Advanced Learning Algorithms (training)

Colleagues, in the “Advanced Learning Algorithms” program you will learn to build and train a neural network with TensorFlow to perform multi-class classification. Apply best practices for machine learning development so that your models generalize to data and tasks in the real world. And use decision trees and tree ensemble methods, including random forests and boosted trees. Gain highly marketable skills in Decision Tree Learning, Applied Machine Learning, Machine Learning Algorithms, Logistic Regression, Deep Learning, Model Evaluation, Model Training, Transfer Learning, Model Optimization,  Random Forest Algorithm, Data Ethics, Regression Analysis, Responsible AI, Supervised Learning, and Artificial Neural Networks. You will also acquire hands-on experience with Tensorflow and Classification Algorithms. 

Skill-based training modules address: 1) Neural Networks - learn about neural networks and how to use them for classification tasks. You'll use the TensorFlow framework to build a neural network with just a few lines of code. Then, dive deeper by learning how to code up your own neural network in Python, "from scratch". Optionally, you can learn more about how neural network computations are implemented efficiently using parallel processing (vectorization); 2) Neural Networks - you'll learn how to train your model in TensorFlow, and also learn about other important activation functions (besides the sigmoid function), and where to use each type in a neural network. You'll also learn how to go beyond binary classification to multiclass classification (3 or more categories). Multiclass classification will introduce you to a new activation function and a new loss function. Optionally, you can also learn about the difference between multiclass classification and multi-label classification. You'll learn about the Adam optimizer, and why it's an improvement upon regular gradient descent for neural network training, 3) Advice for Applying Machine Learning -  learn best practices for training and evaluating your learning algorithms to improve performance. This will cover a wide range of useful advice about the machine learning lifecycle, tuning your model, and also improving your training data, and 4) Decision Trees -  you'll learn about a practical and very commonly used learning algorithm the decision tree. You'll also learn about variations of the decision tree, including random forests and boosted trees (XGBoost). 

Enroll today (teams and execs are welcome): https://imp.i384100.net/POKMKj

Recommended Reading:

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

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

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)