What you will learn
- Learn what Machine Learning is and meet Sebastian Thrun!
- Find out where Machine Learning is applied in Technology and Science.
- Use Naive Bayes with scikit learn in python.
- Splitting data between training sets and testing sets with scikit learn.
- Calculate the posterior probability and the prior probability of simple distributions.
- Learn the simple intuition behind Support Vector Machines.
- Implement an SVM classifier in SKLearn/scikit-learn.
- Identify how to choose the right kernel for your SVM and learn about RBF and Linear Kernels.
- Learn the formulas for entropy and information gain and how to calculate them.
- Decide how to pick the right Machine Learning Algorithm among K-Means, Adaboost, and Decision Trees.
- Understand how continuous supervised learning is different from discrete learning.
- Code a Linear Regression in Python with scikit-learn.
- Understand different error metrics such as SSE, and R Squared in the context of Linear Regressions.
- Remove outliers to improve the quality of your linear regression predictions.
- Identify the difference between Unsupervised Learning and Supervised Learning.
- Implement K-Means in Python and Scikit Learn to find the center of clusters.
- Apply your knowledge on the Enron Finance Data to find clusters in a real dataset.
- Understand how to preprocess data with feature scaling to improve your algorithms.
This Course will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.
This course is also a part of our Data Analyst Nanodegree.
How to Enroll Introduction to Machine Learning Course course?
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Introduction to Machine Learning Course Course coupon is limited to the first 1,000 enrollments. Click 'Enroll Now' to secure your spot and dive into this course on Udemy before it reaches its enrollment limits!
