CertNexus Certified AI Practitioner Certification Training

Topic A: Identify AI and ML Solutions for Business Problems

• Comprehending The Hierarchy Of Data
• A Guide To Effective Big Data Management Strategies For Deriving Insights Via Data Mining Practical Implementations Of AI And ML Selection Criteria For Appropriate Business Solutions
• Addressing Business Obstacles With Machine Learning And AI Solutions

Topic B: Follow a Machine Learning Workflow

• Comprehending Models of Machine Learning
• Essential Machine Learning Workflow Proficiency Sets in the Field of Data Science
• Integrating Conventional IT Abilities with ML Workflow
• Concept Drift Management and Transfer Learning
• Practical Directives for Implementing the ML Workflow
• Strategizing the Execution of the ML Workflow

Topic C: Formulate a Machine Learning Problem

• Objective Formulation for Machine Learning
• Problem Framing for ML Solutions
• A Comparison of Conventional Programming and ML Methods
• Comparing Unsupervised and Supervised Learning
• Randomness and Uncertainty Management
• Predicting the Outcomes of Machine Learning
• Precautions Regarding Problem Formulation and Objective Establishment

Topic D: Select Appropriate Tools

• Comparing Proprietary and Open-Source AI Tools
• Investigating Emerging Technologies and Tools
• Comprehension of Hardware Requirements, Comparing GPUs and CPUs
• The Application of Cloud Platforms to ML Projects
• Configuration Best Practices for ML Toolsets
• Installation Instructions for Anaconda Criteria for the Selection of Machine Learning Toolsets

Topic A: Collect the Dataset

• A Comprehension of Machine Learning Datasets
• An Investigation of Data Structures and Terminology
• The Resolution of Data Quality Issues
• Determining Open Datasets and Data Sources
• Determining the Appropriate Dataset
• Assessing the Structure of the Dataset and Executing ETL Operations
• Integrating Software Environments and Machine Learning Pipelines
• Recommended Approaches for Loading Datasets

Topic B: Analyze the Dataset to Gain Insights

• Examination of Dataset Structures
• Guidance for Exploratory Data Analysis
• Gaining Insight into Distribution Patterns
• Descriptive statistical analysis in practice
• An Analysis of Central Tendency and Variability Metrics
• Effective Management Moments, skewness, and kurtosis
• Evaluating Correlational Associations
• Criteria for the Analysis of Statistical Datasets

Topic C: Use Visualizations to Analyze Data

• Implementing Visualizations for Data Exploration, Including Histograms, Box Plots, and Scatterplots, and Leveraging Geographic and Heat Maps Best Practices for Visual Data Analysis

Topic D: Prepare Data

• Comprehending Data Types and Handling Operations In contrast, discrete variables are continuous.
• Data Encoding and Dimensionality Reduction Implementation
• Attending to Duplicates and Missing Values
• Guidelines for Data Preparation, Including Dataset Splitting for Training and Testing and Normalizing and Standardizing Data Summary Techniques alongside the Holdout Method.

Topic A: Setting Up a Machine Learning Model

• Experiment and Hypothesis Definitions
• A Comprehension of Hypothesis Testing and Methodologies
• P-values and confidence intervals are investigated.
• Assessing and Choosing Appropriate Machine Learning Algorithms
• Developing Standards for Model Setup

Topic B: Train the Model

• Strategies for Iterative Model Tuning that Mitigate Bias and Attain Generalization
• Cross-validation methods Implemented, Including k-Fold and Leave-p-Out, Outlier Management, and Feature Transformation
• Acquiring Knowledge of the Bias-Variance Tradeoff
• Regularization and Model Parameter Optimization
• Exploring the Potential of Combined Models to Improve Performance
• Optimizing the Efficiency of Processing
• Instructions for Model Training, Tuning, and Testing
   

