Artificial Intelligence Practitioner​

Emerging Technologies Certifications

Certified Artificial Intelligence Practitioner

The Certified Artificial Intelligence Practitioner™ (CAIP) is an in-demand, fast-growing training program and certification designed for data practitioners desiring to get equipped with vendor-neutral, cross-industry knowledge of Artificial Intelligence (AI) concepts and skills.

It enables you to select, train, and implement Machine Learning solutions.


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AI Practitioner Certification

A Certified Artificial Intelligence Practitioner™ (CAIP) is a data professional that can implement the power of AI and machine learning to solve business challenges using various modeling techniques.

CAIPs can utilize AI to automate processes, reduce costs, drive down completion times, and perform operational tasks that allow humans to perform higher level work.

Certified AI Practitioners enable organizations to enhance customer experiences and propel innovation to achieve their AI goals.

AI Practitioner Course Overview

The Certified Artificial Intelligence Practitioner™ (CAIP) is an in-demand, fast-growing training program and certification designed for data practitioners desiring to get equipped with vendor-neutral, cross-industry knowledge of Artificial Intelligence (AI) concepts and skills. It enables you to select, train, and implement Machine Learning solutions.

The Certified Artificial Intelligence Practitioner™ (CAIP) has emerged as the industry standard for those desiring to confirm their AI and ML skills.

Validate a foundational knowledge of AI concepts, technologies, algorithms, and applications.

Verify that applicants and team members have the requisite skills and ability to perform AI tasks.

Course Overview

  • Course accredited by CertNexus
  • 4 days live virtual sessions with accredited trainers for online live learning + Lifetime access to E-learning
  • Lab access included
  • Certification exam included
  • High-quality E-learning material for self-paced learning
  • E-learning access includes quizzes and practice exams
  • Doubt clearance sessions included
  • Applied Artificial Intelligence and Machine Learning in Business
  • Problem Formulation
  • Data Collection, Comprehension, Cleaning, and Engineering
  • Algorithm Selection and Model Training
  • Model Handoff
  • Ethics and Oversight

Course Curriculum

Target Audience

This certification exam is designed for practitioners who are seeking to demonstrate a vendorneutral, cross-industry skill set within AI and with a focus on ML that will enable them to design, implement, and hand off an AI solution or environment. Exposure in a professional environment: 1 to 3 years.


For attending the course or the examination, no pre-requisites are required. However, the following background knowledge is recommended:

  • Explain how artificial intelligence (AI) and machine learning (ML) can solve business problems.
  • Execute an applied ML workflow.
  • Summarize outcomes of accepted learning algorithms.
  • Formulate mathematical representations of business problems using domain insight.
  • Develop and test hypothesis using experimental design.
  • Distinguish benefits and drawbacks of various machine learning models and given a scenario select appropriate model and define tradeoffs.
  • Given a scenario, select appropriate tool sets (both proprietary and open source).
  • Demonstrate responsibility based upon ethical implications when sharing data sources.
  • Plan, manage, train and hand off an ML model as part of a (software) solution.
  • Communicate the findings of an AI and ML workflow and solution back to the organization.
  • Identify the impact that propagating biases has within AI.
  • Select and implement an appropriate algorithm for a given business problem.
  • Select and implement the appropriate techniques for a given ML problem.
  • Demonstrate a working level knowledge of development tools such as Python and R

Certification Examination Details

  • No. of items: 80 
  • Pass mark: 60%
  • Exam duration: 120 minutes (Note: exam time includes 5 minutes for reading and signing the Candidate Agreement and 5 minutes for the Pearson VUE testing system tutorial.)
  • Exam Options: In person at Pearson VUE test centers or online via Pearson OnVUE
  • Item Formats: Multiple Choice/Multiple Response
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Course Content

Objective 1.1 Identify and describe how artificial intelligence and machine learning are used to solve business problems.

  • Business benefits of applied artificial intelligence and machine learning
  • Relationship between machine learning and data science
    • ML is a subset of data science
  • Data mining
  • NLP
    • Speech recognition
    • Text analysis
  • Computer Vision
    • Image recognition
    • Video tracking

Objective 2.1 Given a business problem, select an appropriate machine learning model and outcome.

