Machine Learning Training by Experts

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Our Training Process

Machine Learning - Syllabus, Fees & Duration

Module 1 : CORE PYTHON

  • Short history
  • Introduction
  • Features of Python
  • Python2 Vs Python 3
  • Python Installation
  • Python Interpreter
  • How to Run Python
  • Basic Syntax
  • Python Identifiers, Keywords and Indentation Rules
  • Type Checking
  • Input, Output, Variables, Data Type and Datatype Casting

Module 2 : MACHINE LEARNING

  • Data Analysis
  • Data Visualization
  • Data Classification
    • Supervised Learning
    • Unsupervised Learning

Module 3 : SUPERVISED LEARNING

  • Classification
    • K-Nearest Neighbours
    • Decision Tree
    • Naive Bayes
    • Logistic Regression
    • Support Vector Machine
    • Random Forest
    • Logistic Regression
  • Regression
    • Single Linear Regression
    • Multiple Linear Regression

Module 4 : UNSUPERVISED LEARNING

  • Clustering
    • Hierarchical Clustering
    • KMeans Algorithm Association

Module 5 : DATA PREPROCESSING

  • PCA
  • Dimensionality reduction
  • Correlation
  • Features Extraction Algorithm

Download Syllabus - Machine Learning
This syllabus is not final and can be customized as per needs/updates
 
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Machine Learning Jobs in Dublin

Enjoy the demand

Find jobs related to Machine Learning in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Dublin, chennai and europe countries. You can find many jobs for freshers related to the job positions in Dublin.

  • Machine Learning Engineer
  • Data Architect
  • Data Engineer
  • Artificial Intelligence Analyst
  • Data Mining Specialists
  • BigQuery Engineer
  • Machine Learning Specialist
  • Data Manager
  • Advanced Analytics Engineer
  • Data Scientist

Machine Learning Internship/Course Details

Machine Learning internship jobs in Dublin
Machine Learning The student will be able to create and apply pattern classification algorithms to categorize multivariate data, create and apply regression algorithms to uncover correlations between data variables, and use reinforcement learning methods to operate complicated systems after finishing the course. We live in a world surrounded by humans who can study everything using their abilities and learning abilities, as well as machines that follow our directions. Can a machine, like a human, learn from skills or previous data? So here's where Machine Learning comes in. An overview of artificial intelligence and machine learning, fundamental principles for machine learning, data pre-processing, feature extraction, regression, logistic regression, nave Bayes, decision trees, kernel methods, and support vector machine and k-means and hierarchical clustering are among the topics covered in this course. You'll also have the opportunity to work as a data scientist, Machine Learning engineer, or data engineer for several years and learn from industry specialists. Learning machine learning can help you advance your profession. . You'll need data training capabilities, algorithm basics, advanced, automation, and iterative processes, ensemble modeling, and scalability to build a strong ML (machine learning) system. Machine learning is the study of computational algorithms that can automatically improve witpracticese and is implemented as part of artificial intelligence. Candidates will acquire the fundamental concepts and intuition underpinning modern machine learning algorithms, as well as a more formal knowledge of how, when, and why they work, in this course.

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