Data Science Specialization Training – Johns Hopkins University

Colleagues, this Data Science Specialization from one of America’s top universities will equip you to use R to clean, analyze, and visualize data, Use GitHub to manage data science projects, Navigate the entire data science pipeline from data acquisition to publication, and Perform regression analysis, least squares and inference using regression models. This Specialization includes ten courses: Data Scientist’s Toolbox, R Programming, Cleaning Data, Exploratory Data Analysis, Reproducible Data, Statistical Inference, Regression Models, Practical Machine Learning, Developing Data Products and a Data Science Capstone Project.

Register today at: https://fxo.co/7ux5

Much career success, Lawrence Wilson –  Artificial Intelligence Academy

Machine Learning Specialization (University of Washington)

Software Developers & Engineers, are you ready to Build Intelligent Applications? Master machine learning fundamentals in four hands-on courses. This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. There are 4 Courses in this Specialization which include: 1) Machine Learning Foundations: A Case Study Approach, 2) Machine Learning: Regression, 3) Machine Learning: Classification, and 4) Machine Learning: Clustering & Retrieval. You will gain high demand, marketable skills in Data Clustering Algorithms, Machine Learning, Classification Algorithms, Decision Tree.

Register today at https://fxo.co/7pic

Much career success, Lawrence Wilson – Artificial Intelligence Academy

Programming Foundations of Classification and Regression (Machine Learning with Python series)

Colleagues, this video-based training program includes over four hours of Code-along sessions move you from introductory machine learning concepts to concrete code. Machine learning is moving from futuristic AI projects to data analysis on your desk. Code-along sessions move you from introductory machine learning concepts to concrete code. Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond nodding along in discussion to coding machine learning tasks. These videos show you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends.You will learn how to Build and apply simple classification and regression models, Evaluate learning performance with train-test splits, Evaluate learning performance with metrics tailored to classification and regression, and Evaluate the resource usage of your learning models. Core skills-based training modules will equip you with: 1) Software Background (Numpy, Matplotlib, scikit-learn, seaborn, and pandas–high-level packages), 2) Mathematical Background, 3) Beginning Classification (Part I): Two models: k-nearest neighbors and naive Bayes, 4) Beginning Classification (Part II): Evaluate learning performance with accuracy and how to evaluate resource utilization for memory and time within Jupyter notebooks and also in standalone Python scripts, and 5) Beginning Regression (Parts I and II).

Register today at: https://tinyurl.com/y77nvs7m

Career success awaits you, Lawrence Wilson – Artificial Intelligence Academy