Topic A: Translate Results into Business Actions

• Comprehending the Audience and Their Requirements
• Efficient Data Visualization for Presentation Adhering to Findings Presentation Guidelines
• The process of converting analytical findings into practical business strategies

Topic B: Incorporate a Model into a Long-term Business Solution

• Model Deployment in the Production Environment
• Pipeline Automation and Production Algorithm Implementation
• Scalability, maintenance, and testing assurance
• Aspects of Consumer-Focused Applications
• Principles Governing the Extended Integration of Machine Learning Solutions Into Organizational Activities

Topic A: Build a Regression Model Using Linear Algebra

• An Overview of Linear Regression and Its Fundamentals
• Investigating the Representation of Data and Linear Equations
• Achieving Linear Fits to Data and Acknowledging Limitations
• Implementing Linear Regression in Contexts of Machine Learning
• The application of matrices to linear regression
• The process of applying the Normal Equation to optimize a model
• Utilizing Higher Order Fits and Multiple Parameters to Extend Linear Models
• Assessment of Model Performance Utilizing Cost Functions Such as MSE and MAE
• Model Quality Evaluation Using the Coefficient of Determination
• Examining the Constraints of the Normal Equation
• Instructions for Using Linear Algebra to Construct Linear Regression Models

Topic B: Build a Regularized Regression Model Using Linear Algebra

• Implementing Ridge, Lasso, and Elastic Net Regression Best Practices for Constructing Regularized Linear Regression Models: An Introduction to Regularization Techniques

Topic C: Build an Iterative Linear Regression Model

• Investigating Iterative Model Construction Methods
• Comprehending the Objectives of Gradient Descent Optimization
• Difficulties in Navigating Global Minimum versus Local Minimum
• Using a Variety of Gradient Descent Methods to Adjust Learning Rates for Optimal Gradient Descent Performance
• Instructions for the Development of Iterative Linear Regression Models

Topic A: Train Binary Classification Models

• An Aware of the Constraints of Linear Regression in Classification Comprehension of Decision Boundaries and Logistic Regression
• Cost Function Implementation for Logistic Regression
• Investigating k-NN (k-Nearest Neighbor) as a Method of Classification
• Optimal Value Determination for k in k-NN
• A Comparison of the k-NN and Logistic Regression Methods and Instructions for Training Binary Classification Models

Topic B: Train Multi-Class Classification Models

• An Analysis of Multinomial Logistic Regression for Multi-Class Problems: A Comprehension of Multi-Label and Multi-Class Classification
• Instructions for Training Classification Models with Multiple Classes

Topic C: Evaluate Classification Models

• Guidelines for Evaluating Classification Models: Assessing Model Performance Metrics Such as Accuracy, Precision, and Recall Utilizing Confusion Matrix for Comprehensive Performance Evaluation Comprehending the Tradeoff Between Precision and Recall and F1 Score Analyzing Performance Curves Such as ROC and PRC

Topic D: Tune Classification Models

• The application of hyperparameter optimization methods
• An Investigation into Genetic Algorithms, Randomized Search, Grid Search, and Bayesian Optimization
• Tuning Hyperparameter Guidelines for Classification Models

Topic A: Build k-Means Clustering Models

• Comprehension of the k-means clustering algorithm
• Differentiating Between Local and Global Optimization Methods
• Ascertaining the Optimal Cluster Count (k)
• Determining k by Employing Elbow Point and Cluster Sum of Squares
• Implementing Silhouette Analysis to Assess Clusters
• Investigating Supplementary Cluster Analysis Techniques
• Construction Guidelines for k-Means Clustering Models

Topic B: Build Hierarchical Clustering Models

• An Examination of the Constraints of K-means Clustering
• An Examination of the Hierarchical Clustering Algorithm in Practice Utilizing Hierarchical Clustering on Diverse Data Structures, Including Spiral Datasets
• Stopping Criteria Determination for Hierarchical Clustering
• Building Hierarchical Clustering Models: Guidelines for Dendrogram Analysis for Cluster Interpretation

