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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Prerequisite Certification

  • No mandatory prerequisites
  • Basic understanding of Data analysis.
  • Recommended essential mathematics knowledge

Who can pursue this certification?

The Certified Machine Learning Associate certification is typically designed for individuals who have a foundational understanding of machine learning concepts and techniques and want to validate their knowledge and skills in the field.

Students: Undergraduate or graduate students studying computer science, data science, statistics, mathematics, or related fields can pursue the certification to enhance their knowledge and demonstrate their proficiency in machine learning concepts.

Data Analysts: Professionals working as data analysts who want to expand their skill set and gain expertise in machine learning techniques can pursue the certification to strengthen their knowledge of data preprocessing, feature engineering, and building predictive models.

Software Engineers: Software engineers interested in developing machine learning applications or incorporating machine learning algorithms into their software systems can pursue the certification to gain a solid understanding of machine learning principles and techniques.

Data Scientists: Data scientists who want to validate their expertise in machine learning and demonstrate their ability to develop and implement machine learning models can pursue the certification to showcase their skills to employers or clients.

Business Analysts: Business analysts who work with data and want to leverage machine learning to extract insights and make data-driven decisions can pursue the certification to enhance their understanding of machine learning algorithms and their applications.

Professionals in Related Fields: Individuals working in fields such as finance, healthcare, marketing, or any industry where machine learning is increasingly applied, can pursue the certification to expand their Knowledge and apply machine learning techniques to their domain-specific challenges.

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.

Specialization

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.

COURSE SYLLABUS

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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