In this Machine Learning Capstone course, you will be using various Python-based machine learning libraries such as Pandas, scikit-learn, Tensorflow/Keras, to:
Dieser Kurs ist Teil der Spezialisierung IBM Machine Learning Zertifikat über berufliche Qualifikation
von


Über diesen Kurs
Comfort with Python and completion of the prerequisite IBM Machine Learning Professional Certificate.
Was Sie lernen werden
Compare and contrast different machine learning algorithms by creating recommender systems in Python
Develop a final project using machine learning methods and evaluate your peers’ projects
Predict course ratings by training a neural network and constructing regression and classification models
Create recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering
Kompetenzen, die Sie erwerben
- Artificial Neural Network
- Python Programming
- Data Analysis
- Supervised Learning
- unsupervised machine learning
Comfort with Python and completion of the prerequisite IBM Machine Learning Professional Certificate.
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IBM Skills Network
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Lehrplan - Was Sie in diesem Kurs lernen werden
Capstone Overview
In this module, you will be introduced to the idea of recommender systems in the first video. All labs in subsequent modules are based on this concept. You will also be provided with an overview of the capstone project. In the last two exercises, you will obtain an IBM Cloud feature code and use that code to create an IBM Watson Studio account.
Exploratory Data Analysis and Feature Engineering
In module 2, you will perform exploratory data analysis to find preliminary insights such as data patterns. You will also use it to check assumptions with the help of summary statistics and graphical representations of online course-related data sets such as course titles, course genres, and course enrollments. Next, you will extract a word-count vector called a “bag of words” (BoW) from course titles and descriptions. The BoW feature is probably the simplest but most effective feature characterizing textual data. It is widely used in many textual machine learning tasks. Finally, you will apply the cosine similarity measurement to calculate the course similarity using the extracted BoW feature vectors.
Unsupervised-Learning Based Recommender System
In module 3, you will create three course recommendation systems using different methods. In lab 1, you will create a course recommendation system based on user profile and course genre matrices by computing an interest score for each course and recommend the courses with the highest interest scores. In the second lab, you will generate a course similarity matrix to create the recommendation system. In the third lab, you will implement a clustering-based recommender system algorithm using K-means clustering and principal component analysis based on group members’ course enrollment history. In labs four and five you will use collaborative filtering to make predictions about a user’s interest based on a collection of other users’ similar preferences. In lab 4, you will perform KNN-based collaborative filtering and in lab 5, you will use non-negative matrix factorization.
Supervised-Learning Based Recommender Systems
In this module, you will predict course ratings using neural networks. In the first lab, you will train neural networks to predict course ratings while simultaneously extracting users' and items' latent features. In lab 2, you will be given course interaction feature vectors as input data. Using regression analysis, you will calculate numerical rating scores that predict whether a student will audit or complete a course. Lab 3 is similar to lab 2 but instead of using regression you will use a classification model. You will extract user and item embedding feature vectors from a neural network. With those embedding feature vectors, you will create an interaction feature vector and use that to build a classification model. The model maps the interaction feature vector to a rating mode that predicts whether a learner will audit or complete a course.
Über den IBM Machine Learning Zertifikat über berufliche Qualifikation
Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics.

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