Search result for Model training and evaluation Online Courses & Certifications
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Build Regression, Classification, and Clustering Models
by Anastas Stoyanovsky- 0.0
Approx. 20 hours to complete
Models make decisions, predictions—anything that can help the business understand itself, its customers, and its environment better than a human could. You need to know how to select the best algorithm for a given job, and how to use that algorithm to produce a working model that provides value to the business....
Financial Modeling
by Dmitry Ryabykh- 4.7
Approx. 13 hours to complete
The financial model in MS Excel is the most important tool for planning, structuring, and analysis of a project. All stages the development of the model will be supplemented with Excel files, so by the end of the course you will have both new skills and ready professional financial models. Analysis and presentation....
Writing Professional Email and Memos (Project-Centered Course)
by Tamara Michele Powell , Tiffani Kristine Reardon- 4.3
Approx. 20 hours to complete
There will be thousands of learners working side-by-side on their projects, and the environment will be social, supportive, and constructive. - write clear and concise emails/memos relative to their professional endeavors - recognize five different types of emails/memos and their formats - analyze email/memo context for audience and tone Evaluation Model...
Machine Learning Rapid Prototyping with IBM Watson Studio
by Mark J Grover , Meredith Mante- 4.6
Approx. 9 hours to complete
An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. Evaluation measures for models Python and scikit-learn library (including Pipeline class) Automated Data Preparation and Model Selection Evaluation and Deployment of AutoAI-generated Solutions...
Design Thinking and Predictive Analytics for Data Products
by Julian McAuley , Ilkay Altintas- 4.5
Approx. 8 hours to complete
This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python. Review: Classification and K-Nearest Neighbors Review: Logistic Regression and Support Vector Machines Introduction to Training and Testing Review: Classification and Training...
Machine Learning: Regression
by Emily Fox , Carlos Guestrin- 4.8
Approx. 22 hours to complete
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,. Irreducible error and bias Variance and the bias-variance tradeoff Training/validation/test split for model selection, fitting, and assessment Balancing fit and magnitude of coefficients...
Data Science Capstone
by Jeff Leek, PhD , Roger D. Peng, PhD , Brian Caffo, PhD- 4.5
Approx. 6 hours to complete
Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners. Overview, Understanding the Problem, and Getting the Data Task 1 - Getting and cleaning the data Exploratory Data Analysis and Modeling Prediction Model Final Project Submission and Evaluation Build an efficient and accurate prediction model...
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Introduction to Deep Learning
by Evgeny Sokolov , Зимовнов Андрей Вадимович , Alexander Panin , Ekaterina Lobacheva , Nikita Kazeev- 4.5
Approx. 34 hours to complete
The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Overfitting problem and model validation Training tips and tricks for deep CNNs...
Image Understanding with TensorFlow on GCP
by Google Cloud Training- 4.6
Approx. 12 hours to complete
We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. Linear and DNN Models CNN Model Parameters Lab Intro: Training with Pre-built ML Models using Cloud Vision API and AutoML Lab Solution: Training with Pre-built ML Models using Cloud Vision API and AutoML...
Introduction to Trading, Machine Learning & GCP
by Jack Farmer , Ram Seshadri- 4
Approx. 9 hours to complete
This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. Choosing the right model and BQML - part 1 Choosing the right model and BQML - part 2 Validation and Training Data Splits...