

Industries that greatly depend on machine learning

A lot of tech giants including IBM, Google, and Facebook have already started using machine learning as one of their primary technologies. Startups and small-scale businesses are also leaning toward using this technology to improve their functionalities and performances. Here’re some industries that are using machine learning to a great extent.

 

Machine LearningTransportation: None of us likes to sit in the vehicles and wait for the green lights, particularly when there is not any vehicle appearing from the other end. Thanks to machine learning, traffic lights have already started getting smart. In many countries, machine learning algorithms are efficiently monitoring and managing traffic. Most probably, you have already heard that self-driving cars are going to be the future of transportation. Once activated, the machine learning system in the vehicle can control its speed, park it, etc without requiring any input from the driver.

 

Finance: The finance sector has always been vulnerable to fraudulent activities. Financial institutions now, with the help of machine learning systems, can accurately identify signs of fraud and mitigate them. They can also flawlessly review the financial portfolios of clients to assess risk. Machine learning helps investors to devise advantageous investment plans by predicting the highs and lows of stock values.

 

Marketing: Lots of marketing and sales companies have already implemented machine learning systems to improve customer satisfaction. Today, they can analyze social media and e-commerce sites to identify the search and buying history of customers and make recommendations based on those. Many experts assume that the future of this industry will be heavily driven by machine learning as the systems will become more efficient at gathering, analyzing, and utilizing data to provide individuals with more personalized shopping experiences.

 

Agriculture: Machine learning applications help to accurately analyze weather conditions, field conditions, and crops to give better insights into different hindrances in agriculture. These systems are also helping in maintaining crop quality by aiding in better soil and water management.

 

In addition to these, industries such as education, manufacturing, advertising, food, healthcare, etc. are also benefitting from machine learning.

 

Top present and future jobs in machine learning

For those, who want to pursue a career in machine learning, here’re the jobs that are in high demand not only today but will remain so in the future as well.

 

Machine learning engineers: These people program machine learning systems to intelligently perform tasks. They’re highly skilled in various programming languages such as Java, Python, etc, and work with massive amounts of data.

 

NLP engineers: NLP is the short form of natural language processing that enables machines to comprehend human language. NLP engineers help to develop machines that are capable of understanding patterns of speech and translating verbal words into various other languages. In addition to machine learning skills, these professionals have a solid knowledge of grammar, syntax, and spelling of a minimum of one language so that they can train a machine the same.

 

Human-centered machine learning designers: The job role of these professionals revolves around developing systems that can recognize patterns and process information based on the available data. Netflix and YouTube are two such examples where the platforms offer suggestions based on viewers’ preferences.

 

Apart from these, professionals like data scientists, robotics engineers, big data engineers, and business intelligence developers are also anticipated to remain sought-after in the upcoming years.

 

Why parents should introduce their kids to machine learning

As machine learning appears with the potential to revolutionize the way we interact with technologies, a lot of companies have already started hiring experts in the field. In the future, this demand will be even greater with more businesses embracing this disruptive technology. If you’re still hesitating about whether or not you should introduce your child to machine learning, the following reasons could help make up your mind.

 

Helps to learn to code: Coding is an inherent component of machine learning and thus, your kids will automatically get introduced to coding when you introduce them to ML. Coding is one of the most sought-after job skills that open up a plethora of opportunities. Additionally, learning to code would help them develop logical thinking and mathematical reasoning which will become extremely beneficial for their future professional life even if they don’t pursue a career in machine learning or become a programmer.

 

Helps to develop data fluency: We’re heading toward a future that will be shaped by data-driven methods. In their effort to learn machine learning, kids will understand how gathering and analyzing datasets could help them land them a rewarding job in the era of big data. Every kid may not become a data analyst or a data scientist but they will become familiar with data processes, which will be almost essential in the future job market.

 

How to start teaching kids machine learning

Here’re some simple yet effective ways that parents can use to introduce their kids to machine learning.

 

Use applications: Coding applications like Tynker and Scratch let kids utilize prewritten coding instructions to help them develop algorithmic thinking. To help your kid learn the fundamentals of machine learning, you can use resources such as Machine Learning for Kids, Apps for Good, etc.

 

Use online courses: This is probably the best method when you are trying to introduce your kids to machine learning. Cutting-edge online courses on machine learning are designed to help kids obtain a robust foundation in technology. Some top-tier institutions also offer coding workshops so even if your kid doesn’t know to code, he/she can start from scratch.

 

Machine learning has the potential to transform many aspects of human lives for the better. It has the responsibility of parents to act now and start preparing their kids who could be the ones contributing to future developments in the field.

 

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JOHN H. FALK

Director of the Institute for Learning Innovation and Sea Grant Professor Emeritus of Free-Choice Learning at Oregon State University

 

Dr. John H. Falk is Director of the Institute for Learning Innovation and Sea Grant Professor Emeritus of Free-Choice Learning at Oregon State University. He is a leading expert on free-choice learning; the learning that occurs when people have significant choice and control over what, where, and when they learn.

