Certified Machine Learning Associate Certification (CMLA – AI3020)

  • Essential Certification for Machine Learning aspirants.
  • Core concepts and Algorithms of Machine Learning
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The Certified Machine Learning Associate Certification provided by IABAC has global recognition. It covers concepts like Basics of Machine Learning, Statistics, Machine Learning Algorithms, Statistical methods, Business Analytics etc.

  • Python for Data Science
  • Statistics for Data Science
  • SQL for Data Science
  • Machine Learning

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Certified Machine Learning Associate

Test Perparation Study Guide

The Benefits

International Credential

IABAC® is a widely recognized credentialing framework based on European commission funded EDISON Data Science body of knowledge. This credential provides distinction as high potential certified Data Science Professionals enabling better career prospects.

Global Opportunities

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.


IABAC Certification designed to cater to the job requirements of all experience levels and specializations, which suits roles aligned with the industry standards.

Relevant and updated

IABAC® CPD (Continuing Professional Development) program enables credential holders to update their skills and stay relevant to the industry requirements.

Higher Salaries

On an average, a certified professional earns 30-40% more than their non-certified as per recent study by Forbes.

Summits & Webinars

In addition, IABAC members will have exclusive access to seminars and Data Science summits organised by IABAC partners across the globe.


Introduction to Machine Learning   


  • What is machine learning and its applications?
  • Types of machine learning: supervised, unsupervised, and reinforcement learning
  • Steps in a machine learning project lifecycle
  • Introduction to Python and relevant libraries for machine learning (e.g., NumPy, Pandas)

Data Preprocessing  

  • Handling missing values in data
  • Data cleaning and outlier detection
  • Feature scaling and normalization techniques
  • Encoding categorical variables

Supervised Learning: Regression 


  •  Introduction to regression analysis
  •  Linear regression and its variants
  •  Polynomial regression
  •  Evaluation metrics for regression models (e.g., mean squared error, R-squared)

Supervised Learning: Classification  


  •  Introduction to classification problems
  •  Logistic regression
  •  Decision trees and random forests
  •  Support Vector Machines (SVM)
  •  Evaluation metrics for classification models (e.g., accuracy, precision, recall, F1-score)

Unsupervised Learning: Clustering  


  •  Introduction to clustering algorithms
  •  K-means clustering
  •  Hierarchical clustering
  •  Density-based clustering (e.g., DBSCAN)
  •  Evaluation metrics for clustering algorithms

Unsupervised Learning: Dimensionality Reduction  


  •  Principal Component Analysis (PCA)
  •  Singular Value Decomposition (SVD)
  •  t-SNE (t-Distributed Stochastic Neighbor Embedding)
  •  Applications of dimensionality reduction in machine learning

Ensemble Learning  


  •  Bagging and random forests
  •  Boosting algorithms (e.g., AdaBoost, Gradient Boosting)
  •  Stacking and voting classifiers
  •  Introduction to XGBoost and LightGBM

Deep Learning  


  •  Introduction to artificial neural networks
  •  Feedforward neural networks and backpropagation
  •  Convolutional Neural Networks (CNN) for image classification
  •  Recurrent Neural Networks (RNN) for sequence data
  •  Introduction to deep learning frameworks (e.g., TensorFlow, Keras)

Model Selection and Evaluation  


  •  Overfitting and underfitting in machine learning models
  •  Cross-validation techniques
  •  Hyperparameter tuning
  •  Model evaluation and selection strategies

Introduction to Natural Language Processing  


  •  Basics of text preprocessing and tokenization
  •  Text classification using Naive Bayes and SVM
  •  Sentiment analysis and text generation
  •  Word embeddings and Word2Vec

Introduction to Recommendation Systems  


  •  Collaborative filtering
  •  Content-based filtering
  •  Evaluation metrics for recommendation systems
  •  Introduction to matrix factorization methods

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