edX
edXAI Skills: Basic and Advanced Techniques in Machine Learning
306 studentsINTERMEDIATEEnglish
Korshub's take
From Data Wrangling to Deep Learning
This comprehensive journey spans the full machine learning pipeline: starting with Python data preparation and visualization, then moving through supervised, unsupervised, and reinforcement learning paradigms. You'll implement algorithms using Scikit-learn, optimize pipelines, and graduate to deep neural networks.
The course balances theory with hands-on coding, covering clustering techniques like k-means, dimensionality reduction via PCA, and how reinforcement learning applies to real-world problems. By the end, you'll understand each model's strengths and limitations—crucial for choosing the right tool when real data isn't perfectly clean or labeled.
About this course
Apply common operations (pre-processing, plotting, etc.) to datasets using Python. ,Explain the concept of supervised, semi-supervised, unsupervised machine learning and reinforcement learning. ,Explain how various supervised learning models work and recognize their limitations. ,Analyze which factors impact the performance of learning algorithms. ,Apply learning algorithms to datasets using Python and Scikit-learn and evaluate their performance. ,Optimize a machine learning pipeline using Python and Scikit-learn. ,Describe the main classes of clustering techniques. ,Implement k-means and hierarchical clustering. ,Motivate the need and choice of dimensionality reduction techniques. ,Implement Principal Component Analysis (PCA) for feature extraction. ,Explain how deep neural networks work and their advantages. ,Train deep neural networks for classification and regression tasks. ,Explain the basic concepts and techniques of reinforcement learning. ,Describe how reinforcement learning could be applied in real world applications.
Price
$370
Advertisement
