Machine Learning in Python builds upon the statistical knowledge you have gained earlier in the program. This couse focuses on predictive modelling and enters multidimensional spaces which require an understanding of mathematical methods, transformations, and distributions. The course introduces these concepts as well as complex means of analysis such as clustering, factoring, Bayesian inference, and decision theory while also allowing you to exercise your Python programming skills.
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In this part of the course, we will discuss what the course covers, why you need to learn advanced statistics, what’s the differences are with machine learning, and how to get the most out of this training. In this section you will also expand on what you learned in our Statistics training with additional concepts and will apply all the theory in Python. This section serves for two purposes 1) a useful refresher of regression and 2) a great way to reinforce what you have learned applying it in practice while coding.
After we learn about the simple linear regression, we build up on that knoweldge to multiple linear regression. In this part, we explore models with many input variables, no matter of numerical or categorical and learn how to make predictions using them.
While there are many libraries that can compute a regression model, the most numerically stable one is sklearn. It is also the preferred choice of many machine learning professionals. In this section, we implement all we know about regressions in this amazing library.
Data scientists use logistic regressions when the dependent variable is binary (0 and 1, true and false, etc.). This type of data is encountered on a daily basis when working as a data scientist and here, you will learn how to build a logistic regression, how to understand tables, how to interpret the coefficients of a logistic regression, calculate the accuracy of the model, and how to test. We will introduce under and overfitting and will teach you how to test your models.
Cluster analysis is the most intuitive and important example of unsupervised learning. However, to be able to understand cluster analysis, we must first explore the mathematics behind it.
In this section, you will learn how to do Cluster analysis. Cluster analysus consists in dividing your data into separate groups based on an algorithm. Clustering is an amazing technique often employed in data science. But what’s more, often it makes much more sense to study patterns observed in a particular group rather than trying to find patterns in the entire dataset. We will provide several practical examples that will help you understand how to carry out cluster analysis and the difference between classification and clustering.
In previous sections, we focus extensively on k-means clustering as it is fastest and most efficient method for clustering. In this section, we explore other approaches that are less common.
This course is part of Module 3 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Expanding on your statistical and programming skills from Modules 1 and 2, Module 3 is designed to improve your programming skills and develop your advanced statistical thinking. You will learn how to build complete linear and logistic regression models, how to cluster data, and how to build deep learning models with TensorFlow 2.0.See All Modules
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The course is in-depth and is delivered at a steady pace with eye catching visuals. The instructors go through all the basics really well. They try not to over-simplify the material, you get a good sense аof how deep Data Science is in the course. Great job!!!
This course is amazing! After watching the video carefully and doing all the exercises, I am even capable of having discussions with Machine learning major Master’s students! High standard course with reasonable pricing.
Very clear and in-depth explanation of data science and how all the inter-related concepts apply in real life business environment. Absolutely great for beginners! Best data science course I have come across so far!
I would highly recommend the course to any beginner who wants to venture into the world of Data Science. The concepts are very well explained and there is an emphasis on practical application which really helps create a better understanding of the concepts.