What you’ll learn
- Learn how to build a Data Science project by theirselves
- Learn how to analyze the Machine Learning models performance
- Learn how to apply Machine Learning Algorithms
- Learn performing Data Analysis and Feature Engineering
Welcome to the Data Science Projects 2 – Data Analysis & Machine Learning course. Data science projects course series are created from the projects that i built for my courses and website. This is the second course for data science projects which we will be working with another 4 datasets. 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.