Machine Learning A-Z

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

Beginner 5(2 Ratings) 3 Students enrolled
Created by sudhanshu Raghuwanshi Last updated Fri, 28-May-2021 English
What will i learn?
  • The course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

Curriculum for this course
6 Lessons 01:13:11 Hours
Welcome to the course
2 Lessons 00:13:59 Hours
  • Applications of machine learning 00:13:59
  • Why Machine Learning is the Future
  • Getting Started 00:35:36
  • Importing the Dataset 00:07:09
  • R operations Data Cleaning,Error Correction and Data Transformation on airquality dataset 00:16:27
  • Data Preprocessing
Requirements
  • ust some high school mathematics level.
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Description

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

This course has been designed by two professional Data Scientists 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

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

Important updates (June 2020):

  • CODES ALL UP TO DATE

  • DEEP LEARNING CODED IN TENSORFLOW 2.0

  • TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!


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About the instructor
  • 4 Reviews
  • 25 Students
  • 17 Courses
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dfsd

sadas

Student feedback
5
Average rating
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Reviews
  • Wed, 15-Jul-2020
    review 2
  • Wed, 15-Jul-2020
    Instructor Four
    A good course...give it 5 star : bharat joshi
Free
Includes:
  • 01:13:11 Hours On demand videos
  • 6 Lessons
  • Full lifetime access
  • Access on mobile and tv