Udemy Course- Machine Learning A-Z™: Hands-On Python & R In Data Science
Machine Learning A-Z™: Hands-On Python & R In Data Science
Udemy
Overview:
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists in order that they will share their knowledge and assist you to learn complex theory, algorithms, and coding libraries in a simple way. They will walk you step-by-step into the World of Machine Learning. With every tutorial, you'll develop new skills and improve your understanding of this challenging yet lucrative sub-field of knowledge Science.
Requirements:
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any intermediate level people that know the fundamentals of machine learning, including the classical algorithms like rectilinear regression or logistic regression, but who want to find out more about it and explore all the different fields of Machine Learning.
- Any people that aren't that comfortable with coding but who have an interest in Machine Learning and need to use it easily on datasets.
- Any students in college who want to start out a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people that aren't satisfied with their job and who want to become a knowledge Scientist.
- Any people that want to make added value to their business by using powerful Machine Learning tools.
Course content:
45 sections • 322 lectures • 44h 29m total length
Topics:
- 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
NOTE: As a bonus, this course includes both Python and R code templates which you'll download and use on your own projects.