No Code AI & Machine Learning Specialization Course

Get started with the No Code AI and Machine Learning Specialization, delivered by Simplilearn and Purdue University Online. 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 Machine Learning Operations

• Introduction to No-Code AI & Machine Learning

• Advantages and Limitations of No-Code AI & Machine Learning

• 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

• 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 Extract, Transform, and Load Processes

• 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

• 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

• 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

• 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 Machine Learning

• Best Practices for Machine Learning Project Success

• Ethical Considerations in No-Code Machine Learning Deployment

Academic Masterclass by Purdue University

• Attend an online interactive masterclass and get insights about advancements in technology and techniques in no-code machine learning

Essentials of Generative AI, Prompt Engineering, & ChatGPT

• Learn generative AI, prompt engineering, and ChatGPT

• Acquire a real-world understanding of business applications

• Master the art of applying generative AI efficiently

• Grasp the significance of prompt engineering by producing tailored outputs