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AI Solutions Mastery

AI Practitioner Plus

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Transition into a

Skilled End-to-End Data Scientist

(in 5 Weeks )

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By Following a 10-Step Roadmap and Engaging in Comprehensive Hands-On Projects,

Including an Additional End-to-End Project to Solve Real-World Problems and Deploy Advanced AI Web Applications

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Training is Suitable for you if:Ā 

Individuals:

  • šŸ‘¤Industry Experts:Ā You are an industry expert whose company wants to train in ML and AI to leverage your domain knowledge with advanced data science skills.

  • šŸ‘¤Ā Professionals and PhD Graduates:Ā You are a professional (e.g., Finance Expert, IT Specialist) or a recent PhD graduate aiming to move into a data science role

  • šŸ‘¤Ā Data Scientists:Ā You are a data scientist with limited real-life project experience seeking to apply best practices.

Companies:

  • šŸ‘¤Ā Upskilling Internal Talent:Ā You are a company looking to upskill your internal talent in machine learning and AI to avoid the lengthy process of onboarding external data scientists who need to learn your business.

Possible Start Dates in 2024:Ā 
  • šŸ“†Ā  Fall:Ā  1 October | 1 NovemberĀ | 1 DecemberĀ 
Reschedule :
  • šŸ”„Ā You can Reschedule to Any Month That Suits YouĀ 
Immediate Access:Ā 
  • āš”Ā Get Immediate Access to Lessons Upon Registration
Certificate:Ā 
  • šŸ…Ā YesĀ 
Duration:
  • āŒ›5-Weeks
  • šŸ•’Total 14 hours Lessons

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Price (excl. VAT):Ā Ā 
ā‚¬1979

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ENROLL NOW

YOU WILLĀ LEARN

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  • Business Understanding:Ā Analyze real-world problems.
  • Data Understanding & EDA:Ā Perform initial data cleaning and exploratory data analysis using Pandas, Matplotlib, Seaborn, and NetworkX.
  • Data Preparation:Ā Prepare data for modeling.
  • Feature Engineering:Ā Implement advanced feature engineering techniques.
  • Modelling:Ā Build AI models (Supervised Learning: Build 9 different models including XGBoost; Deep Learning: TensorFlow, Keras; Unsupervised Learning: Anomaly Detection - Isolation Forest, AutoEncoders; Clustering - K-means, DBSCAN, Hierarchical Clustering).
  • Model Fine-Tuning:Ā Enhance models with hyperparameter tuning using Hyperopt.
  • Evaluation / Comparison:Ā Compare multiple models using different classification metrics such as AUCPR to select the best performer.
  • AI Fairness:Ā Explore different types of parity to ensure fairness in AI models (FairLearn).
  • AI Explainability:Ā Understand model decisions (SHAP).
  • Deployment:Ā Develop and deploy models using web applications (Streamlit).

YOUR PROGRAM

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Ā  WEEK 1

Ā  Ā Start the Engine (Start of 1st End to End Project)
    1. šŸ“‹Ā Get Started
    2. šŸŽ„Ā Introduction to AI vs Machine Learning vs Deep Learning vs DS
    3. šŸŽ„Ā Introduction to 10 Essential Steps of Data Science (Data Science Framework to 10X Your Performance)
    4. šŸŽ„Ā Introduction to Business Understanding - Problem Understanding & Getting the Big Picture
    5. šŸŽ„Ā Working With Real-World Data (HANDS-ON)
    6. šŸŽ„Ā Data Understanding - Part 1: Setup and Data in Google ColabĀ (HANDS-ON)
    7. šŸŽ„Ā Data Understanding - Part 2: Collect and Describe DataĀ (HANDS-ON)
    8. šŸŽ„Ā Data Understanding - Part 3: Explore and Verify DataĀ (HANDS-ON)
    9. šŸ“‹Ā Assignment Week 1 - Apply Your Skills in Data Understanding & EDAĀ (HANDS-ON)
    10. āœļø Personalized feedback on Weekly Assignments

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Ā  WEEK 2

Ā  Ā AI SolutionĀ Engineered
    1. šŸŽ„Ā Introduction to Week 2 - Data Prep - Feature Engineering - Modelling
    2. šŸŽ„Ā Why Data Preparation and Feature Engineering
    3. šŸŽ„Ā Why Modelling? Which Models? Model Evaluation Method
    4. šŸŽ„Ā ScikitLearn Library - the Golden SourceĀ Ā (HANDS-ON)
    5. šŸŽ„Ā Data Preparation: Setup Unique IDs and Stratified Test SetĀ Ā (HANDS-ON)
    6. šŸŽ„Ā Data Preparation: Feature TransformationĀ Ā (HANDS-ON)
    7. šŸŽ„Ā Feature EngineeringĀ Ā (HANDS-ON)
    8. šŸŽ„Ā ModellingĀ Ā (HANDS-ON)
    9. šŸ“‹Ā Assignment Week 2: Apply Your Skills in Data Preparation, Feature Engineering, and ModelingĀ Ā (HANDS-ON)
    10. šŸ’¬Ā Q&A Live Session
    11. āœļø Personalized feedback on Weekly Assignments

