AI Solutions Mastery By Maryam Miradi – Free Download Course With Mentorship
Gain Practical Skills and Confidence to Land Data Science Jobs For Data Scientists, PhDs, Engineers, and Professionals!
✅ About This Course:
✅ Course Author: Maryam Miradi
✅ Official Course Price: $2500
✅ Free For Our VIP Members? : Yes
✅ Download Links : Mega & Google Drive
✅ Course Size : 15.03 GB
✅ Updatable? : Yes, all future updates included.
✅ Sales Page : You can check at the bottom of this page.
🏆 Here’s What You Get & Learn With This Course:
WEEK 1
Start the Engine (Start of 1st End to End Project)
📋 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)
WEEK 2
Start the Engine (Start of 1st End to End Project)
📋 Assignment Week 1 – Apply Your Skills in Data Understanding & EDA (HANDS-ON)
✍️Personal Feedback on Assignment
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)
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
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)
WEEK 6
AI Solution Advance
📋 Assignment Week 3: Ensemble Classification – Deep Learning Oversampling with SMOTE, ADASYN, and CTGANs, and Hyperparameter Tuning
✍️Personal feedback on Assignment
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)
WEEK 8
AI Solution Ultimate
📋 Assignment Week 4 – Unsupervised Learning, Deep Learning, Explainability, Fairness, Pipeline and Deployment (HANDS-ON)
✍️Personal feedback on Assignment
💬 Q&A Live Session
YOU’LL MASTER:
✔️Mastery of a 10-Step AI Framework:
Manage AI projects end-to-end.
✔️ Advanced Feature Engineering:
Hands-on experience with feature scaling, encoding, extraction, and selection to maximize predictive accuracy.
✔️Comprehensive Model Evaluation:
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.
✔️ Handling Imbalanced Data:
Practical experience with imbalanced data techniques like SMOTE, ADASYN, and advanced methods like CTGANs for synthetic data generation.
✔️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.
✔️AI Fairness:
Ensuring ethical AI by applying fairness frameworks (e.g., FairLearn library) to mitigate bias in AI models.
✔️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.
✔️Strategic Thinking, Critical Thinking and Problem-Solving in AI:
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.
✔️Autonomy and Leadership in AI Projects:
Gaining confidence to lead AI initiatives, define objectives, execute models, and deploy solutions while ensuring explainability and fairness.
✔️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 AI
✔️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
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