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Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Udemy
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Deal Score+64
Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]

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

Created by Kirill Eremenko, Hadelin de Ponteves, Ligency Team, SuperDataScience Team | 42.5 hours on-demand video course

Interested in the field of Machine Learning? Then this course is for you! This Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] 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.

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

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    This course is fun and exciting, and 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

    Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.

    User Rating: 5 (1 vote)
    Josh Smith
    Josh Smith

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