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Data Science: Machine Learning Training

Live Online & Classroom Enterprise Training

Data Science: Machine Learning focuses on building models that learn from data to make predictions and decisions. It covers algorithms like regression, classification, clustering, and model evaluation techniques.

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What is Data Science: Machine Learning Training about?

This course introduces learners to the core concepts and techniques of machine learning in the context of data science. It covers supervised and unsupervised learning methods, model evaluation, feature engineering, and optimization techniques. Participants will work with real-world datasets to build regression, classification, and clustering models using popular machine learning frameworks. By the end of the course, learners will be equipped to design, train, and evaluate machine learning models for solving practical business and research problems.

What are the objectives of Data Science: Machine Learning Training ?

  • Understand the principles and applications of machine learning. 
  • Implement supervised learning techniques such as regression and classification. 
  • Apply unsupervised learning methods such as clustering and dimensionality reduction. 
  • Evaluate model performance and fine-tune hyperparameters. 
  • Use Python libraries (e.g., scikit-learn, TensorFlow, or PyTorch) for building ML models.

Who is Data Science: Machine Learning Training for?

  • Aspiring data scientists and machine learning engineers. 
  • Analysts seeking to expand into predictive analytics. 
  • Software developers interested in AI and ML applications. 
  • Business professionals aiming to apply ML for decision-making. 
  • Students or researchers exploring artificial intelligence.

What are the prerequisites for Data Science: Machine Learning Training?

Prerequisites:  

  • Basic knowledge of Python programming. 
  • Understanding of statistics and probability. 
  • Familiarity with linear algebra and calculus fundamentals. 
  • Exposure to data analysis and visualization tools. 
  • Curiosity and problem-solving mindset. 


Learning Path: 

  • Introduction to machine learning concepts and workflows. 
  • Supervised learning: regression and classification. 
  • Unsupervised learning: clustering and dimensionality reduction. 
  • Model selection, evaluation, and optimization. 
  • Real-world projects and case studies with Python libraries. 


Related Courses: 

  • Introduction to Data Science with Python 
  • Deep Learning Fundamentals 
  • Applied Statistics for Data Science 
  • Artificial Intelligence and Neural Networks

Available Training Modes

Live Online Training

5 Days

Course Outline Expand All

Expand All

  • Introduction to Machine Learning
  • Basics of Evaluating Machine Learning Algorithms
  • Conditional Probabilities
  • Linear Regression for Prediction
  • Smoothing
  • Working with Matrices
  • Nearest Neighbors
  • Cross-validation
  • Generative Models
  • Classification with More than Two Classes
  • Caret Package
  • Case Study: MNIST
  • Recommendation Systems
  • Regularization

Who is the instructor for this training?

The trainer for this Data Science: Machine Learning Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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