TOLL FREE No : 1800-103-4583|customer_relations@qaiglobal.com
Menu

CertNexus Certified Artificial Intelligence (AI) Practitioner

Register Now

Go to Training Calendar
Request In-house Training
Become a Trainer

Lesson 1: Solving Business Problems Using AI and ML

Topic A: Identify AI and ML Solutions for Business Problems

  • The Data Hierarchy—Making Data Useful
  • Big Data
  • Guidelines for Working with Big Data
  • Data Mining
  • Examples of Applied AI and ML in Business
  • Guidelines to Select Appropriate Business Applications for AI and ML
  • Identifying Appropriate Business Applications for AI and ML

Topic B: Follow a Machine Learning Workflow

  • Machine Learning Model
  • Machine Learning Workflow
  • Data Science Skillset
  • Traditional IT Skillsets
  • Concept Drift
  • Transfer Learning
  • Guidelines for Following the Machine Learning Workflow
  • Planning the Machine Learning Workflow

Topic C: Formulate a Machine Learning Problem

  • Problem Formulation
  • Framing a Machine Learning Problem
  • Differences Between Traditional Programming and Machine Learning
  • Differences Between Supervised and Unsupervised Learning
  • Randomness in Machine Learning
  • Uncertainty
  • Random Number Generation
  • Machine Learning Outcomes
  • Guidelines for Formulating a Machine Learning Outcome
  • Selecting a Machine Learning Outcome

Topic D: Select Appropriate Tools

  • Open Source AI Tools
  • Proprietary AI Tools
  • New Tools and Technologies
  • Hardware Requirements
  • GPUs vs. CPUs
  • GPU Platforms
  • Cloud Platforms
  • Guidelines for Configuring a Machine Learning Toolset
  • How to Install Anaconda
  • Selecting a Machine Learning Toolset

Lesson 2: Collecting and Refining the Dataset             

Topic A: Collect the Dataset – Machine Learning Datasets

  • Structure of Data
  • Terms Describing Portions of Data
  • Data Quality Issues
  • Data Sources
  • Open Datasets
  • Guidelines for Selecting a Machine Learning Dataset
  • Examining the Structure of a Machine Learning Dataset
  • Extract, Transform, and Load (ETL)
  • Machine Learning Pipeline
  • ML Software Environments
  • Guidelines for Loading a Dataset
  • Loading the Dataset

Topic B: Analyze the Dataset to Gain Insights

  • Dataset Structure
  • Guidelines for Exploring the Structure of a Dataset
  • Exploring the General Structure of the Dataset
  • Normal Distribution
  • Non-Normal Distributions
  • Descriptive Statistical Analysis
  • Central Tendency
  • When to Use Different Measures of Central Tendency
  • Variability
  • Range Measures
  • Variance and Standard Deviation
  • Calculation of Variance
  • Variance in a Sample Set
  • Calculation of Standard Deviation
  • Skewness
  • Calculation of Skewness Measures
  • Kurtosis
  • Calculation of Kurtosis
  • Statistical Moments
  • Correlation Coefficient
  • Calculation of Pearson’s Correlation Coefficient
  • Guidelines for Analyzing a Dataset
  • Analyzing a Dataset Using Statistical Measures

Topic C: Use Visualizations to Analyze Data

  • Visualizations
  • Histogram
  • Box Plot
  • Scatterplot
  • Geographical Maps
  • Heat Maps
  • Guidelines for Using Visualizations to Analyze Data
  • Analyzing a Dataset Using Visualizations

Topic D: Prepare Data

  • Data Preparation
  • Data Types
  • Operations You Can Perform on Different Types of Data
  • Continuous vs. Discrete Variables
  • Data Encoding
  • Dimensionality Reduction
  • Impute Missing Values
  • Duplicates
  • Normalization and Standardization
  • Summarization
  • Holdout Method
  • Guidelines for Preparing Training and Testing Data
  • Splitting the Training and Testing Datasets and Labels

Lesson 3: Setting Up and Training a Model

Topic A: Set Up a Machine Learning Model

  • Design of Experiments
  • Hypothesis
  • Hypothesis Testing
  • Hypothesis Testing Methods
  • p-value
  • Confidence Interval
  • Machine Learning Algorithms
  • Algorithm Selection
  • Guidelines for Setting Up a Machine Learning Model
  • Setting Up a Machine Learning Model

