Advanced Predictive Techniques With Scikit-Learn & TensorFlow

Colleagues, the Advanced Predictive Techniques With Scikit-Learn And TensorFlow program will equip you to use ensemble methods to improve accuracy in classification and regression problems. Ensemble methods improve prediction accuracy by combining in a clever way predictions from many individual predictors. Artificial Neural Networks are models loosely based on how neural networks work in a living being. This course introduces the use of Deep Learning models for Predictive Analytics using the powerful TensorFlow library. Learn to use ensemble algorithms to combine many individual predictors to produce better predictions, apply advanced techniques such as dimensionality reduction to combine features and build better models, evaluate models and choose the optimal hyper-parameters using cross-validation, grasp the foundations for working and building models using Neural Networks and understand different techniques to solve problems that arise when doing Predictive Analytics in the real world. Training modules include: 1) Ensemble Methods for Regression and Classification, 2) Cross-Validation and Parameter Tuning, 3) Working with Features, 4) Introduction to Artificial Neural Networks and TensorFlow, and 5) Predictive Analytics with TensorFlow and Deep Neural Networks.  

Enroll today (individuals & teams welcome): https://tinyurl.com/4tyd7vjc 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Implementing Deep Learning Algorithms with TensorFlow 2.0

Colleagues, the Implementing Deep Learning Algorithms with TensorFlow 2.0 provides you with a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems. Learn various Deep Learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2.0. You will program a model to classify breast cancer, predict stock market prices, process text as part of Natural Language Processing (NLP),  Skill-based training modules include: 1)  Deep Learning Introduction and Environment Setup, 2) Building First Neural Network for Tabular Data with TensorFlow 2.0, 3) Convolutional Neural Networks with TensorFlow 2.0, 4) Recurrent Neural Network with TensorFlow 2.0, 5) Long Short-Term Memory Networks (LSTM), and 6) Transfer Learning with TensorFlow 2.0.  Basic knowledge of Python is assumed. You will understand what problems Deep Learning and TensorFlow 2.0 have solved and can solve, various Deep Learning model architectures and work with them, apply neural network models, deep learning, NLP, and LSTM to several diverse data classification scenarios, including breast cancer classification; predicting stock market data for Google; classifying Reuters news topics; and classifying flower species.

Enroll today (individuals & teams welcome): https://tinyurl.com/a9utjzxc 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Google Cloud Machine Learning with TensorFlow

Colleagues, the Google Cloud Machine Learning with TensorFlow equips you to use Google Cloud to train TensorFlow models and use them to predict results for multiple users. You will learn to efficiently train neural networks using large datasets and to serve your training models. You will use the power of Google's Cloud Platform to train deep neural networks faster. Learn to run predictions for your model using the cloud. You will also explore topics such as cloud infrastructures, distributed training, serverless technologies, model serving. Finally, you will learn to use GPUs organized in clusters to optimize your performance, train bigger models faster using the Google Cloud infrastructure, explore machine types and learn how to configure clusters to solve problems, train deep learning models using the Google Cloud AI Platform, run classical machine learning algorithms with TensorFlow, and execute your trained models to get predictions using the Artificial Intelligence Platform API. The program includes 40 lectures spanning the following training modules: 1) A Quick Start with Google Cloud Platform, 2) Machine Learning with TensorFlow Fundamentals, 3) Basic Model Training with TensorFlow 2.0, 4) Advanced Model Training with TensorFlow 2.0, 4) Serving Model Predictions with TensorFlow on GCP, and 6) Neural  Networks.

Enroll today (individuals & teams welcome): https://tinyurl.com/vj957fya 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Introduction to Deep Learning (and Neural Networks)

Colleagues, the Introduction to Deep Learning program will equip you in all the important concepts relating to deep learning models and how they give rise to the recent results in AI. We use guided examples and discuss a variety of practical applications, all accompanied by animations and visualizations. We also cover recent breakthroughs in deep learning research. This course will demystify the models that underpin the recent AI revolution and provide a solid foundation for further learning. Skill-based training modules include: 1) Fundamentals, 2) Perceptron: Weights, Biases, Activation Functions, 3) Multi-neuron Networks : XOR and nonlinearity, and 4)  Learning: Gradient Descent. After taking this course you will understand What deep learning is and how it  differs from other types of machine learning and artificial intelligence, How deep learning models use neural networks to make computations, What types of problems deep learning models can be used to solve, Types of data needed to train deep learning models, Variety of inputs deep  learning models receive and solutions they produce, Advantages that deep learning can offer over traditional machine learning, Why multi-neuron networks are able to solve complex problems, How neural networks use gradient descent and back-propagation to learn to make predictions.

Enroll today (individuals & teams welcome): https://tinyurl.com/phdve8xj 

Much career success, Lawrence E. Wilson - Online Learning Central