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AI Solutions Mastery

AI Practitioner Plus

 

 

Build a Real-World AI Product

in 4 Weeks

 

Plus One End to End Project

in 5th Week

 

Become a Unicorn with Future-Proof Skills

By Going from Problem to Product

 

 

Do you find it Challenging to Apply AI and data science knowledge to real-world scenarios?

 

Are you Overwhelmed by the Vast amount of AI resources, Unsure where to Start or how to Progress effectively?

Enroll now to Master Translating Business Problems into Complete AI Solutions more efficiently in 5 Weeks.

  1. Understand your Business / Data
  2. Build your AI Base Model
  3. Enhance and Engineer your AI Model
  4. Build a Complete AI Product
  5. Apply All you Learned to One End to End Project

 

Level: Intermediate to Advanced
Start Date: 1 June, 2024
Duration: 5-Weeks
Certificate: Yes
Price (excl. 21% VAT):  

€1979

Cancellation: 7-Days (Up to 20 May)

 

ENROLL NOW

YOU WILL LEARN

 

Roadmap

  • Analyze a Real-world complex problem and outline how data can address its questions.
  • Perform initial data cleaning on a provided dataset. 
  • Build a base AI model using provided data, apply cross-validation, and prepare a presentation of your results aimed at a business audience. 
  • Enhance your model with advanced features and tuning, implement robust cross-validation, and update your business presentation with advanced visualizations. 
  • Compare multiple models and select the best performer.
  • Develop a web application for your chosen model, focusing on user interface and experience. 

 

Techniques

With this comprehensive training, you'll master

  • Supervised and Unsupervised Learning,
  • Both Machine Learning and Deep Learning algorithms for real-world AI projects,
  • Advanced Feature Engineering and Hyperparameter Tuning.

 

Coding / Environment

  • Python Libraries: you will use Tensorflow, SHAP, XGBoost, Scikit-Learn, Hyperopt, Streamlit, Seaborn and more
  • Environment you will work in Google Colab which provides free access to GPUs and TPUs 

YOU WILL BE ABLE TO

 
01 Translate Business Problems into Analytical Questions
Avoiding later Disappointments or Lost Work.
 
02 Apply AI through real-world Projects that mimic Industry Challenges
Preparing you for the Job Market.
 
03 Utilize a Data Science Framework
Enabling you to Build AI Solutions more Efficiently. 
 
04 Navigate the AI Resources
Identifying the most Effective Solutions. 
 
05 Communicate technical insights in a way that non-technical stakeholders can understand and appreciate
Fostering better collaboration and informed decision-making.
 
IF YOU WANT TO ACHIEVE THESE THEN AI PRACTITIONER PLUS IS FOR YOU.

YOUR PROGRAM

 

  Start Date: 1 June 2024
  Duration: 5-Weeks
  Including: Lectures / Q&A / Capstone Project
  Days: Tuesdays and Thursdays 
  Hours: 3-4 hours per week

 

  WEEK 1

   Start the Engine

  • Overview: Lay the Business and Data Foundation by understanding the business question and how data can provide answers. This week is about aligning data science with business objectives, understanding the data landscape, and preparing for the AI journey.

  • Key Topics:

    • Business Understanding: Deciphering the core questions businesses face.

    • Data Comprehension: Getting to know your data and its potential.

    • Initial Data Handling: Basics of data collection, cleaning, and preliminary analysis.

  • Python Libraries:
    • Pandas for data manipulation.
    • NumPy for numerical operations.
    • Matplotlib and Seaborn for data visualization.
    • Pandas-profiling for automatic data reports.
 
 
 

 

  WEEK 2

   AI Solution Light

  •  Overview: Build your Explainable Supervised AI model. This week is centered on feature creation, model building, k-fold cross-validation, model Evaluation, and Effectively Communicating Results to business stakeholders.

  • Key Topics:

    • Feature Engineering: Build your first Features.

    • Building Your First AI Model: Introduction to model selection and training.

    • Cross-Validation: Ensuring your model's reliability.

    • Model Evaluation and Interpretability: Effectively communicating results and understanding model decisions using SHAP values.

  •  Python Libraries:
    • Scikit-learn for RandomForest and model evaluation metrics.
    • XGBoost for efficient gradient boosting.
    • SHAP to interpret model predictions and understand feature importance.

 

  WEEK 3

   AI Solution Engineered

  •  Overview: Enhance your AI model with Deep Learning with advanced feature engineering, hyperparameter tuning, and introduction to Deep Learning.

  • Key Topics:

    • Advanced Feature Engineering: Techniques to improve model input.

    • Hyperparameter Tuning: Optimizing your model for better performance.

    • Introduction to Deep Neural Networks: Build deep NN using TensorFlow or Keras.

  • Python Libraries:
    • Feature-engine for feature engineering.
    • Hyperopt for hyperparameter optimization.
    • TensorFlow and Keras for building simple neural networks.
 

 

 

  WEEK 4

   AI Solution Complete

  •  Overview: Learn Unsupervised Machine and Deep Learning and Build an AI Product. Expand model scope and transition from a model to a deployable web application, introducing anomaly detection techniques.

  •  Key Topics:

    • Model Comparison: Evaluate different models to identify the best fit.
    • Anomaly Detection: Implement Isolation Forest and AutoEncoders for detecting outliers.
    • Product Development: Convert your AI solution into a deployable web application.
  • Python Libraries:
    • Streamlit for creating interactive web applications.
    • Scikit-learn for Isolation Forest.
    • TensorFlow or Keras for building AutoEncoder.