 Topic B: Train the Model

  • Iterative Tuning
  • Bias
  • Compromises
  • Model Generalization
  • Cross-Validation
  • k-Fold Cross-Validation
  • Leave-p-Out Cross-Validation
  • Dealing with Outliers
  • Feature Transformation
  • Transformation Functions
  • Scaling and Normalizing Features
  • The Bias–Variance Tradeoff
  • Parameters
  • Regularization
  • Models in Combination
  • Processing Efficiency
  • Guidelines for Training and Tuning the Model
  • Refitting and Testing the Model

Lesson 4: Finalizing a Model

Topic A: Translate Results into Business Actions

  • Know Your Audience
  • Visualization for Presentation
  • Guidelines for Presenting Your Findings
  • Translating Results into Business Actions

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

  • Put a Model into Production
  • Production Algorithms
  • Pipeline Automation
  • Testing and Maintenance
  • Consumer-Oriented Applications
  • Guidelines for Incorporating Machine Learning into a Long-Term Solution
  • Incorporating a Model into a Long-Term Solution

Lesson 5: Building Linear Regression Models                    

 Topic A: Build a Regression Model Using Linear Algebra

  • Linear Regression
  • Linear Equation
  • Linear Equation Data Example
  • Straight Line Fit to Example Data
  • Linear Equation Shortcomings
  • Linear Regression in Machine Learning
  • Linear Regression in Machine Learning Example
  • Matrices in Linear Regression
  • Normal Equation
  • Linear Model with Higher Order Fits
  • Linear Model with Multiple Parameters
  • Cost Function
  • Mean Squared Error (MSE)
  • Mean Absolute Error (MAE)
  • Coefficient of Determination
  • Normal Equation Shortcomings
  • Guidelines for Building a Regression Model Using Linear Algebra
  • Building a Regression Model Using Linear Algebra

Topic B: Build a Regularized Regression Model Using Linear Algebra

  • Regularization Techniques
  • Ridge Regression
  • Lasso Regression
  • Elastic Net Regression
  • Guidelines for Building a Regularized Linear Regression Model
  • Building a Regularized Linear Regression Model

  Topic C: Build an Iterative Linear Regression Model

  • Iterative Models.
  • Gradient Descent.
  • Global Minimum vs. Local Minima.
  • Learning Rate.
  • Gradient Descent Techniques.
  • Guidelines for Building an Iterative Linear Regression Model.
  • Building an Iterative Linear Regression Model.

Lesson 6: Building Classification Models

Topic A: Train Binary Classification Models

  • Linear Regression Shortcomings
  • Logistic Regression
  • Decision Boundary
  • Cost Function for Logistic Regression
  • A Simpler Alternative for Classification
  • k-Nearest Neighbor (k-NN)
  • k Determination
  • Logistic Regression vs. k-NN
  • Guidelines for Training Binary Classification Models
  • Training Binary Classification Model

Topic B: Train Multi-Class Classification Models

  • Multi-Label Classification
  • Multi-Class Classification
  • Multinomial Logistic Regression
  • Guidelines for Training Multi-Class Classification Models
  • Training a Multi-Class Classification Model

Topic C: Evaluate Classification Models

  • Model Performance
  • Confusion Matrix
  • Classifier Performance Measurement
  • Accuracy
  • Precision
  • Recall
  • Precision–Recall Tradeoff
  • F1 Score
  • Receiver Operating Characteristic (ROC) Curve
  • Thresholds
  • Area Under Curve (AUC)
  • Precision–Recall Curve (PRC)
  • Guidelines for Evaluating Classification Models
  • Evaluating a Classification Model

 Topic D: Tune Classification Models

  • Hyperparameter Optimization
  • Grid Search
  • Randomized Search
  • Bayesian Optimization
  • Genetic Algorithms
  • Guidelines for Tuning Classification Models
  • Tuning a Classification Model

Lesson 7: Building Clustering Models           

Topic A: Build k-Means Clustering Models

  • k-Means Clustering
  • Global vs. Local Optimization
  • k Determination
  • Elbow Point
  • Cluster Sum of Squares
  • Silhouette Analysis
  • Additional Cluster Analysis Methods
  • Guidelines for Building a k-Means Clustering Model
  • Building a k-Means Clustering Model

