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 well-known credential that shows someone knows the basics of machine learning. It's great for beginners and people who want to work in data science. This certification gives you a strong base to start a successful career in this field.

Also, the Certified Machine Learning Associate Certification teaches you all the important concepts, algorithms, and techniques in machine learning. You'll learn how to handle real-world challenges from preparing data to evaluating models. This knowledge helps you contribute to projects, make smart choices, and find valuable insights that help businesses grow.

Another benefit of getting this certification is that it makes you more marketable. As machine learning becomes more important in different industries, companies are looking for professionals with credentials in this area. The IABAC Certified Machine Learning Associate Certification boosts your resume, giving you more job options and chances for career growth.

The certification process is made easy and accessible. You will follow a well-organized curriculum that teaches you the basics of machine learning. You will learn through online lessons, hands-on activities, and tests. This way, you can learn at your own pace and fit your studies into your busy schedule.

To make sure you are good at machine learning, the Certified Machine Learning Associate Certification includes tough tests. By passing these tests, you show that you can use machine learning techniques to solve real problems. This not only makes you feel more confident, but it also gives employers proof that you know what you're doing.

When you become an IABAC Certified Machine Learning Associate, you join a cool group of professionals. This community gives you chances to work together, share knowledge, and grow professionally. You can connect with experts, have discussions, and stay updated on the latest trends. This makes your machine-learning journey even more exciting.


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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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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.