 

 

 

  WEEK 5

   End-to-End AI Project
  • Overview: Apply the skills to a real-world problem from start to finish. This week, you will either select your own problem or choose one recommended by the course, prepare the data, build and optimize a model, evaluate it, and deploy it as a functioning AI product.
  • Key Topics:
    • Project Selection and Planning: Choose your own real-world problem or opt for a recommended project; plan your approach.
    • Data Preparation: Apply advanced data cleaning, feature engineering, and preprocessing techniques learned in previous weeks using enhanced tools.
    • Model Building and Optimization: Construct and refine your model using both traditional machine learning and deep learning frameworks.
    • Model Evaluation: Assess model performance using various metrics and apply interpretability tools to understand model decisions.
    • Deployment: Develop a user-friendly interface for your model using modern web frameworks and deploy it as a web application.
    • Presentation and Personalized Feedback: Present your project to the class, highlighting your methodology, results, challenges, and key insights. Receive personalized feedback to refine your approach and enhance your learning experience.
  • Python Libraries:
    • Pandas, NumPy, Pandas-profiling for data manipulation and numerical operations, with  for automated data reports.
    • Seaborn and Matplotlib for data visualization.
    • Scikit-learn and TensorFlow for building and evaluating machine learning and deep learning models respectively.
    • Hyperopt for hyperparameter tuning.
    • SHAP for model interpretability.
    • Streamlit  for deploying the model as a web application.

 

ENROLL NOW

IT SUITS YOU BEST IF

 

Your Goal is Applying AI to solve real-world problems.

The training expects a foundational understanding of data science but is designed to elevate skills to a more advanced level.

 

This training is ideal for:

  • Aspiring Data Scientists

    If you're at the start of your career and eager to dive into the world of data science and AI, this training will equip you with the foundational knowledge and hands-on skills you need to kickstart your journey.

  • Academics and Researchers

    Those in academia or research-focused roles wishing to apply the latest data science methodologies to their work or contribute to the advancement of AI technologies.

  • Technology Professionals

    Software engineers, analysts, and IT professionals looking to transition into data science roles or add AI expertise to their skillset will find this training invaluable for advancing their careers.

     

YOU NEED TO KNOW 

 

Your success is in your hands. The Training is here for you to build your path.

 
Foundational Knowledge
  • Basic Understanding of Programming: Familiarity with Python is a must. 
  • Mathematics and Statistics Fundamentals: A grasp of high level mathematics, particularly in algebra and statistics, is essential. 

 

Time Commitment
  • Dedication and Time: This intensive 4-week training requires a commitment of 6 to 8 hours per week. This includes watching lectures, completing assignments, and participating in Q&A, workshops and discussions.

 

Mindset and Approach
  • Curiosity and Willingness to Learn: The field of AI and data science is vast and constantly evolving. A curious mindset and an eagerness to learn and experiment with new concepts are key to making the most of this training.
  • Collaboration and Communication: Be prepared to engage with your peers, share your insights, and collaborate on projects. Effective communication and teamwork are invaluable skills in the data science world.

 

WHY JOIN? 

 

The destination: You are not just data scientists in Title but in Practice

  • Initial Orientation and Assessment

    A kickoff webinar to introduce the course structure, tools, and platforms we'll use, and assess individual goals and backgrounds to tailor guidance.

 

  • Weekly Structured Learning Modules

    Each week focuses on a specific aspect of AI solution development, starting with understanding business questions and data (Week 1), building and evaluating base AI models (Week 2), enhancing those models with advanced techniques and chose a final model (Week 3), and finally, building a web app from your enhanced final model (Week 4).

 

  • Assignments

    Practical assignments at the end of each weekly module to apply learning, culminating in a capstone project that encompasses all aspects of the training.

 

  • Personalized, Expert Feedback

    Receive direct feedback on your work from expert in AI with 20+ years of experience, Maryam Miradi, ensuring you understand not just the 'how' but also the 'why' behind data science solutions.

 

  • Practical, Project-Based Learning

     Dive deep into data science with real-world projects that challenge you to apply concepts in practical scenarios, from data cleaning to deploying AI models.

 

  • Interactive, Immersive Workshops

     Benefit from live workshops that go beyond the basics into advanced topics, providing you with the skills to tackle current and future challenges in AI.

 

  • Continuous Support and Community Access

    Ongoing support via a dedicated forum or group, where students can share insights, ask questions, and network with peers.
ENROL NOW

GET INSTANT ACCESS TO

AI Solutions Mastery

AI Practitioner Plus

 

Date:

1 June - 6 July, 2024

Make Use of Early Bird Prices 

Space is Limited to ensure Quality.

AI Practitioner Plus

€1979

(excl. 21% VAT)

  •  Unlimited Access to Pre-recorded Lessons (incl. updates)

  •  Access to Community for Support and Networking.

  • 2 x Live Q&A sessions with Maryam for deeper insights and real-time guidance.

  • Weekly assignments to reinforce Learning.

  • Certification of Completion, showcasing their achievements
  • 1 x real-world project that allows students to solve a real-world problem.
  • Personalized feedback on Assignments to to fine-tune skills.
  • Access to 60-page AI Toolkit Pro (€500 value)

  • 7-Days Free Cancellation (Up to 20 May)

ENROLL NOW

YOUR TRAINER

Maryam Miradi

With 20+ Years in AI development, a PhD in AI, 24 publications, 2 books, 5 awards, and experience coaching 200+ data scientists in 12 Industries, I’m here to share AI Solutions with you.

VIEW PROFILE

 Any Questions?

Reach Out to me on LinkedIn

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