• This article discusses five free AI courses and certifications that can help individuals gain the necessary knowledge and skills to stay relevant in today’s job market.
• The Machine Learning Specialization by DeepLearning.AI and Stanford Online is a foundational online program that provides a broad introduction to modern machine learning.
• Notable instructors in the specialization include Andrew Ng, Eddy Shyu, Aarti Bagul, and Geoff Ladwig.

Introduction

Learning artificial intelligence (AI) is becoming increasingly important for both technical and non-technical professionals, as it has the potential to revolutionize various industries and provide innovative solutions to complex problems. With free AI courses and online certifications, individuals can acquire the necessary knowledge and skills to stay relevant in today’s rapidly evolving job market.

Machine Learning Specialization

The Machine Learning Specialization by DeepLearning.AI and Stanford Online is a foundational online program that provides a broad introduction to modern machine learning. This three-course specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. Other notable instructors include Eddy Shyu, curriculum product manager at DeepLearning.AI; Aarti Bagul, a curriculum engineer; and Geoff Ladwig, another top instructor at DeepLearning.AI.

Course Content

The first course in the specialization is “Supervised Machine Learning: Regression and Classification,” which covers building machine learning models in Python using popular machine learning libraries NumPy and scikit-learn, as well as building and training supervised machine learning models for prediction tasks such as linear regression or binary classification tasks like logistic regression.

The second course is “Advanced Learning Algorithms,” which teaches students how to build neural networks with TensorFlow for multiclass classification tasks; apply best practices for machine learning development so that their models generalize better for real-world data; build decision trees; use tree ensemble methods including random forests; as well as boost trees for improved accuracy of predictions on unseen data points/tasks..

Finally, the third course covers unsupervised learning algorithms such as K-means clustering algorithm used for data segmentation purposes; hierarchical clustering used for generating insights from nested datasets; dimensionality reduction techniques used when dealing with high dimensional datasets; principal component analysis (PCA); t-distributed stochastic neighbor embedding (t-SNE); autoencoders used when dealing with missing values or outliers in datasets; deep generative models such as variational autoencoders (VAEs) used when dealing with high dimensional datasets or when creating synthetic data points from existing ones; reinforcement learning techniques used when trying to optimize a given policy based on rewards/punishments received over time upon taking certain actions inside an environment or system etc..

Conclusion

With these free AI courses offered by Deeplearning AI & Stanford Online are comprehensive enough for someone interested in getting started on their journey into Artificial Intelligence & Machine Learning domain & also helpful enough for experienced practitioners who want more practice & hands-on experience on some advanced topics within this field of study .