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Do the words Machine Learning or Data Scientist ring a bell? Are you curious about what these techniques are for or why companies around the world pay a salary of $120.000 to $200.000 per year to a data scientist?
Well, this course is conceived and designed by a professional from the world of Data Science, such as Juan Gabriel Gomila, so that he is going to share all his knowledge with you and help you understand the complex theory of mathematics behind it, the algorithms and Python programming libraries to become experts even if you have no previous experience.
We will see step by step how to start working with concepts and algorithms from the world of Machine Learning. With each new class and section you complete, you will have new skills that will help you understand this complete and lucrative world that this branch of Data Science can be.
Also tell you that this course is very fun, along the lines of Juan Gabriel Gomila and that you will learn and have fun while learning about Machine Learning techniques with Python. In particular, the topics that we will work on will be the following:
Part 1 - Installing Python and packages needed for data science, machine learning, and data visualization
Part 2 - Historical evolution of predictive analytics and machine learning
Part 3 - Pre-processing and data cleaning
Part 4 - Data handling and data wrangling, operations with datasets and most famous probability distributions
Part 5 - Review of basic statistics, confidence intervals, hypothesis tests, correlation,...
Part 6 - Simple linear regression, multiple linear regression and polynomial regression, categorical variables and treatment of outliers.
Part 7 - Classification with logistic regression, maximum likelihood estimation, cross validation, K-fold cross validation, ROC curves
Part 8 - Clustering, K-means, K-medoids, dendrograms and hierarchical clustering, elbow technique and silhouette analysis
Part 9 - Classification with trees, random forests, pruning techniques, entropy, information maximization
Part 10 - Support Vector Machines for Classification and Regression Issues, Nonlinear Kernels, Face Recognition (How CSI Works)
Part 11 - K Nearest Neighbors, Majority Decision, Programming Machine Learning Algorithms vs Python Libraries
Part 12 - Principal Component Analysis, Dimension Reduction, LDA
Part 13 - Deep learning, Reinforcement Learning, Artificial and convolutional neural networks and Tensor Flow
In addition, in the course you will find exercises, datasets to practice based on real life examples, so that you will not only learn the theory with the videos, but also practice to build your own Machine Learning models. And how not to forget that you will have a github with all the source code in Python to download and use in all your projects. So don't wait any longer and sign up for the most complete and useful Machine Learning course on the Spanish market!
Who is this course for?
Anyone interested in learning Machine Learning
Students who have a background in mathematics who want to learn about Machine Learning with Python
Intermediate users who know the fundamentals of machine learning such as classical linear or logistic regression algorithms but are looking to learn more and explore other fields of statistical learning
Programmers who like to code and who are interested in learning Machine Learning to apply these techniques to their datasets
University students who seek to specialize and learn to be Data Scientists
Data analysts who want to go further thanks to Machine Learning
Anyone who is not satisfied with their own job and is looking to start working as a professional Data Scientist
Anyone who wants to add value to their own company using the powerful tools of Machine Learning
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