Machine Learning for Business-No-Code Solutions
Unlock the Power of Machine Learning Without Writing a Single Line of Code!
Why Machine Learning for Business?
In this data-driven age, Machine Learning (ML) has evolved from a technical novelty to a business necessity. Our course demystifies ML, focusing on no-code solutions that allow professionals to turn heaps of data into actionable insights and strategies, without the need for programming skills.
Duration: 30 Hours (online / virtual live session)
Who Should Attend?
Business executives, startup founders, managers, data-driven marketers, and anyone looking to harness the power of ML without delving into coding. If you're passionate about making data-driven decisions and innovations in your business, this course is for you.
Course Highlights
ML Minus the Code: Discover the rising world of no-code ML platforms, making data science accessible to all.
Decoding ML, the No-Code Way: Grasp foundational ML concepts without getting lost in technical jargon.
Decoding ML, the No-Code Way: Grasp foundational ML concepts without getting lost in technical jargon.
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Streamlined ML Workflow: Understand the step-by-step no-code ML process, from data ingestion to actionable insights.
Leveraging Pre-Trained Models: Learn how off-the-shelf models can offer instant business solutions, from image recognition to sentiment analysis.
Model Performance for Business: Understand how to evaluate the results of your ML models using intuitive, built-in tools and dashboards.
Smooth Sailing in ML Projects: Address common challenges and considerations, ensuring successful ML deployments without coding headaches.
Looking Ahead: Dive into the future of no-code ML, exploring how businesses can further simplify and amplify their data-driven initiatives.
Practical Magic: Engage in a hands-on workshop using no-code platforms, transforming real-world business data into insightful strategies.
Leveraging Pre-Trained Models: Learn how off-the-shelf models can offer instant business solutions, from image recognition to sentiment analysis.
Model Performance for Business: Understand how to evaluate the results of your ML models using intuitive, built-in tools and dashboards.
Smooth Sailing in ML Projects: Address common challenges and considerations, ensuring successful ML deployments without coding headaches.
Looking Ahead: Dive into the future of no-code ML, exploring how businesses can further simplify and amplify their data-driven initiatives.
Practical Magic: Engage in a hands-on workshop using no-code platforms, transforming real-world business data into insightful strategies.
Pre-requisites for Participants:
Business Acumen: A basic understanding of business operations and challenges.
Analytical Curiosity: An enthusiasm for harnessing data to drive business outcomes.
Tech Comfort: Familiarity with basic digital tools and platforms. No coding knowledge required!
Analytical Curiosity: An enthusiasm for harnessing data to drive business outcomes.
Tech Comfort: Familiarity with basic digital tools and platforms. No coding knowledge required!
What to Bring?
Laptop/PC
With relevant ML tools/software installed.
Analytical Mindset
Ready to explore and dissect business problems.
Note-taking Materials
For capturing insights and concepts.
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Training Content
Objective: Empower participants with the skills to leverage machine learning in their business operations using intuitive no-code platforms and tools.
1. Introduction to No-Code Machine Learning & Business Context
Objective: Understand the growing trend of no-code ML and its potential impact on businesses.
- The rise and significance of no-code ML solutions.
- Real-world business applications and success stories.
2. Exploring Leading No-Code ML Platforms
Objective: Familiarize participants with top no-code platforms in the market.
- Overview of platforms like Microsoft Azure Machine Learning Studio, Google AutoML, and IBM Watson Studio.
- Key features and strengths of each platform.
3. The ML Process Simplified: From Data to Insights
Objective: Grasp the streamlined ML process using no-code tools.
- Uploading and preparing data without coding.
- Selecting the right algorithm or model template.
- Training and validating models with a few clicks.
- Deploying and integrating models seamlessly into business operations.
4. Building Business Solutions with Pre-trained Models
Objective: Learn how to utilize readily available pre-trained models for business needs.
- Introduction to API-based services like Google Cloud Vision and AWS Rekognition.
- Using pre-trained models for tasks like image recognition, sentiment analysis, and language translation.