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Get your syllabus

 

CHRISTINE CUNNINGHAM

Professor of Practice of Education and Engineering at Penn State University

 

Dr. Christine Cunningham is a Professor of Practice of Education and Engineering at Penn State University. Her work focuses on making engineering more relevant, accessible, and inclusive, especially for underserved and underrepresented populations. She does this by creating researched-based engineering curricula for PreK-8 children and their educators. Her book, Engineering in Elementary STEM Education, describes her groundbreaking work. Previously, Cunningham was the Founding Director of Engineering is Elementary. Cunningham has received a number of awards; in 2017 her work was recognized with the prestigious Harold W. McGraw Jr. Prize in Education.

 

CHRIS ROGERS

Chris Rogers Professor and Chair of Mechanical Engineering at Tufts University

 

His research interests are:

Engineering Education, Robotics, and Musical Instrument Design

 

Education

Ph., D Mechanical Engineering, Stanford University
M.S., Mechanical Engineering, Stanford University
B.S., Mechanical Engineering, Stanford University

 

Biography

Chris Rogers earned his B.S., M.S., and Ph.D. in mechanical engineering at Stanford University, where he worked with Professor John Eaton on his thesis on particle motion in a boundary layer flow. Rogers joined the Department of Mechanical Engineering at Tufts School of Engineering in 1989. He is involved in a number of research areas, including particle-laden flows (a continuation of his thesis), telerobotics and controls, the slurry flows in chemical-mechanical planarization, the engineering of musical instruments, measuring flame shapes of couch fires, measuring fruit-fly locomotion, and engineering education (kindergarten to college). At Tufts, Rogers has exercised his strong commitment to teaching by exploring a number of new directions, including teaching robotics with LEGO bricks and teaching manufacturing by building musical instruments. His teaching work extends to the elementary school level, where he talks with over 1,000 teachers around the world every year on methods of introducing young children to engineering.

 

RumeysaDogan

RUMEYSA DOGAN

Co-founder and COO at TechDev Academy

  • Graduated from top-ranked business school with high honor
  • Worked in top global companies as Vodafone, Benetton Group, etc
  • Experienced in Product Management and Digital Marketing Analytics
  • Managed Entrepreneurship Club and mentored several entrepreneurs

 

 

ismail-marulcu

ISMAIL MARULCU

Co-founder & Chief Education Officer at TechDev Academy

  • Educator and Researcher since 2001
  • M.Ed. in Curriculum and Instruction from Boston College
  • Ph.D. in STEM Education from Boston College
  • Mentored more than 100 pre-service teachers, college students, and high school students

 

 

PaolaGomez

PAOLA G. GONZALES

Mentor & Educator

  • over 2,000 hours mentoring students and 4 years of teaching experience
  • spearheaded a nonprofit organization that provides mentorship to underrepresented students at the UC, Davis
  • an active member of the Surfrider Foundation

 

 

AyushKanodia

AYUSH KANODIA

Ph.D. Student in Computer Science at Stanford

  • Ph.D. Candidate in Computer Science at Stanford Uni.
  • Worked as a software engineer for Google
  • Expert in the intersection of Computer Science and Economics.

 

 

KairatSabyrov

KAIRAT SABYROV

Ph.D., Data Scientist

  • B.S. in chemistry and physics
  • Ph.D. In physical chemistry
  • Data science instructor at Lambda School
  • Worked at Lawrence Berkeley National Lab at the UC, Berkeley

 

 

BAHRUDIN TRBALIC

Ph.D., Candidate at Stanford University

  • Studied Physics & Electrical Engineering at MIT.
  • Worked at MIT as a Medical Data Analyst and Product Developer.
  • The founder and lead developer of Expert Experiments.
  • Received the 2023 Robert H. Siemann Graduate Fellowship and 2022 NASA Astrophysics Research and Analysis Award.
  • Spearheaded STEM camps across Europe and Asia.
  • He has been mentoring students for years.

 

 

SHASHA ANRONIKOV

Researcher at Stanford University

  • Recent honors graduate from Cornell University with a major in biological sciences and a minor in business at the College of Agriculture and Life Sciences.
  • Currently working at Stanford University in the Nolan Lab to conduct immunopathology research.

 

 

LISA WANG

BSc Harvard University Graduate

  • Studied Environmental Science and Engineering.
  • Cross-registered to Harvard Univ. and MIT.
  • An advisor to the Harvard Undergraduate Clean Energy Group.
  • Co-founder of Coolant, a company that builds software to unlock nature-based carbon markets.

 

 

SEMI HASAJ

MBA Data Scientist at C3 AI

  • Studied Data Science while obtaining his Master's of Business Analytics at MIT.
  • Studied Space Engineering in Toronto, Canada where he grew up.
  • He has spent years tutoring others because he loves to help people learn and grow.

