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The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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What You'll Learn

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.

Who Should Attend

  • Developers 
  • Engineers 
  • Designers 
  • Administrators 
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Prerequisites

Knowledge of Python programming language. Basic familiarity with statistics and linear algebra is recommended.

Learning Journey

Coming Soon...

Introduction to Applied Machine Learning
  • Statistical learning vs. Machine learning
  • Iteration and evaluation
  • Bias-Variance trade-off

Supervised Learning and Unsupervised Learning
  • Machine Learning Languages, Types, and Examples
  • Supervised vs Unsupervised Learning

Supervised Learning
  • Decision Trees
  • Random Forests
  • Model Evaluation

Machine Learning with Python
  • Choice of libraries
  • Add-on tools

Regression
  • Linear regression
  • Generalizations and Nonlinearity
  • Exercises

Classification
  • Bayesian refresher
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Exercises

Cross-validation and Resampling
  • Cross-validation approaches
  • Bootstrap
  • Exercises

Unsupervised Learning
  • K-means clustering
  • Examples
  • Challenges of unsupervised learning and beyond K-means

Neural networks
  • Layers and nodes
  • Python neural network libraries
  • Working with scikit-learn
  • Working with PyBrain
  • Deep Learning
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Frequently Asked Questions (FAQs)

  • Why should I pick up this course and get certified?

    Career Advancement: Boost your earning potential and unlock new job opportunities.

    Stay Relevant: Keep your skills current in the ever-changing tech landscape.

    Problem-Solving Prowess: Hone your critical thinking and problem-solving abilities.

    Hands-On Experience: Apply your knowledge through practical labs and projects.

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  • Why take this course with Trainocate?

    Here’s what sets us apart:

    - Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 16 countries (and counting!).

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    - Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.

    - Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.

    - Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.

    - Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

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Course Curriculum

Course Curriculum

Training Schedule

Training Schedule

Exam & Certification

Exam & Certification

FAQs

Frequently Asked Questions

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