3.1 Machine Learning using Python in cloud environment -and- 1:1 use case support to apply learning (15-25 hours)

3.1 Machine Learning using Python in cloud environment (15-25 hours)   Machine Learning using Python in cloud environment -and- 1:1 use case support to apply learning (15-25 hours) Objectives of course will enable participants to select, recommend and perform machine learning using Python in cloud. Six-Seven (6-7) 2.5 hours sessions; 2.0 hour session integrated with coding exercises; 0.5 hour summary session to apply learning. Material provided in MS Powerpoint, Google Slides and Python Google Colab environment with prepared code; see enrollment email for instructions. Complimentary 4-hour 1:1 applied learning / use case support included; additional support can be purchased in 4-week blocks from www.analyticsforliving.org. Session 1:  Review foundational Statistics and Python, Python Packages and Cloud environments Statistics - review foundational statistics and statistical tools which will be applied for machine learning. Python package review - review and application of statistical, data preparation, advanced python and introduction to machine learning.  Review outline of course including preparation, homework, use cases and expected learning objectives; Machine, Supervised, Unsupervised, and Ensemble Learning.   Session 2:  Machine Learning overview and introduction to Supervised, Unsupervised and Ensemble learning Session 3-4:  Machine Learning:  Supervised Learning Regression - linear, polynomial, ridge/lasso. Classification - decision trees, logistic, naive bayes, K-NN (k-nearest neighbor), SVM (support vector machines). Session 4-6:  Machine Learning:  Unsupervised Learning Clustering - k-means, hierarchical. Dimensionality - Dimensionality reduction - t-SNE (t-distributed stochastic neighbor embedding), PCA (principal component analysis), LSA (latent semantic analysis), LDA (linear discriminant analysis).  Session 7-8:  Machine Learning:  Ensemble Learning Stacking - improve predictive performance, reduce bias, variance by merging results of base models. Bagging - random forest or bagging with equal contributions to reduce complexity that overfit training data. Boosting - AdaBoost (adaptive with different contributions), LightGBM (light gradient machine), XGBoost (extreme gradient). Session 7-8:  Machine Learning:  Use case application Use case support to apply learning to business challenges using illustrative data to support your application and success.  Employing the Advanced Python packages with focus on machine learning. Scikit Learn - implement machine learning models and statistical modeling including regression, classification, clustering and statistical tools.  1:1 Sessions:  Use Case support to apply Machine Learning 1:1 use case support to apply learning to your custom business challenges using illustrative data to support your application and success.  Support available in your time zone and able to connect via google live meeting. (4-hours within 4 weeks of purchase) Note: Prerequisite 1.1 Statistics for Data Science with Python, 2.1 Advanced Python Pandas NumPy Submit enrollment for customized quote of business needs.  Proposal and introductory meeting will be scheduled within 24-48 hours. This 15-25 hour course provides a comprehensive introduction to Machine Learning using Python in a cloud environment. An experienced instructor offers one-on-one use case support to hone your skills and apply the learning. Gain the practical tools and confidence to begin developing Machine Learning applications. Learn Machine Learning using Python in the cloud with 15-25 hours of 1:1 use case support to ensure you can apply your learning. Perfect for those wanting hands-on expert support to achieve their goals. Learn to apply machine learning to real-world problems with this 15-25 hour online course. Gain the skills to select, recommend, and perform machine learning using Python in the cloud environment. Benefit from the included 1:1 use case support to help you succeed. Access deep learning knowledge and apply it with confidence using this 15-25 hour comprehensive program. Featuring 6-7, 2.5-hour sessions, guided coding exercises, and a 0.5-hour summary session to reinforce learning, this course offers real-world applications of Python in cloud environments. Get the guidance you need to bring your machine learning skills to the next level! This comprehensive program offers 15-25 hours of machine learning support, including material in PowerPoint and Google Slides, as well as access to a Python Google Colab environment with prepared code. Get 1:1 use case support to apply the learning and get the most from the program. Learn to build and deploy machine learning models with confidence, using this comprehensive program featuring 15-25 hours of training in a cloud environment and 4 hours of 1:1 use case support included. Additional support is available in 4-week blocks from www.analyticsforliving.org. Session 1:  Review foundational Statistics and Python, Python Packages and Cloud environments. Practitioners can gain knowledge and confidence to apply machine learning to real world problems with this course. It covers a comprehensive introduction to Python and the cloud environment, foundation of statistics, and use of Python packages for machine learning. It includes 1:1 use case support, with up to 15-25 hours of guidance provided for in-depth understanding and practical application. Session 2:  Machine Learning overview and introduction to Supervised, Unsupervised and Ensemble learning. Take your python skills to the next level by learning machine learning concepts, supervised learning, unsupervised learning, and ensemble learning with 3.1 Machine Learning using Python in cloud environment. Benefit from 15-25 hours of 1:1 use case support to apply your learning. Session 3-4:  Machine Learning:  Supervised Learning. This 15-25 hour machine learning program provides a comprehensive introduction to supervised learning with Python in a cloud environment. It covers a wide range of topics from linear and polynomial regression to K-Nearest Neighbors and Support Vector Machines. With 1:1 use case support, you can apply what you've learned and hone your skills. Session 5-6:  Machine Learning:  Unsupervised Learning. Learn unsupervised machine learning techniques with 3.1 Machine Learning using Python in cloud environment. Benefit from 1:1 use case support to apply learning across topics such as clustering, hierarchical, and dimensionality reduction. Study k-means, t-SNE, PCA, LSA, and LDA in a maximum of 25 hours.  Session 7-8:  Machine Learning:  Ensemble Learning. Tap into the power of machine learning with this comprehensive course. Learn to apply ensemble learning techniques such as stacking, bagging and boosting to improve predictive performance. Practice with popular algorithms like AdaBoost, and XGBoost. Get 1:1 use case support to help you apply your new skills. Session 7-8:  Machine Learning:  Use case application. Advance your knowledge of machine learning with this comprehensive program combining Python programming in the cloud, and 1:1 use case support to help apply your learning. Session 7-8 focuses on building and applying machine learning models, utilizing Scikit Learn packages. Get hands-on experience with regression, classification, clustering and more, along with the processing tools you need to make the most of your data. 1:1 Sessions:  Use Case support to apply Machine Learning. Use this course to learn how to use Machine Learning with Python in a cloud environment, and receive 1:1 use case support to apply your learning. Get personalized assistance in your time zone via Google Live Meeting, with 4 hours of support within 4 weeks of purchase. Unlock custom business solutions with confidence. This comprehensive course provides 15 to 25 hours of real-world training in Machine Learning using Python in a cloud environment. Students also receive customized 1:1 use case support to apply their knowledge. Submit an enrollment to receive a personalized quote that meets your business requirements. Proposals and an introductory meeting will be scheduled within 24 to 48 hours. This course provides 15-25 hours of instruction on applying machine learning techniques using Python in the cloud environment. It combines in-depth technical instruction with 1:1 use case support, and it's designed to be used as a follow-up to 1.1 Statistics for Data Science with Python and 2.1 Advanced Python Pandas NumPy.