In this module, we will focus on real-world challenges faced during data preprocessing, such as bias/variance tradeoffs, data cleaning, and handling missing or unbalanced data. You will also explore key techniques like K-Fold cross-validation, feature engineering, and outlier detection. Through hands-on activities, we will show you how to clean, transform, and normalize data to enhance the performance of your machine learning models.

In this module, we will introduce you to Apache Spark, a powerful tool for big data processing and machine learning. You will gain hands-on experience in installing Spark and using it to implement machine learning models with MLLib, including decision trees, K-Means clustering, and text search techniques. We will also explore the new DataFrame API and demonstrate how it enhances your ability to work with big data efficiently.

In this module, we will focus on applying machine learning techniques in the real world, specifically through experimental design methods like A/B testing. You will learn how to deploy machine learning models in production environments and measure the success of your experiments using statistical tools such as T-Tests and P-values. We will also cover the challenges of running experiments, including understanding test duration and avoiding common mistakes that can lead to incorrect conclusions.

In this module, we will delve deep into the world of neural networks and deep learning, covering everything from the basic principles and history to advanced techniques used in modern AI. You’ll get hands-on experience building and training neural networks using TensorFlow and Keras, including CNNs for image recognition and RNNs for sequence analysis. Additionally, we will explore key optimization methods, transfer learning, and discuss the ethical considerations surrounding the use of deep learning technologies.