How to learn Complete Machine Learning Data Science in Python

Do the words Machine Learning or Data Scientist sound familiar to you? 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 (Click here ) is thought and designed by a professional in the world of Data Science such as Juan Gabriel Gomila, so that he is going to share all his knowledge and help you understand the complex theory about mathematics that is behind it, the algorithms and Python programming libraries to become experts even though 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 that 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 vein of Juan Gabriel Gomila and that you will learn and have fun while you are learning about Machine Learning techniques with Python. In particular, the topics that 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 - Pre-processing and cleaning of the data

Part 4 - Data handling and data wrangling, operations with datasets and most famous probability distributions

Part 5 - Review of basic statistics, confidence intervals, hypothesis testing, correlation,...

Part 6 - Simple linear regression, multiple linear regression and polynomial regression, categorical variables and treatment of outliers.

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

And many more topics, complementary material, all the theory explained, the source code available from minute zero so you can become an expert with our course on Udemy.

Do the words Machine Learning or Data Scientist sound familiar to you? 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 (Click here ) is thought and designed by a professional in the world of Data Science such as Juan Gabriel Gomila, so that he is going to share all his knowledge and help you understand the complex theory about mathematics that is behind it, the algorithms and Python programming libraries to become experts even though 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 that 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 vein of Juan Gabriel Gomila and that you will learn and have fun while you are learning about Machine Learning techniques with Python. In particular, the topics that 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 - Pre-processing and cleaning of the data

Part 4 - Data handling and data wrangling, operations with datasets and most famous probability distributions

Part 5 - Review of basic statistics, confidence intervals, hypothesis testing, correlation,...

Part 6 - Simple linear regression, multiple linear regression and polynomial regression, categorical variables and treatment of outliers.

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

And many more topics, complementary material, all the theory explained, the source code available from minute zero so you can become an expert with our course on Udemy.

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