Become a data scientist in the tech industry! Comprehensive data mining and machine learning course with Python and Spark
Key Features
- Take your first steps in the world of data science by understanding the tools and techniques of data analysis
- Train efficient machine learning models in Python using the supervised and unsupervised learning methods
- Learn how to use Apache Spark for processing big data efficiently
Book Description
This course begins with a Python crash course and then guides you on setting up Microsoft Windows-based PCs, Linux desktops, and Macs. After the setup, we delve into machine learning, AI, and data mining techniques, which include deep learning and neural networks with TensorFlow and Keras; generative models with variational autoencoders and generative adversarial networks; data visualization in Python with Matplotlib and Seaborn; transfer learning, sentiment analysis, image recognition, and classification; regression analysis, K-Means Clustering, Principal Component Analysis, training/testing and cross-validation, Bayesian methods, decision trees, and random forests.
Additionally, we will cover multiple regression, multilevel models, support vector machines, reinforcement learning, collaborative filtering, K-Nearest Neighbors, the bias/variance tradeoff, ensemble learning, term frequency/inverse document frequency, experimental design, and A/B testing, feature engineering, hyperparameter tuning, and much more! There's a dedicated section on machine learning with Apache Spark to scale up these techniques to "big data" analyzed on a computing cluster.
The course will cover the Transformer architecture, delve into the role of self-attention in AI, explore GPT applications, and practice fine-tuning Transformers for tasks such as movie review analysis. Furthermore, we will look at integrating the OpenAI API for ChatGPT, creating with DALL-E, understanding embeddings, and leveraging audio-to-text to enhance AI with real-world data and moderation.
What you will learn
- Implement machine learning on a massive scale with Apache Spark's MLLib
- Data visualization with Matplotlib and Seaborn
- Understand reinforcement learning and how to build a Pac-Man bot
- Use train/test and K-Fold cross-validation to choose and tune models
- Build artificial neural networks with TensorFlow and Keras
- Design and evaluate A/B tests using T-Tests and P-Values
Who this book is for
Software developers or programmers who want to transition into the lucrative data science career path will learn a lot from this course. Data analysts in finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools.
You will need some prior experience in coding or scripting to be successful. If you have no prior coding or scripting experience, you should not take this course as we have covered the introductory Python course in the earlier sections.
