Introduction to Machine Learning Open Position at PaperVideo

Introduction to Machine Learning

Course Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed  so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way:

  • Part 1 - Data Preprocessing
  • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  • Part 4 - Clustering: K-Means, Hierarchical Clustering
  • Part 5 - Association Rule Learning: Apriori, Eclat
  • Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  • Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
  • Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
  • Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA
  • Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Who is the Course For

  • Before you load Python, Before you start R - you need this course. This introductory course will introduce you to the Fundamentals, that you need before you start getting "Hands on".
  • Anyone interested in understanding how Machine Learning is used for Data Science.Including business leaders, managers, app developers, consumers - you!
  • Adventurous folks, whom are ready to strap themselves into the exotic world of Data Science and Machine Learning.

Course Outline

  • Introduction
  • Core Concepts
  • Impacts , Importance and examples
  • The Machine Learning Process
  • How to apply Machine Learning for Data Science
  • Conclusion
  • Bonus Content

Prerequisites

  • A passion to learn, and basic computer skills!
  • Students should understand basic high-school level mathematics, but Statistics is not required to understand this course.
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