Machine Learning Specialization (Stanford)

Colleagues, in the “Machine Learning Specialization” from Stanford University and DeepLearning.AI you will master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, 3-course program by AI visionary Andrew Ng. Learn to build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression), train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods, apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection, and build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model. Gain high-demand skill in Logistic Regression, Artificial Neural Network, Linear Regression, Decision Trees and Recommender Systems. The four training modules include: 1) Supervised Machine Learning: Regression and Classification, 2) Advanced Learning Algorithms, 3) Unsupervised Learning, Recommenders, and 4) Reinforcement Learning.

Enroll today (teams & executives are welcome): imp.i384100.net/XYbQbo  

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) (Kindle)

Much career success, Lawrence E. Wilson - AI Academy (share with your team)