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Ā  WEEK 3

Ā  Ā AI SolutionĀ Advance
    1. šŸŽ„Ā Modelling - Supervised Learning - Classification - Regression
    2. šŸŽ„Ā Introduction to Classification Metrics
    3. šŸŽ„Ā ML Algorithms - Naive Bayes - Logistic Regression
    4. šŸŽ„Ā ML Algorithms - Tree - Ensemble - Gradient Descent - RandomForest - Gradient Boosting
    5. šŸŽ„Ā Solutions to Imbalanced Data - Part I - SMOTE - ADASYN
    6. šŸŽ„Ā Introduction to Deep Learning and Its Concepts
    7. šŸŽ„Ā Solutions to Imbalanced Data - Part II - GANs and Oversampling with CTGANs
    8. šŸŽ„Ā Hyperparameter Tuning
    9. šŸŽ„Ā Hyperparameter Tuning Using Bayesian and Tree-structured Parzen Estimators
    10. šŸŽ„Ā XGBoost Hyperparameters
    11. šŸŽ„Ā Supervised Classification XGBoost Deep Learning Hyperparameter Tuning CTGANsĀ (HANDS-ON)
    12. šŸ“‹Ā Assignment Week 3: Ensemble Classification - Deep Learning Oversampling with SMOTE, ADASYN, and CTGANs, and Hyperparameter Tuning
    13. āœļø Personalized feedback on Weekly Assignments

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Ā  WEEK 4

Ā  Ā AI SolutionĀ Ultimate
    1. šŸŽ„Ā Introduction to ML Algorithms - Distance Similarity - KNN - Clustering - Anomaly Detection
    2. šŸŽ„Ā Introduction to AI Explainability - Global vs Local Explainability - SHAP Values
    3. šŸŽ„Ā Introduction to AI Fairness for Classification with Tabular Data - FairLearn Library
    4. šŸŽ„Ā Final Model Selection - Deep Learning Ensemble Model Selection Hyperparameter TuningĀ (HANDS-ON)
    5. šŸŽ„Ā Model Selection - Unsupervised Learning Anomaly Detection Dimensionality ReductionĀ (HANDS-ON)
    6. šŸŽ„Ā AI Explainability - Global vs Local Explainability - SHAP ValuesĀ (HANDS-ON)
    7. šŸŽ„Ā Hands-on AI Fairness with FairLearnĀ (HANDS-ON)
    8. šŸ“‹Ā Instruction to Install StreamlitĀ (HANDS-ON)
    9. šŸŽ„Ā Workshop on Streamlit - Web Application Library for DeploymentĀ (HANDS-ON)
    10. šŸŽ„Ā Complete Pipeline and Deployment with StreamlitĀ (HANDS-ON)
    11. šŸ“‹Ā Assignment Week 4 - Unsupervised Learning, Deep Learning, Explainability, Fairness, Pipeline and DeploymentĀ (HANDS-ON)

    12. šŸ’¬Ā Q&A Live Session

    13. āœļø Personalized feedback on Weekly Assignments

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Ā  WEEK 5

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Ā  Ā AI SolutionĀ Plus (2ndĀ End-to-End AIĀ Project)
  • Overview: Apply the skillsĀ 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.
  • āœļø Personalized feedback on End-to-End Project
ENROLL NOW

TRAINING ISĀ FOR YOUĀ IF:

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āœ” You have some basic experience with Python.

āœ”Ā Your Profile is any of the following:

  • šŸ‘¤ You are anĀ Industry ExpertĀ whose company wantsĀ to train in ML and AIĀ to leverage your domain knowledge with advanced data science skills.
  • šŸ‘¤ You are aĀ Professional (e.g., Finance Expert, IT Specialist)Ā or a recentĀ PhD graduateĀ aiming to move into a data science role.
  • šŸ‘¤ You are aĀ Data Scientist with Limited Real-life Project ExperienceĀ seeking to apply best practices.

āœ”Ā You want to get certified in data science.

āœ” You want to learn from an AI expert with over 20 years of real-world experience.

āœ”Ā You want to gain the skills to successfully apply data science in any field.

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TRAININGĀ ISĀ NOT FORĀ YOUĀ IF:

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āœ˜ Youā€™ve never worked with Python.

āœ˜ You are already an experienced data scientist.

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ENROLL NOW

YOUR TRAINER

Dr. Maryam Miradi

WithĀ 20+ YearsĀ in AI development, a PhD in AI, 24 publications, 2 books, 5 awards, and experience coaching 200+ data scientists in 12 Industries,Ā Iā€™m here to share AI Solutions with you.

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