Machine Learning, Data Science and Generative AI with Python Udemy Course
Complete hands-on machine learning and AI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks
New! Updated with extra content and activities on generative AI, transformers, GPT, ChatGPT, the OpenAI API, and self-attention based neural networks!
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!
If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice.
What you’ll learn in Machine Learning, Data Science and Generative AI with Python Course
- Build artificial neural networks with Tensorflow and Keras
- Implement machine learning at massive scale with Apache Spark’s MLLib
- Classify images, data, and sentiments using deep learning.
- Make predictions using linear regression, polynomial regression, and multivariate regression.
- Data Visualization with MatPlotLib and Seaborn
- Understand reinforcement learning – and how to build a Pac-Man bot.
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross validation to choose and tune your models.
- Build a movie recommender system using item-based and user-based collaborative filtering.
- Clean your input data to remove outliers.
- Design and evaluate A/B tests using T-Tests and P-Values
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