• Supervised learning o Discrete classification ▪ Unbalanced class distribution ▪ Resampling ▪ Class probability distributions o Continuous regression ▪ Different types of regressions • Lasso • Ridge • Linear

Leave one out regression ▪ Relationships between Lasso/Ridge and L1/L2 norms o Deep learning • Unsupervised learning o Clustering o No labels present o Identifying groups or segments o Dimensionality reduction • Semi-supervised • Reinforcement learning

Objective 2.2 Use an experimental design approach to develop and test a hypothesis.

• Design of experiments • Statement of hypotheses o AB testing o Alpha vs. Beta values o P values o Confidence intervals • Understanding business KPIs • Actionable insights over higher accuracy models o Precision vs. recall ▪ F Score ▪ Confusion matrix • Utilizing judgement to recognize when to stop

Objective 2.3 Select appropriate tools to solve a given machine learning task.

  • Open source AI tools
    • Tensorflow
      • NumPy
      • Keras
      • TensorBoard
    • PyTorch
    • NLTK
    • Scikit-learn
    • Pandas
    • Spark ML
      • Spark Core
      • Spark SQL
  • Proprietary AI tools
    • Microsoft Azure AI tool
      • Azure Machine Learning
      • Azure Databricks
      • Azure Search
      • Cognitive Services
    • Amazon Web Services AI Services
      • Amazon SageMaker
      • ML Framework
      • AI Services
      • AWS DeepRacer
    • IBM Watson
      • Watson OpenScale
      • Watson Machine Learning
      • NLU
    • Google AI
      • ML Kit
      • DeepDream
      • Data Search
      • Cloud AI
      • Cloud AutoML

Objective 2.4 Evaluate the applicability of new AI technologies to perform a given business task.

  • Process to evaluate the applicability of new technologies
  • Future innovations
    • Distributed artificial intelligence
    • Hyper-heuristic
    • Federated learning

Objective 3.1 Collect and prepare a dataset to use for training and testing.

  • Sources of data
    • Big data/data sets
      • Volume, variety, velocity
      • Data repositories
      • Data prep
      • Stream data
  • Structure of data
  • Extract, transform, and load (ETL)

Objective 3.2 Analyze a dataset to gain insights.

  • Visualization
  • Correlations between attributes
  • Attribute characteristics
  • Descriptive statistics applied to features
    • Standard deviation
    • Kurtosis
    • Mean, mode, median
    • Variance
    • Column distribution
  • Classic data sets
    • Time Series
    • Bayesian

Objective 3.3 Clean data in preparation for use in machine learning.

  • Data backups
  • Data cleansing
  • Typecasting
  • Operations appropriate for different data types
  • Data encryption

Objective 3.4 Engineer features of a dataset to prepare it for use in a machine learning model.

  • Feature transformation
  • Dimensionality reduction
  • Center and spread measures
  • Imputing missing values
  • Duplicates
  • Data binning
  • Normalization and standardization
  • String manipulation
  • Summarization
  • Embedded spaces

Objective 4.1 Select and implement an appropriate algorithm to solve a given business problem.

  • Decision Tree
    • Hyperparameters
      • Maximum depth
      • Minimum sample per leaf node
    • Number of random splits
    • Information gain/entropy measure
    • GINI Index
    • Continuous variable discretization
    • Random Forest
      • Hyperparameters
        • Number of trees
        • Maximum depth
        • Minimum samples required to split node
        • Minimum samples required to be at leaf node
      • Variable selection
      • Assumption
      • Sampling is representative
  • SVM
    • Kernels
      • RBF
      • Gaussian
      • Polynomial
      • Linear kernel
      • Sigmoid
      • TanH
    • Can tackle non-linearly separable data
    • Uses hyperplanes for separation
  • Neural networks
    • ANN
      • Layers of a traditional network Input
      • Hidden
      • Input
      • Output
    • Activation functions
      • Tanh
      • Sigmoid
      • ReLU
    • CNN
      • Image processing
    • RNN
      • Natural language
      • LSTM
        • Natural language
        • Forecasting
    • GAN
      • Adversarial content creation
  • Clustering
    • K-means clustering
      • Issues clustering circular data
      • Methods for deciding K-means
        • Elbow
        • Silhouette plot
  • Classification
    • Logistic regression
    • Softmax regression
  • Regression
    • Linear regression
  • KNN
    • How do you decide the K?
    • Difference between K-Means and KNN

Objective 4.2 Select and implement the appropriate techniques for a given machine learning problem.

