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Description of  Machine Learning AZ AI, Python & R + ChatGPT Bonus [2023]

2023 Duration: 42h 35m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 10.4 GB Genre: eLearning | Language: English Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. 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 This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. Over 900,000 students world-wide trust this course. 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 can be completed by either doing either the Python tutorials, or R tutorials, or both - Python & R. Pick the programming language that you need for your career. 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 . real-life case studies . So not only will you learn the theory, but you will also get lots of hands-on practice building your own models. And as a bonus, this course includes both Python and R code templates Who this course is for Students who have at least high school knowledge in math and who want to start learning Machine Learning. Any students in college who want to start a career in Data Science. Any data analysts who want to level up in Machine Learning. Any people who are not satisfied with their job and who want to become a Data Scientist. Any people who want to create added value to their business by using powerful Machine Learning tools.Last updated 7