Topic B: Build Hierarchical Clustering Models

  • k-Means Clustering Shortcomings
  • Hierarchical Clustering
  • Hierarchical Clustering Applied to a Spiral Dataset
  • When to Stop Hierarchical Clustering
  • Dendrogram
  • Guidelines for Building a Hierarchical Clustering Model
  • Building a Hierarchical Clustering Model

Lesson 8: Building Advanced Models            

Topic A: Build Decision Tree Models

  • Decision Tree
  • Classification and Regression Tree (CART)
  • Gini Index Example
  • CART Hyperparameters
  • Pruning
  • C4.5
  • Continuous Variable Discretization
  • Bin Determination
  • One-Hot Encoding
  • Decision Tree Algorithm Comparison
  • Decision Trees Compared to Other Algorithms
  • Guidelines for Building a Decision Tree Model
  • Building a Decision Tree Model

Topic B: Build Random Forest Models

  • Ensemble Learning
  • Random Forest
  • Out-of-Bag Error
  • Random Forest Hyperparameters
  • Feature Selection Benefits
  • Guidelines for Building a Random Forest Model
  • Building a Random Forest Model

Lesson 9: Building Support-Vector Machines

Topic A: Build SVM Models for Classification

  • Support-Vector Machines (SVMs)
  • SVMs for Linear Classification
  • Hard-Margin Classification
  • Soft-Margin Classification
  • SVMs for Non-Linear Classification
  • Kernel Trick
  • Kernel Trick Example
  • Kernel Methods
  • Guidelines for Building an SVM Model
  • Building an SVM Model

Topic B: Build SVM Models for Regression

  • SVMs for Regression
  • Guidelines for Building SVM Models for Regression
  • Building an SVM Model for Regression

Lesson 10: Building Artificial Neural Networks

Topic A: Build Multi-Layer Perceptrons (MLP)

  • Artificial Neural Network (ANN)
  • Perceptron
  • Multi-Label Classification Perceptron
  • Perceptron Training
  • Perceptron Shortcomings
  • Multi-Layer Perceptron (MLP)
  • ANN Layers
  • Backpropagation
  • Activation Functions
  • Guidelines for Building MLPs
  • Building an MLP

Topic B: Build Convolutional Neural Networks (CNN)

  • Traditional ANN Shortcomings
  • Convolutional Neural Network (CNN)
  • CNN Filters
  • CNN Filter Example
  • Padding
  • Stride
  • Pooling Layer
  • CNN Architecture
  • Generative Adversarial Network (GAN)
  • GAN Architecture
  • Guidelines for Building CNNs
  • Building a CNN

Lesson 11: Promoting Data Privacy and Ethical Practices

Topic A: Protect Data Privacy

  • Protected Data
  • Obligation to Protect PII
  • Relevant Data Privacy Laws
  • Privacy by Design
  • Data Privacy Principles at Odds with Machine Learning
  • Guidelines for Complying with Data Privacy Laws and Standards
  • Complying with Applicable Laws and Standards
  • Open Source Data Sharing and Privacy
  • Data Anonymization
  • Guidelines for Data Anonymization
  • The Big Data Challenge
  • Guidelines for Protecting Data Privacy
  • Protecting Data Privacy

Topic B: Promote Ethical Practices

  • Preconceived Notions
  • The Black Box Challenge
  • Prejudice Bias
  • Proxies for Larger Social Discriminations
  • Ethics in NLP
  • Guidelines for Promoting Ethical Practices
  • Promoting Ethical Practices

 Topic C: Establish Data Privacy and Ethics Policies

  • Privacy and Data Governance for AI and ML
  • Intellectual Property
  • Humanitarian Principles
  • Guidelines for Establishing Policies Covering Data Privacy and Ethics
  • Establishing Policies Covering Data Privacy and Ethics
Get 10% discount on a group of 4 or more nominations! (Discount will be applied during checkout)
Only applicable for selected batches and courses.

CertNexus Certified Artificial Intelligence (AI) Practitioner

TrainingCourseLocationPriceQuantityAdd to Cart Button
SKU: N/A Category:
Our Clients