  • Dimensionality reduction
    • Methods for DR
      • PCA
      • Lasso
      • Random Forest
  • Feature engineering
    • When feature engineering is useful
    • Benefits of deriving custom features
  • Feature expansion
    • Benefits to tackling nonlinear data
    • Methods of feature expansion
  • Hyper-parameter optimization
    • Awareness of core parameters for key models
  • Model tuning
  • Types of hyperparameter searches
    • Random
    • Grid
    • Bayesian
    • Genetic
  • Cross validation
    • Leave 1 out cross validation
      • Used for small data sets
    • N Fold cross validation
  • Regularization
    • Types
      • L1
      • L2
    • Know the differences between L1 and L2
  • Variance vs. bias
    • The variance bias tradeoff
  • Model generalization
    • Overfitting
    • Use regularization
    • Relationship with variance and bias
  • Embedded spaces
    • Embedded space extraction from CNN
    • Word vector open source tools
      • Fasttext
      • word2vec
      • doc2vec
  • Data sets
    • Structured
    • Unstructured

Objective 4.3 Manage the time needed to train a model.

  • Estimating the time needed to run a batch over a certain number of epochs
  • Optimizing the development environment (e.g., scaling up GPU) vs. making compromises in the model to reduce processing time
  • Awareness around processing costs

Objective 4.4 Train and tune a machine learning model.

  • Model performance evaluation
    • Confusion matrix
    • Classifier performance measurement
    • Accuracy o Precision
    • Recall
    • Precision and recall tradeoff
    • F1 Score
    • ROC Curve
    • Thresholds
    • AUC
    • PRC
  • Cross validation
  • Model generalization
  • Performance tuning

Objective 5.1 Communicate the findings of a machine learning project back to the organization.

  • Translating ML results into potential business actions
    • Prediction or classification problems
  • Data visualization
  • Big data
  • Internet of things
  • Categorical
  • Quantitative
  • Types
    • Table
    • Graph
  • Explain accuracy vs precision vs recall to non-ML practitioners
    • True positives
    • False positives
  • Compromising on accuracy for model interpretability

Objective 6.1 Identify and describe the impact that propagating biases has within AI.

  • Avoiding biases in data
    • Current data sources
    • Model explainability
    • Transparency
  • Preventing propagation of preconceived notions
    • Open review of data sets and algorithmic approaches
  • Recognizing that proxies may be indicative of larger social discriminations

Objective 6.2 Comply with laws and standards that are applicable to businesses that employ AI.

  • Relevant data privacy laws
    • GDPR
    • California Consumer Privacy Act

Objective 6.3 Given a use case, identify and describe the ethical issues on sharing the data sources.

  • The importance of Open Source Data Access and data integrity
  • Respecting the privacy of data sources and targets
  • Guidelines for protecting privacy when collecting, storing, and disposing of data

Objective 6.4 Comply with company policies to promote privacy, security, and ethical practices.

  • Potential ethical issues resulting from AI practices
    • Recognizing that the output of AI even though derived can still infringe on privacy/copyright/intellectual property rights
    • Recognizing the importance of basic humanitarian principles before developing any AI capabilities
    • Ability to identify alternative uses of AI you create by bad actors


Learning Options

Self-Paced Learning
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 40 Hours of Self-Paced Videos, Quizzes and Practice Exams
  • Certification exam voucher included
  • 24x7 learner assistance and support
Online Live Sessions
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • Four Days of Online Live Public Training Sessions
  • Certification exam voucher included
  • 24x7 learner assistance and support
Group Sessions
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • Four Days of Online Live OR Classroom Private Training Sessions
  • Certification exam voucher included
  • 24x7 learner assistance and support

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