Patrick Curtis – Applied Machine Learning
Original price was: $197.00.$56.00Current price is: $56.00.
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Description
Unlock exclusive learning opportunities with the Patrick Curtis – Applied Machine Learning course at esys[GB]. Explore expert insights, advanced techniques, and practical applications from world-renowned instructors in your chosen field. Empower your growth and career with our curated collection of over 70,000 courses from top authors such as John Overdurf, Conor Harris, Tony Robbins, Dr. Joe Dispenza, and more.
[Pre-Order] – Deliver digital download link within 4-8 business days after successful payment. Please contact us to get more details.
Purchase Patrick Curtis – Applied Machine Learning courses at here with PRICE $197 $56
Introducing…
The Wall Street Oasis
APPLIED MACHINE LEARNING
140+ Lessons, 40 Exercises, 3+ hours of video lessons
To Help you Thrive in the Most Prestigious Jobs on Wall Street…
HERE’S JUST SOME OF WHAT YOU’LL GET IN THIS COURSE
Data Cleaning & Exploration (34 lessons)
This module uses video lessons and 12 exercises to practice exporting and filtering through data using Jupyter Notebook. We will also practice manipulating information by replacing and combining, identifying outliers, and display the data with graphs.
Regression Algorithms (4 lessons)
This module uses 4 video lessons to delve deep into regression algorithms, hitting on real relationships, overfitting, and regularization. We will also discuss non-linear relationships and how to model them using decision trees. We then discuss using various ensemble methods.
Liquidity Regressor (41 lessons)
This module uses video lessons and 11 exercises to go over how to split data into training and testing sets, construct model pipelines, perform hyperparameter tuning, and cross-validate alternative models to find the top performer. Additionally, we will go over how to evaluate models and visualize predictions.
Classification Algorithms (3 lessons)
This module contains 3 video lessons to demonstrate how some learning algorithms are used to solve classification problems. By the end of this module, you will be familiar with Characteristics of Binary Classification Problems, Regularized Logistic Regression Models, and Decision Tree Ensemble Classification Models.
Investor Classifier I (30 lessons)
This module uses video lessons and 9 exercises to walk through a business case study. We will perform more advanced data exploration and visualization and engineer features based on conditional relationships between existing features.
Investor Classifier II (31 lessons)
This module uses video lessons and 8 exercises to continue the business case study from the previous module. We will go over how to use stratified random sampling, the confusion matrix and its advantages over R^2, and go into detail over AUROC. After this module, you would have built a machine learning classifier from start to finish.
Course Summary – Table Of Contents
Below you will find a list of the modules and lessons included in this course.
Module 1: Data Cleaning & Exploration
Module 2: Regression Algorithms
Module 3: Liquidity Regressor
Module 4: Classification Algorithms
Module 5: Investor Classifier I
Module 6: Investor Classifier II
Purchase Patrick Curtis – Applied Machine Learning courses at here with PRICE $197 $56
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📚 Course Highlights
- ✅ Comprehensive training materials from top experts in the industry.
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