Decision Trees & Ensemble Methods

A specialized course focusing on tree-based methods and ensembles, covering decision trees, bagging, random forests, and gradient boosting machines.

A project that teaches you how to build popular ensemble methods like Bagging and Ada Boost from scratch in Python.

This course provides a deep dive into gradient boosting and the popular XG Boost library. You'll learn how to build and tune high-performance machine learning models.

This course contains over 200 lessons, quizzes, practical examples, ... - the easiest way if you want to learn Machine Learning. Step by step I teach you machine learning. In each section you will learn a new topic - first the idea / intuition behind it, and then the code in both Python and R.Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course:Estimate the value of used cars Write a spam filter Diagnose breast cancer All code examples are shown in both programming languages - so you can choose whether you want to see the course in Python, R, or in both languages!After the course you can apply Machine Learning to your own data and make informed decisions:You know when which models might come into question and how to compare them. You can analyze which columns are needed, whether additional data is needed, and know which data needs to be prepared in advance. This course covers the important topics:Regression Classification On all these topics you will learn about different algorithms. The ideas behind them are simply explained - not dry mathematical formulas, but vivid graphical explanations.We use common tools (Sklearn, NLTK, caret, data.table, ...), which are also used for real machine learning projects. What do you learn?Regression:Linear Regression Polynomial Regression Classification:Logistic Regression Naive Bayes Decision trees Random Forest You will also learn how to use Machine Lear

Questo corso sul Data Science con Python nasce per essere un percorso completo su come si è evoluta l'analisi dati negli ultimi anni a partire dall'algebra e dalla statistica classiche. L'obiettivo è accompagnare uno studente che ha qualche base di Python in un percorso attraverso le varie anime del Data Science. Cominceremo con un ripasso delle basi di Python, a partire dallo scaricamento e installazione, all'impostazione dell'ambiente di lavoro, passando per le strutture, la creazione di funzioni, l'uso degli operatori e di alcune funzioni importanti. Passeremo poi a vedere come manipolare e gestire un dataset, estrarne dei casi oppure delle variabili, generare dei dataset casuali, calcolare delle misure statistiche di base, creare grafici con i pacchetti Matplotlib e Seaborn.Nelle sezioni successive cominciamo a entrare nel cuore del Data Science con Python, a cominciare dal preprocessing: vediamo infatti come ripulire e normalizzare un dataset, e come gestire i dati mancanti. La sezione successiva ci permette di cominciare a impostare dei modelli di machine learning con Python: vedremo tutti gli algoritmi più comuni, sia supervisionati che non supervisionati, come la regressione, semplice, multipla e logistica, il k-nearest neighbors, il Support Vector Machines, il Naive Bayes, gli alberi di decisione e il clustering. Passeremo poi ai più comuni metodi ensemble, come il Random Forest, il Bagging e il Boosting, e all'analisi del linguaggio naturale e al suo utilizzo nel machine learning per la catalogazione dei testi.

Welcome to the Data Science Projects - Data Analysis & Machine Learning course. Data science projects course series is made from the projects that i built for my website and courses. This is not a beginner level course. This course is built for the students who learned python for data science and wants to apply what they learned but don't know where to start or for the ones who wants to practice and test their knowledge. In this course we will be building 4 data science projects which are going to be Regression, Classification, Time-Series and NLP projects. We will be covering Linear Regression, Logistic Regression, K Nearest Neighbors, Support Vector Machines, Decision Tree, Random Forests, ARIMA, Text Classification and Sentiment Analysis as machine learning algorithms in our course. All projects are going to be end to end so it will be easy to follow what we are doing step by step and I will be giving short explanations for the codes that i write. Main motivation of this course is teaching students how to do projects by theirselves. By taking this course you will be experienced in data science projects and you can apply the codes by yourself in order to build yor own project. Building projects is one of the most important ways to get into and learn Data Science. Thanks for reading, if you are interested in Data Science lets meet in the first lesson.

This Course Cover Topics such as Python Basic Concepts, Python Advance Concepts, Numpy Library , Scipy Library , Pandas Library, Matplotlib Library, Seaborn Library, Plotlypy Library, Introduction to Data Science and steps to start Project in Data Science, Case Studies of Data Science and Machine Learning Algorithms such as Linear, Logistic, SVM, NLP This is best course for any one who wants to start career in data science. with machine Learning.Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.The course provides path to start career in Data Analysis. Importance of Data, Collection of Data with Case Study is covered. Machine Learning Types such as Supervise Learning, Unsupervised Learning, are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered. This Course will design to understand Machine Learning Algorithms with case Studies using Scikit-Learn Library. The Machine Learning Algorithms  such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies