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Updated in [June 30th, 2023]
This Introduction to Machine Learning Course provides an overview of the fundamentals of machine learning. Students will learn how to extract and identify useful features that best represent data, understand a few of the most important machine learning algorithms, and evaluate the performance of machine learning algorithms. The course also covers the end-to-end process of investigating data through a machine learning lens. This course is part of the Data Analyst Nanodegree.
[Applications]
The application of the Introduction to Machine Learning Course can be seen in many areas. It can be used to develop predictive models for various industries, such as healthcare, finance, and retail. It can also be used to create automated systems for data analysis and decision-making. Additionally, it can be used to develop algorithms for natural language processing, image recognition, and other areas of artificial intelligence. Finally, it can be used to create machine learning-based applications for mobile devices.
[Career Path]
One job position path that learners can pursue after taking this course is a Machine Learning Engineer. A Machine Learning Engineer is responsible for developing and deploying machine learning models and algorithms to solve real-world problems. They must have a strong understanding of data science and machine learning concepts, as well as the ability to develop and implement machine learning models. They must also be able to evaluate the performance of the models and make improvements as needed.
The development trend for Machine Learning Engineers is very positive. As more and more companies are recognizing the value of machine learning, the demand for Machine Learning Engineers is increasing. Companies are looking for engineers who can develop and deploy machine learning models that can help them gain insights and make predictions from their data. As the demand for machine learning increases, so does the demand for Machine Learning Engineers.
[Education Path]
The recommended educational path for learners interested in Information Systems Auditing Controls and Assurance is to pursue a Bachelor's degree in Information Systems, Information Technology, or Computer Science. This degree will provide learners with the foundational knowledge and skills needed to understand the principles of IS auditing, controls, and assurance.
The Bachelor's degree will cover topics such as computer programming, database management, systems analysis, network security, and software engineering. Learners will also gain an understanding of the principles of IS auditing, including risk assessment, control design, and audit procedures. Additionally, learners will learn about the legal and ethical implications of IS auditing, as well as the importance of maintaining data privacy and security.
The development trend for Information Systems Auditing Controls and Assurance is to focus on emerging technologies such as Big Data, FinTech, and Virtual Banks. As these technologies become more prevalent, the need for IS auditors to ensure the integrity of these systems will become increasingly important. Additionally, the need for IS auditors to understand the legal and ethical implications of IS auditing will become more important as organizations become more reliant on technology. As a result, the demand for IS auditors with the necessary skills and knowledge to audit these systems will continue to grow.
Course Syllabus
Welcome to Machine Learning
Learn what Machine Learning is and meet Sebastian Thrun!,Find out where Machine Learning is applied in Technology and Science.Naive Bayes
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.Support Vector Machines
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.Decision Trees
Code your own decision tree in python.,Learn the formulas for entropy and information gain and how to calculate them.,Implement a mini project where you identify the authors in a body of emails using a decision tree in Python.Choose your own Algorithm
Decide how to pick the right Machine Learning Algorithm among K-Means, Adaboost, and Decision Trees.Datasets and Questions
Apply your Machine Learning knowledge by looking for patterns in the Enron Email Dataset.,You'll be investigating one of the biggest frauds in American history!Regressions
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.Outliers
Remove outliers to improve the quality of your linear regression predictions.,Apply your learning in a mini project where you remove the residuals on a real dataset and reimplement your regressor.,Apply your same understanding of outliers and residuals on the Enron Email Corpus.Clustering
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.Feature Scaling
Understand how to preprocess data with feature scaling to improve your algorithms.,Use a min mx scaler in sklearn.
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