Â
Â
Â
Hands-on Data Science Training +Â Mentorship
AI Solutions Mastery
Â
For Data Scientists, PhDs, Engineers, and Professionals
Move Beyond AI Theory
 &
Gain practical skills and confidence to land Data Science Jobs
Â
My 20+ Years in AI in one Course
 👤Instructor
 Dr. Maryam Miradi - AI Expert with 20+ Years in Hands-on AI Development
Join 200+ successful professionals who have been Trained by Maryam ↓
Â
Maryam's 20+ Years in AI
- PhD in AI - Machine Learning & Deep Learning
- Specialized in Computer Vision & NLP/LLMs
- Best European Researcher in Future VisionÂ
- Top Voice LinkedIn in ML and Data Science
- Chief AI Scientist of Profound Analytics
- 200+ Data Scientists are coached by me
- 20+ Years of Experience
- Best European Researcher in Future Vision
- International Projects, Awards, Publications & Books

Â
THIS IS FOR YOU
If you are:
👤 Professionals looking to upskill in AI and machine learning
👤 Data scientists / AI Engineers wanting to specialize in end-to-end AI solutions
👤 Engineers or developers who need hands-on experience with AI
👤 Individuals or organizations needing practical AI implementation skills
If you feel:
✔️ You've taken multiple courses, yet you still don’t feel job-ready for AI roles.
✔️ You have some experience in AI or Data Science, but real-world applications feel overwhelming.
✔️ Despite your efforts, you’re struggling to land high-impact roles in AI.

 Success Stories & Real Transformations

With my 20+ years of experience, I’ve helped over 200 professionals bridge the gap between theory and real-world AI applications.

