AI & Machine Learning

Get started with the No Code AI and Machine Learning Specialization, delivered jointly by Purdue University Online and Simplilearn. Kickstart your learning journey and explore the ability to build practical AI solutions using no-code tools.

• Overview of AI & Machine Learning, and Their Importance
• Machine Learning Life Cycle
• Machine Learning Challenges
• Introduction to MLOps
• Introduction to No-Code AI & Machine Learning
• Advantages and Limitations of No-Code AI & ML
• Popular No-Code AI Platforms
• Key Features of No-Code AI Platforms
• Working with Data in No-Code AI Platforms
• Building Models with No-Code Tools
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• Data Sources and Datasets
• Data Acquisition Techniques
• Assessing Data Completeness, Consistency, and Accuracy
• Automated Data Collection Tools
• Data Import and Preprocessing using No-Code Tools
• No-Code Tools for Data Transformation
• Data Visualization Techniques without Coding
• Data Cleaning Techniques using No-Code Platforms
• Feature Engineering without Coding
• Dimensionality Reduction
• Handling Categorical Data
• Balancing Imbalanced Datasets
• Advanced Imputation Techniques
• Advanced Outlier Detection and Treatment
• Data Warehousing and ETL Processes
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• Supervised Learning Algorithms
• Linear Regression and Polynomial Regression
• Using No-Code Tools for Linear Regression
• Logistic Regression and Classification Algorithms
• Decision Trees, Random Forests and K-Nearest Neighbors
• Building Classifiers using No-Code Tools
• Unsupervised Learning Algorithms
• Clustering Techniques
• No-Code Clustering Tools and Visualizations
• Dimensionality Reduction Techniques using No-Code Tools
• Anomaly Detection and Outlier Analysis
• Evaluation Metrics for Regression
• Evaluation Metrics for Classification
• No-Code Tools for Model Evaluation
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• Ensemble Learning Methods (e.g., Bagging, Boosting)
• Support Vector Machines (SVM)
• Introduction to Artificial Neural Networks
• Building Blocks and Learning Process of ANNs
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Attention Mechanism
• Building and Training Neural Network Model with No-Code Tools
• Text Analytics and Natural Language Processing (NLP)
• Text Processing, Representation, and Sentiment Analysis without Coding
• Building NLP Models using No-Code Platforms
• Vector Embeddings
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• Cross-Validation Techniques (K-fold, Stratified, etc.)
• Model Selection Strategies (Hyperparameter Tuning, Grid Search, Randomized Search, etc.)
• Bias-Variance Tradeoff and Overfitting/Underfitting
• Feature Selection Techniques without Programming
• Model Interpretability and Explainability
• Interpreting Model Outputs and Insights
• Deploying Models without Coding
• Integration with Web and Mobile Applications using No-Code Platforms
• Model Monitoring and Management
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• Applications of No-Code Machine Learning in Various Industries
• Case Studies in Finance (Fraud Detection, Credit Scoring)
• Case Studies in Healthcare (Diagnosis, Treatment Recommendations)
• Case Studies in Marketing (Customer Churn Prediction, Targeted Advertising)
• Case Studies on Predictive Analytics
• Case Studies on Image Recognition
• Common Challenges of No-Code ML
• Best Practices for ML Project Success
• Ethical Considerations in No-Code ML Deployment
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