Certified Machine Learning Associate Certification (CMLA – AI3020)

  • Career Boost: Gain an edge with the Certified Machine Learning Associate Certification, showcasing your expertise and opening doors to exciting opportunities.
  • Skill Validation: Stand out and prove your proficiency in machine learning techniques, earning trust and credibility in the industry.
  • Stay Ahead: Stay updated with the latest advancements and best practices in machine learning, equipping yourself to navigate the evolving AI landscape.
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The Certified Machine Learning Associate Certification is a recognized credential that validates an individual's expertise in the fundamentals of machine learning. Designed for beginners and aspiring professionals, this certification serves as a solid foundation for building a successful career in the field of data science.

Moreover, the Certified Machine Learning Associate Certification equips you with a comprehensive understanding of essential concepts, algorithms, and techniques in machine learning. From data preprocessing to model evaluation, you will gain the necessary skills to handle real-world machine learning challenges. This knowledge empowers you to contribute meaningfully to projects, make informed decisions, and generate valuable insights that drive business growth.

Another advantage of obtaining this certification is the increased marketability it brings. As machine learning becomes more integral to various industries, professionals with recognized credentials in this field are in high demand. The IABAC Certified Machine Learning Associate Certification enhances your profile, opening doors to exciting job opportunities and career advancements.

The certification process itself is streamlined and accessible. It consists of a well-structured curriculum that covers the fundamental concepts of machine learning, delivered through a combination of online modules, practical exercises, and assessments. This flexibility allows you to learn at your own pace, fitting your studies around your existing commitments.

To ensure a high standard of proficiency, the Certified Machine Learning Associate Certification incorporates rigorous assessments. By successfully completing these evaluations, you demonstrate your ability to apply machine learning techniques and solve real-world problems. This validation not only boosts your confidence but also provides employers with tangible evidence of your competence.

Additionally, upon achieving IABAC Certified Machine Learning Associate Certification, you become part of a vibrant community of like-minded professionals. This network offers opportunities for collaboration, knowledge sharing, and professional growth. You can connect with experts in the field, engage in discussions, and stay updated on the latest trends, further enriching your machine learning journey.



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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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Certified Machine Learning Associate

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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.

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What is the process to change my name on the certificate?

The name change needs to be requested over email service@iabac.org. You need to include the identity proof with name matching with the request name change. The process usually takes about 15 working days.


Do IABAC provide scholarships?

Yes. IABAC provides scholarships for both individual aspirants and organizations on a case-to-case basis. IABAC has granted nearly USD 100 thousand worth of scholarships to universities and academic institutions.  You can send an email to service@iabac.org requesting scholarship with a scholarship letter detailing the reason and references attached.

Is the certification open to individuals who have a gap after graduation ?

 Yes , you can still opt for this certification which might be helpful for your future career journey

What Is Unique about the IABAC Certification Body?

IABAC is a globally recognized professional association dedicated to growing and enhancing the field of applied data science and business analytics

How long does it take to get a Certified Machine Learning Associate Certification after the exam completion?

The time taken for Certified Machine Learning Associate certification after the exam completion is usually 10 working days.