UDEMY 2021 - Machine Learning A-Z : Become Kaggle Master

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Master Machine Learning Algorithms Using Python From Beginner to Super Advance Level including Mathematical Insights.

You can find "Download Link" as a button at the end of this article.

What you’ll learn

  • Master Machine Learning on Python
  • Learn to use MatplotLib for Python Plotting
  • Learn to use MatplotLib for Python Plotting

  • Learn to use Numpy and Pandas for Data Analysis
  • Learn to use Numpy and Pandas for Data Analysis

  • Learn to use Seaborn for Statistical Plots
  • Learn All the Mathmatics Required to understand Machine Learning Algorithms
  • Implement Machine Learning Algorithms along with Mathematic intutions
  • Projects of Kaggle Level are included with Complete Solutions
  • Learning End to End Data Science Solutions
  • All Advanced Level Machine Learning Algorithms and Techniques like Regularisations , Boosting , Bagging and many more included
  • Learn All Statistical concepts To Make You Ninza in Machine Learning
  • Real World Case Studies
  • Model Performance Metrics
  • Deep Learning
  • Model Selection
  • Requirements

  • Any Beginner Can Start this Course
  • 2+2 knowledge is more than sufficient as we have covered almost everything from scratch.
  • Description

    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 from beginner to advance level.

    We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites.

    We have covered following topics in detail in this course:

    1. Python Fundamentals

    2. Numpy

    3. Pandas

    4. Some Fun with Maths

    5. Inferential Statistics

    6. Hypothesis Testing

    7. Data Visualisation

    8. EDA

    9. Simple Linear Regression

    10. Multiple Linear regression

    11. Hotstar/ Netflix: Case Study

    12. Gradient Descent

    13. KNN

    14. Model Performance Metrics

    15. Model Selection

    16. Naive Bayes

    17. Logistic Regression

    18. SVM

    19. Decision Tree

    20. Ensembles – Bagging / Boosting

    21. Unsupervised Learning

    22. Dimension Reduction

    23. Advance ML Algorithms

    24. Deep Learning

    Who this course is for:

  • This course is meant for anyone who wants to become a Data Scientist
  • Created by Teclov Pvt LtdLast updated 11/2018EnglishEnglish [Auto-generated]

    Size: 13.97 GB

    Download File Here


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