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Are you interested in getting to know the world of Machine Learning in depth? Then this course is designed especially for you!
This course has been designed by professional Data Scientists to share our knowledge and help you learn the complex theory, algorithms and programming libraries in an easy and simple way.
In it we will guide you step by step in the world of Machine Learning. With each class you will develop new skills and improve your knowledge of this complicated and lucrative sub-branch of Data Science.
This course is fun and enjoyable but at the same time a challenge because we have a lot of Machine Learning to learn. We have structured it as follows:
Part 1 - Data Preprocessing
Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, and Random Forest Regression
Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification and Random Forest Classification
Part 4 - Clustering: K-Means, Hierarchical Clustering
Part 5 - Learning by Association Rules: Apriori, Eclat
Part 6 - Reinforcement Learning: Upper Confidence Limit, Thompson Sampling
Part 7 - Natural Language Processing: Bag-of-words model and NLP algorithms
Part 8 - Deep Learning: Artificial Neural Networks and Convolutional Neural Networks
Part 9 - Dimension Reduction: ACP, LDA, Kernel ACP
Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
In addition, the course is packed with practical exercises based on real life examples, so you will not only learn theory, but also put your own models into practice with guided examples.
And as a bonus, this course includes all the code in Python and R for you to download and use in your own projects.
Who is this course for?
Any student who is interested in Machine Learning.
Students with a high school level of mathematics who want to start in Machine Learning.
Intermediate level students with a basic knowledge of Machine Learning, including classical linear or logistic regression algorithms, but who want to learn more and explore the different fields of Machine Learning.
Students who do not feel comfortable programming but are interested in Machine Learning and want to apply the techniques to the analysis of data sets.
University students who want to start in the world of Data Science.
Any data analyst who wants to improve their machine learning skills.
People who are not satisfied with their work and want to become a Data Scientist.
Anyone who wants to add value to their company with the power of Machine Learning.
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