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 a 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
- Just some high school mathematics level.
Intrigued in the field of Machine Learning? This https://joyhints.com/wp-content/uploads/2021/03/UdemyDownload.com.Udemy.-.Machine.Learning.A-Z™.Hands-On.Python..R.In.Data.Science.torrent course is for you!
This Complete Python Scripting for Automation course has actually been designed by 2 expert Data Scientists so that we can share our understanding and assist you to find out complicated theory, algorithms, and coding libraries in an easy method.
We will stroll you detailed into the World of Machine Learning. With every tutorial, you will develop brand-new abilities and enhance your understanding of this tough yet financially rewarding sub-field of Data Science.
This Complete Python Scripting for Automation course is enjoyable and interesting, however at the same time, we dive deep into Machine Learning. It is structured the list below method:
- Part 1 – Data Preprocessing
- Part 2 – Regression: Basic Direct Regression, Several Linear Regression, Polynomial Regression, SVR, Choice Tree Regression, Random Forest Regression
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Ignorant Bayes, Choice 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 Decrease: PCA, LDA, Kernel PCA
- Part 10 – Model Choice & Boosting: k-fold Cross Recognition, Specification Tuning, Grid Browse, XGBoost
The Complete Python Scripting for Automation course is loaded with useful exercises that are based on real-life examples. Not just will you discover the theory, however, you will likewise get some hands-on practice constructing your own models.
And as a benefit, this Complete Python Scripting for Automation course consists of both Python and R code templates which you can download and utilize by yourself tasks.
Crucial updates (June 2020):.
- CODES ALL AS MUCH AS DATE.
- DEEP DISCOVERING CODED IN TensorFlow 2.0.
- LEADING GRADIENT INCREASING MODELS CONSISTING OF XGBOOST AND EVEN CATBOOST!
Who this course is for:
- Anybody thinking about Machine Learning.
- Trainees who have at least high school understanding in mathematics and who wish to begin discovering Machine Learning.
- Any intermediate level individuals who understand the fundamentals of machine learning, consisting of the classical algorithms like direct regression or logistic regression, however, who wish to discover more about it and check out all the various fields of Machine Learning.
- Any individuals who are not that comfy with coding however who have an interest in Machine Learning and wish to use it quickly on datasets.
- Any trainees in college who wish to begin a profession in Data Science.
- Any data experts who wish to level up in Machine Learning.
- Any individuals who are not pleased with their task and who wish to end up being a Data Scientist.
- Any individuals who wish to develop included worth to their organization by utilizing effective Machine Learning tools.
Created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, SuperDataScience Support
Last updated 2/2021
Size: 6.32 GB