Do you hear the words Machine Learning or Data Scientist? Are you curious about what these techniques are for or why companies around the world pay a salary of $ 120,000 to $ 200,000 a year to a data scientist?
Well, this course is designed and designed by a professional in the world of Data Science such as Juan Gabriel Gomila, so that he will share all his knowledge and help you understand the complex theory of mathematics behind him, the algorithms and Python programming libraries to become all experts even though you don't have 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 world so complete and lucrative that this branch of Data Science can be.
Also tell you that this course is very fun, in the line of Juan Gabriel Gomila and that you will learn and have fun while learning about Machine Learning techniques with Python. In particular, the topics we will work on will be the following:
- Part 1 - Installation of Python and necessary packages for data science, machine learning and data visualization
- Part 2 - Historical evolution of predictive analysis and machine learning
- Part 3 - Preprocessing and data cleaning
- Part 4 - Handling data and data wrangling, operations with datasets and most famous probability distributions
- Part 5 - Review of basic statistics, confidence intervals, hypothesis contrasts, correlation, ...
- Part 6 - Simple linear regression, multiple linear regression and polynomial regression, categorical variables and outliers treatment.
- Part 7 - Classification with logistic regression, estimation with maximum likelihood, 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, maximization of information
- Part 10 - Support Vector Machines for classification and regression problems, non-linear kernels, facial recognition (how CSI works)
- Part 11 - The nearest K 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 videos, but also to 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 in the Spanish market!
Who this course is for:
- Anyone interested in learning Machine Learning
- Students who have a knowledge of mathematics who want to learn about Machine Learning with Python
- Intermediate users who know the fundamentals of Machine learning as the classical linear or logistic regression algorithms but seek to learn more and explore other fields of statistical learning
- Programmers who like the code and are interested in learning Machine Learning to apply these techniques to their datasets
- University students looking 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 work and seeks to start working as a professional Data Scientist
- Anyone who wants to add value to their own company using the powerful Machine Learning tools
- High school mathematics knowledge or basic statistical knowledge are required
- It is recommended to know how to program a bit to focus on learning the analysis techniques in Python although it is not totally necessary
- Be a true Jedi Master of Machine Learning with Python
- Carry out accurate predictions
- Develop robust Machine Learning models
- Use Machine Learning techniques for personal use and to advise companies
- Have a good intuition of most Machine Learning models
- Make very powerful and accurate analyzes
- Give added value to your own company or business
- Know which Machine Learning model best suits each type of problem
- Build diverse models of Machine Learning and combine them to solve any problem that one arises
- Use advanced techniques to reduce the size of the problem