 

 

SAMY AWWAD

Junior at Stanford University

  • Studying Symbolic Systems with a focus on Neurosciences and plans to be a medical doctor.
  • Founded ImmuniGlobal, a national nonprofit in vaccine education, and he was featured in Healthline magazine.
  • A published researcher in PubMed.
  • Honored by the CDC as a Flu Fighter during the COVID-19 pandemic.
  • Enthusiastic about empowering young changemakers.

 

 

HASAN TUNCER

Ph.D., Product Manager at Cruise

  • BSc. in Computer Science at Koc University, Istanbul.
  • Ph.D. in Computing and Information Scienves at Rochester Institute of Technology in New York.
  • Worked as a software engineering at Wall Street.
  • Product Manager for Cloud Services (at IBM Silicon Valley Lab), autonomous vehicles (at NIO, aka Chinese Tesla, Uber ATG, Aurora and Cruise)

 

 

RayYucel

RAY YUCEL

Ph.D., Data Scientist in Magnimind Inc.

  • B.S in Materials Engineering
  • M.Sc in Management
  • Ph.D. Candidate in Economics
  • Data scientist at Magnimind Inc.
  • Employs deep learning in finance and health care data

 

 

SofoklisGoulas

SOFOKLIS GOULAS

Ph.D., Senior Research Associate at the Hoover Institution at Stanford University

  • Senior research associate, Stanford Uni.
  • The use of data science and machine learning in economics
  • M.Sc. in finance and economics, Warwick business school
  • MS and a Ph.D. in economics, the Uni. of North Carolina at Chapel Hill
  • Worked at the Uni. of North Carolina and at the Bank of Greece

 

 

EnricoSantus

ENRICO SANTUS

Senior Data Scientist at Bayer

  • Senior data scientist at Bayer
  • Postdoc at MIT, in the group of Regina Barzilay
  • Experience in NLP in Oncology, Cardiology and Palliative Care
  • Experience in Fake News Detection, Sentiment Analysis, and Lexical Semantics.
  • Invited to talk at the White House

 

 

EMILY HALFORD

Data Analyst

  • Data analyst working in psychiatric epidemiology
  • Data Science&Mental Health Expert with the BBN Times
  • Master of Public Health, Columbia Uni.

 

 

RyanSpitler

RYAN SPITLER

Ph.D., Co-Founder and Deputy Director of the Precision Health and Integrated Diagnostics Center (PHIND) at Stanford University

  • Faculty Member, Standford Uni.
  • Founding Partner at Boutique Venture Partners
  • B.S. in Molecular Cell and Developmental Biology, UC, Santa Cruz
  • Ph.D. In Cellular and Developmental Biology, UC, Irvine

 

 

muratbaday

MURAT BADAY

Scientist at Stanford Uni, Founder & CEO at TechDev Academy

  • Co-founder of Smartlens, Magnimind, Wowso, Nanosight
  • M.S. in Physics from the University of Pittsburgh
  • Ph.D. in Computational Biology and Biophysics from the Uni. of Illinois at Urbana-Champaign
  • Mentored and tutored over 100 high school students
  • Developed novel ideas and has over 8 patents

 

 

GyunelRashidova

GYUNEL RASHIDOVA

B.S. in Biological Sciences,
Research assistant at the Laboratory of Biosensors and Bioinstrumentation

  • iGEM alumni, received Gold Medal among 250 teams
  • Fellowship holder from Women in Tech international organization
  • Founder of social projects:
    “OncoSense” - fabrication of device for the detection of cancer biomarkers;
    “RemiSee” - promotion of a colorblind-friendly educational platform
  • AIESEC alumni, organized case competitions with over 300 participants
  • Organized iGEM Biohackathon and Summer Camp for high-school and university students to apply coding for solving real case studies

 

 

SoudehYaghouti

SOUDEH YAGHOUTI

Ph.D., Data Scientist at Megalab, Silicon Valley

  • Ph.D. in Electrical Engineering, University of Naples Federico II, Italy.
  • More than 4 years of experience in data-driven research on electrical network systems.
  • Collaborating with TechDev Academy for several years and taught students data analysis projects.
  • Collaborated with Stanford scientists on projects that aimed to automate medical diagnosis of diseases with the help of image processing techniques and AI.

 

 

AIZHAN IBRAYEVA

MSc Researcher at Stanford University

  • MS. Aerospace Engineering from Purdue University.
  • Did research at Stanford University, Aerospace Science Lab (Purdue), Rarefied Gas Dynamics Lab (Purdue)
  • Worked on projects supported by NASA.
  • Worked as Engineer at Silicon Valley Startup companies.
  • Mentored Students from top US school

 

The class has 5 available spots.
You can add the class during course registration!

 

June 1-5

Mon-Fri 2 hours of daily instruction and 2 hours of self-paced project development.

June 8-12

Mon-Fri 2 hours of daily instruction and 2 hours of self-paced project development.

June 15-19

Mon-Fri 2 hours of daily instruction and 2 hours of self-paced project development.

The class capacity is full.
Please try other classes!