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Python-Introduction to Data Science and Machine learning A-Z

Python-Introduction to Data Science and Machine learning A-Z free download

Data Science

    Python-Introduction to Data Science and Machine learning A-Z
    Python-Introduction to Data Science and Machine learning A-Z

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Category Development / Data Science /
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Course Description

Python-Introduction to Data Science and Machine learning A-Z Udemy Free Download

Python basics Learn Python for Data Science Python For Machine learning and Python Tips and tricks

What you'll learn:

  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make powerful analysis
  • Make robust Machine Learning models
  • Create strong added value to your business
  • Use Machine Learning for personal purpose
  • Handle specific topics like Reinforcement Learning, NLP and Deep Learning
  • Handle advanced techniques like Dimensionality Reduction
  • Know which Machine Learning model to choose for each type of problem
  • Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Requirements:

  • Just some high school mathematics level.

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!

Who this course is for:

  • Beginner python users curious about Data Science

This course includes:

  • 7.5 hours on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

Course Specifications:

  • Free Demo
  • 100% job Assistance
  • Flexible Timing
  • Realtime Project Work
  • Learn From Experts
  • Get Certified
  • Place your career
  • Reasonable fees
  • Access on mobile and Tv
  • High-quality content and Class videos
  • Learning Management System
  • Full lifetime access

Keywords

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