YOUR PROGRAM
Â
 WEEK 1
  Start the Engine (Start of 1st End to End Project)
-
- đź“‹Â Get Started
- 🎥 Introduction to AI vs Machine Learning vs Deep Learning vs DS
- 🎥 Introduction to 10 Essential Steps of Data Science (Data Science Framework to 10X Your Performance)
- 🎥 Introduction to Business Understanding - Problem Understanding & Getting the Big Picture
- 🎥 Working With Real-World Data (HANDS-ON)
- 🎥 Data Understanding - Part 1: Setup and Data in Google Colab (HANDS-ON)
- 🎥 Data Understanding - Part 2: Collect and Describe Data (HANDS-ON)
- 🎥 Data Understanding - Part 3: Explore and Verify Data (HANDS-ON)
Â
 WEEK 2
  Start the Engine (Start of 1st End to End Project)
-
- đź“‹Â Assignment Week 1 - Apply Your Skills in Data Understanding & EDAÂ (HANDS-ON)
- ✍️Personal Feedback on Assignment
Â
 WEEK 3
  AI Solution Engineered
-
- 🎥 Introduction to Week 2 - Data Prep - Feature Engineering - Modelling
- 🎥 Why Data Preparation and Feature Engineering
- 🎥 Why Modelling? Which Models? Model Evaluation Method
- 🎥 ScikitLearn Library - the Golden Source  (HANDS-ON)
- 🎥 Data Preparation: Setup Unique IDs and Stratified Test Set  (HANDS-ON)
- 🎥 Data Preparation: Feature Transformation  (HANDS-ON)
- 🎥 Feature Engineering  (HANDS-ON)
- 🎥 Modelling  (HANDS-ON)
Â
 WEEK 4
  AI Solution Engineered
-
- 📋 Assignment Week 2: Apply Your Skills in Data Preparation, Feature Engineering, and Modeling  (HANDS-ON)
- ✍️Personal feedback on Assignment
- 💬 Q&A Live Session
Â
 WEEK 5
  AI Solution Advance
-
- 🎥 Modelling - Supervised Learning - Classification - Regression
- 🎥 Introduction to Classification Metrics
- 🎥 ML Algorithms - Naive Bayes - Logistic Regression
- 🎥 ML Algorithms - Tree - Ensemble - Gradient Descent - RandomForest - Gradient Boosting
- 🎥 Solutions to Imbalanced Data - Part I - SMOTE - ADASYN
- 🎥 Introduction to Deep Learning and Its Concepts
- 🎥 Solutions to Imbalanced Data - Part II - GANs and Oversampling with CTGANs
- 🎥 Hyperparameter Tuning
- 🎥 Hyperparameter Tuning Using Bayesian and Tree-structured Parzen Estimators
- 🎥 XGBoost Hyperparameters
- 🎥 Supervised Classification XGBoost Deep Learning Hyperparameter Tuning CTGANs (HANDS-ON)
Â
 WEEK 6
  AI Solution Advance
-
- đź“‹Â Assignment Week 3: Ensemble Classification - Deep Learning Oversampling with SMOTE, ADASYN, and CTGANs, and Hyperparameter Tuning
- ✍️Personal feedback on AssignmentÂ
Â
 WEEK 7
  AI Solution Ultimate
-
- 🎥 Introduction to ML Algorithms - Distance Similarity - KNN - Clustering - Anomaly Detection
- 🎥 Introduction to AI Explainability - Global vs Local Explainability - SHAP Values
- 🎥 Introduction to AI Fairness for Classification with Tabular Data - FairLearn Library
- 🎥 Final Model Selection - Deep Learning Ensemble Model Selection Hyperparameter Tuning (HANDS-ON)
- 🎥 Model Selection - Unsupervised Learning Anomaly Detection Dimensionality Reduction (HANDS-ON)
- 🎥 AI Explainability - Global vs Local Explainability - SHAP Values (HANDS-ON)
- 🎥 Hands-on AI Fairness with FairLearn (HANDS-ON)
- 📋 Instruction to Install Streamlit (HANDS-ON)
- 🎥 Workshop on Streamlit - Web Application Library for Deployment (HANDS-ON)
- 🎥 Complete Pipeline and Deployment with Streamlit (HANDS-ON)
Â
 WEEK 8
  AI Solution Ultimate
-
- 📋 Assignment Week 4 - Unsupervised Learning, Deep Learning, Explainability, Fairness, Pipeline and Deployment (HANDS-ON)
- ✍️Personal feedback on AssignmentÂ
-
💬 Q&A Live Session
Â
YOU MASTER:
Â
✔️Mastery of a 10-Step AI Framework:
Manage AI projects end-to-end.
Â
✔️ Advanced Feature Engineering:
Hands-on experience with feature scaling, encoding, extraction, and selection to maximize predictive accuracy.
Â
✔️Comprehensive Model Evaluation:Â
Expertise in comparing and selecting the best models by assessing performance metrics (e.g., accuracy, precision, recall, F1 score, AUC-ROC).
✔️Strong Foundations in Data Science and AI:
Understanding the distinctions between AI, Machine Learning, Deep Learning, and Data Science.
✔️Hands-On Practical Skills:
Real-world project implementation focused on data setup, feature engineering, and model evaluation using tools like Google Colab.
✔️Machine Learning and Deep Learning Expertise:
- Proficiency in supervised learning (classification and regression) and unsupervised learning (clustering, anomaly detection).
- Practical knowledge of popular ML algorithms: Naive Bayes, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and XGBoost.
- Mastery of deep learning concepts and hyperparameter tuning for neural networks.Â
Â
✔️ Handling Imbalanced Data:
Practical experience with imbalanced data techniques like SMOTE, ADASYN, and advanced methods like CTGANs for synthetic data generation.
Â
✔️Advanced Hyperparameter Tuning:
Applying cutting-edge optimization techniques, such as Bayesian Optimization and Tree-structured Parzen Estimators, to fine-tune models.
✔️AI Explainability:
Implementing explainability methods (e.g., SHAP values) to enhance transparency and trust in AI decisions.
Â
✔️AI Fairness:
Ensuring ethical AI by applying fairness frameworks (e.g., FairLearn library) to mitigate bias in AI models.
Â
✔️Deployment and Full AI Pipeline Development:
Building complete AI pipelines and deploying them using interactive tools like Streamlit to create functional and accessible AI applications.
Â
✔️Strategic Thinking, Critical Thinking and Problem-Solving in AI:
- Â Strengthened abilities to understand business problems, align AI solutions with organizational goals, and communicate AI insights effectively to non-technical stakeholders.
- A systematic approach to tackling complex business challenges using AI methodologies, ensuring participants can deliver impactful solutions.
Â
✔️Autonomy and Leadership in AI Projects:
Gaining confidence to lead AI initiatives, define objectives, execute models, and deploy solutions while ensuring explainability and fairness.
Watch Godwin's Journey: How He Landed a Top AI Job After Completing AI Solution Mastery
Â
Takeaways:
- Discover how Godwin broke into AI after facing rejection due to "lack of experience."
- Â Godwin shares how putting in the work and asking the right questions made all the difference.
- Learn how handling real, messy data during the course helped Godwin ace his job interview.
- Hear how the personal touch and weekly discussions propelled Godwin’s confidence and success.
Start Learning Immediately
No Waiting Required
Â
》Start the program at any Week you want
-
- Enjoy immediate access to lesson materials upon enrollment.
- Follow the Program in your Learning Environment
- Learn at your own pace each week
- Ask your questions directly to Maryam and get answer it same day
- Get Personalized Feedback and Mentorship on your Assignments sent directly to your e-mail
FAQ.
On average, how many hours per week are required to complete the course?
Is it mandatory to complete the 8 weeks consecutively?
Regarding the completion certificate, will it include a grade, or will it state that the course has been successfully completed?
For Personal Feedback on Assignments, will the feedback be provided through a video meeting, via email, or both?
Upon completing this course, will I possess the requisite skills to secure full-time or part-time employment?
Could you please clarify what is meant by "Private Community Access"?
What if I get stuck on something difficult?
Am I really capable of mastering AI and data science?
What If this training not work for me?
Â
AI Solutions Mastery
---with Mentorship---
Â
đź’Ą Limited-Time 50% Discount!đź’Ą
_____________________________________
✔️Mastery of a 10-Step AI Framework
✔️ Advanced Feature Engineering
✔️Comprehensive Model Evaluation
✔️Strong Foundations in Data Science and AI
✔️Hands-On Practical Skills
✔️Machine Learning and Deep Learning Expertise
✔️ Handling Imbalanced Data
✔️Advanced Hyperparameter Tuning
✔️AI Explainability
✔️AI Fairness
✔️Deployment and Full AI Pipeline Development
✔️Strategic Thinking, Critical Thinking and Problem-Solving in AIÂ
✔️Autonomy and Leadership in AI Projects
_____________________________________
- 🔑 37 Pre-Recorded Lessons
- ✍️Personalized Feedback
-
⌛ 8-Weeks Program
-
🎯 End to End Real-World Projects
- 👨‍💻Unlimited Access to FULL Python CODE
- 💬 Private Community Access
-  🗣️2 X Live Q&A (Optional)
- 📜 Completion Certificate
Â
Â
$5000Â Now: $2500
❊ Offer Ends Soon – Save $2500 Today!
Â
ENROLL TODAY