Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive deep into advanced machine learning techniques, including data mining, dimensionality reduction, reinforcement learning, and deep learning. You'll gain hands-on experience with tools like K-Nearest Neighbors, Principal Component Analysis, and Apache Spark while working with real-world datasets. The course emphasizes key machine learning concepts such as model evaluation, cross-validation, and handling unbalanced data. As you progress, you'll explore advanced neural networks like Convolutional and Recurrent Neural Networks, with practical applications such as sentiment analysis and handwriting recognition. Learn how to deploy models, use transfer learning, and understand the ethics behind machine learning and deep learning. This course is ideal for anyone with a basic understanding of machine learning who wants to advance their skills with real-world applications and big data tools. Gain the expertise needed to work with cutting-edge technologies in machine learning and deep learning. Ideal for data scientists, machine learning engineers, and anyone with a keen interest in AI and its real-world applications.

Syllabus

More Data Mining and Machine Learning Techniques

In this module, we will explore advanced machine learning techniques like K-Nearest Neighbors and Principal Component Analysis, applying them to practical data challenges. We will also dive into Reinforcement Learning and classifier performance evaluation, sharpening your understanding of how different algorithms can be used to solve real-world problems. Finally, you will gain hands-on experience through activities designed to reinforce these key concepts.

Dealing with Real-World Data

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.

Apache Spark: Machine Learning on Big Data

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.

Experimental Design / ML in the Real World

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.

Deep Learning and Neural Networks

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.