Machine Learning, Data Science and Generative AI with Python
What you will learn
Build generative AI systems with OpenAI, RAG, and LLM Agents
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
Course Gallery
Loading charts...
Comidoc Review
Our Verdict
This course is an excellent starting point for those with prior exposure to optimization techniques or programming. The instructor provides high-quality content and clear explanations, making it accessible and engaging. However, potential students should be aware that some course materials are outdated—a common issue in long-standing courses. Additionally, there is room for improvement regarding the consistency between video content and Jupyter notebooks. These issues could make the learning experience challenging for beginners. Despite these drawbacks, the comprehensive nature of this course, along with the practical examples and exercises, sets it apart as a valuable resource for those seeking to build a strong foundation in Machine Learning, Deep Learning, and Generative AI.
What We Liked
- Comprehensive introduction to Machine Learning, Deep Learning, and Generative AI
- High-quality instruction with clear explanations and concise content
- Practical examples and exercises enhance understanding of concepts
- Covers a wide array of topics, including data visualization, clustering, recommendation systems, and A/B testing
Potential Drawbacks
- Some course materials are outdated, causing issues with code and installations
- Inconsistencies between video content and Jupyter notebooks
- Minimal guidance on exercises using different data sets for practice
- Limited coverage of Generative AI and OpenAI