5. Evaluating Model Performance with No-Code Tools
Objective: Understand how to measure the success and accuracy of your ML models.
- Using built-in evaluation tools and visualizations.
- Interpreting results and understanding business implications.
6. Overcoming Challenges in No-Code ML Projects
Objective: Address potential obstacles and considerations in no-code ML deployments.
- Ensuring quality data for accurate models.
- Avoiding common pitfalls and misconceptions.
- Ensuring model fairness and ethical considerations in a no-code environment.
7. Integrating ML Insights into Business Workflows
Objective: Seamlessly make ML-driven decisions a part of regular business operations.
- Connecting no-code ML solutions with other business tools and platforms.
- Automated reporting and decision-making using ML insights.
8. Future Trends: The Evolution of No-Code ML
Objective: Stay ahead of the curve by understanding upcoming trends and advancements.
- The growth trajectory of no-code platforms.
- How businesses can stay ahead and make the most of new features and functionalities.
9. Hands-on No-Code ML Workshop & Q&A Session
Objective: Stay ahead of the curve by understanding upcoming trends and advancements.
- Guided workshop: Solving a real-world business problem using a no-code ML platform.
- Recap and reflection on key takeaways.
- Engaging Q&A session to clarify doubts and delve deeper into specific topics.
WOMEN AI ACADEMY
Women AI Academy is a gender-equality and technology driven learning & development organization
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Ali Hessami is currently the Director of R&D and Innovation at Vega Systems, London, UK. He has an extensive track record in systems assurance and safety, security, sustainability, knowledge assessment/management methodologies. He has a background in the design and development of advanced control systems for business and safety-critical industrial applications.
Hessami represents the UK on the European Committee for Electrotechnical Standardization (CENELEC) & International Electrotechnical Commission (IEC) – safety systems, hardware & software standards committees. He was appointed by CENELEC as convener of several Working Groups for review of EN50128 Safety-Critical Software Standard and update and restructuring of the software, hardware, and system safety standards in CENELEC.
Ali is also a member of Cyber Security Standardisation SGA16, SG24, and WG26 Groups and started and chairs the IEEE Special Interest Group in Humanitarian Technologies and the Systems Council Chapters in the UK and Ireland Section. In 2017 Ali joined the IEEE Standards Association (SA), initially as a committee member for the new landmark IEEE 7000 standard focused on “Addressing Ethical Concerns in System Design.” He was subsequently appointed as the Technical Editor and later the Chair of P7000 working group. In November 2018, he was appointed as the VC and Process Architect of the IEEE’s global Ethics Certification Programme for Autonomous & Intelligent Systems (ECPAIS).
Trish advises and trains organisations internationally on Responsible AI (AI/data ethics, policy, governance), and Corporate Digital Responsibility.
Patricia has 20 years’ experience as a lawyer in data, technology and regulatory/government affairs and is a registered Solicitor in England and Wales, and the Republic of Ireland. She has authored and edited several works on law and regulation, policy, ethics, and AI.
She is an expert advisor on the Ethics Committee to the UK’s Digital Catapult Machine Intelligence Garage working with AI startups, is a Maestro (a title only given to 3 people in the world) and expert advisor “Maestro” on the IEEE’s CertifAIEd (previously known as ECPAIS) ethical certification panel, sits on IEEE’s P7003 (algorithmic bias)/P2247.4 (adaptive instructional systems)/P7010.1 (AI and ESG/UN SDGS) standards programmes, is a ForHumanity Fellow working on Independent Audit of AI Systems, is Chair of the Society for Computers and Law, and is a non-exec director on the Board of iTechlaw and on the Board of Women Leading in AI. Until 2021, Patricia was on the RSA’s online harms advisory panel, whose work contributed to the UK’s Online Safety Bill.
Trish is also a linguist and speaks fluently English, French, and German.
In 2021, Patricia was listed on the 100 Brilliant Women in AI Ethics™ and named on Computer Weekly’s longlist as one of the Most Influential Women in UK Technology in 2021.
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