Topic A: Build Decision Tree Models

• An Overview of Decision Trees and Their Architecture
• Investigating the CART (Classification and Regression Tree)
• Implementing the Gini Index to Split Decision Trees
• CART hyperparameter adjustment for model optimization
• The application of pruning techniques to simplify decision trees
• Investigating the C4.5 Algorithm and Discretization of Continuous Variables
• One-Hot Encoding and Determining Bins: A Comparison of Decision Trees and Other Algorithms
• Instructions for the Construction of Decision Tree Models

Topic B:Build Random Forest Models

• A Comprehension of Ensemble Learning and Its Advantages
• Investigating the Random Forest and Out-of-Bag Error Algorithms
• Optimizing Random Forest Performance through Feature Selection
• Optimization of Random Forest Hyperparameters Construction Guidelines for Random Forest Models

Topic A: Build SVM Models for Classification

• An Analysis of Support-Vector Machines (SVMs) and Their Practical Implementations
• SVM implementation for linear classification, including the implementation of hard-margin and soft-margin strategies
• A Kernel Trick Extending SVMs for Non-Linear Classification
• Investigation of Kernel Methods and Their Implementation in SVMs
• Building Guidelines for Effective SVM Classification Models

Topic B: Build SVM Models for Regression

• Guidelines for Constructing Support Vector Machine (SVM) Models for Regression Problems
• Methods for Implementing SVM Regression Models in Practice

Topic A: Build Multi-Layer Perceptrons (MLP)

• A comprehension of perceptrons and artificial neural networks (ANNs)
• Perceptron Training for Multi-Label Classification
• Recognizing the Limitations of Perceptrons
• Examining the Architecture of Multi-Layer Perceptrons (MLP)
• Setting up ANN Layers and Backpropagation Implementation
• Application and Selection of Activation Functions
• Construction Guidelines for Multi-Layer Perceptrons (MLPs)

Topic B: Build Convolutional Neural Networks (CNN)

• Exposing the Limitations of Conventional ANNs through the Introduction of Convolutional Neural Networks (CNNs) and Their Constituents
• Gaining Insight into CNN Filters and Padding Methods
• Implementing Pooling and Stride Layers to Extract Features
• Investigating CNN Applications and Architecture
• Generative Adversarial Networks (GANs): An Overview
• Directives for the Construction of Convolutional Neural Networks

Topic A: Protect Data Privacy

• A Comprehend of Personal Identifiable Information (PII) and Protected Data Compliance with Applicable Data Privacy Laws and Regulations
• Privacy by Design Principles Implemented in AI and ML Systems
• Addressing Data Privacy Obstacles in Applications of Machine Learning
• Standards and Procedures for Ensuring Compliance with Data Privacy Laws
• Privacy and Open-Source Data Sharing Considerations
• Methods of Data Anonymization and Implementation Guidelines
• Confronting the Obstacles Presented by Big Data in the Continuation of Privacy Best Practices for Safeguarding Data Privacy

Topic B: Promote Ethical Practices

• Confronting Obstacles Linked to the "Black Box" Characteristics of AI Systems by Identifying and Ceasing Preconceived Notions and Bias
• Addressing Prejudice Bias and Social Discrimination Proxies
• Concerns Regarding Ethics in Natural Language Processing (NLP)
• Principles to Advance Ethical Behavior in the Development and Implementation of Artificial Intelligence

Topic C: Establish Data Privacy and Ethical Policies

• Formulating Data Governance and Privacy Policies for AI and ML Initiatives
• Humanitarian Principles and Intellectual Property Rights: An Examination
• Principles Governing the Development of All-Inclusive Policies Regarding Data Privacy and Ethics
• Appendix A: Aligning Course Content with CertNexus® Certified Artificial Intelligence (AI) Practitioner (Exam AIP-100) Syllabus