Join us for an eye-opening webinar where the future of talent acquisition unfolds through the lens of AI and technology. GAITT, the Generative AI Think Tank, collaborates with Candidate-1st to offer you a session packed with insights, strategies, and forward-thinking approaches to recruitment. Why Attend? Embrace the Tech Wave: Learn how integrating AI […]
Future Proof Your Recruitment Embracing AI Read More »
What you’ll learn The basics of machine learning How to perform cross-validation to avoid overtraining Several popular machine learning algorithms How to build a recommendation system What is regularization and why it is useful Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques. Click
Free Course: Data Science: Machine Learning – Harvard University Read More »
What you’ll learn Gain hands-on experience and practice using Python to solve real data science challenges Practice Python coding for modeling, statistics, and storytelling Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems Build
Free Course: Introduction to Data Science with Python – Harvard University Read More »
SUMMARY: Many executives are surprised when previously successful leadership approaches fail in new situations, but different contexts call for different kinds of responses. Before addressing a situation, leaders need to recognize which context governs it—and tailor their actions accordingly. Snowden and Boone have formed a new perspective on leadership and decision making that’s based on
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They don’t make plans; they don’t solve problems; they don’t even organize people. What leaders really do is prepare organizations for change and help them cope as they struggle through it. Summary. Leadership is different from management, but not for the reasons most people think. Leadership isn’t mystical and mysterious. It has nothing to do
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In the past decade, the author has watched more than 100 companies try to remake themselves into better competitors. Their efforts have gone under many banners: total quality management, reengineering, right sizing, restructuring, cultural change, and turnarounds. In almost every case, the goal has been the same: to cope with a new, more challenging market
Leading Change: Why Transformation Efforts Fail Read More »
Summary. In the face of rapid, disruptive change, companies are realizing that managers can’t be expected to have all the answers and that command-and-control leadership is no longer viable. As a result, many firms are moving toward a coaching model in which managers facilitate problem-solving and encourage employees’ development by asking questions and offering support
The Leader as Coach Read More »
Want to learn the fundamentals of computer science? This is a good place to start.
You’ll learn fundamental concepts of building programs.
Taught via Python as the programming language.
Introduction to Computer Science and Programming in Python Read More »
Clustering—an unsupervised machine learning approach used to group data based on similarity—is used for work in network analysis, market segmentation, search results grouping, medical imaging, and anomaly detection. K-means clustering is one of the most popular and easy to use clustering algorithms. In this course, Fred Nwanganga gives you an introductory look at k-means clustering—how it works, what it’s good for, when you should use it, how to choose the right number of clusters, its strengths and weaknesses, and more. Fred provides hands-on guidance on how to collect, explore, and transform data in preparation for segmenting data using k-means clustering, and gives a step-by-step guide on how to build such a model in Python.
Machine Learning with Python: k-Means Clustering Read More »
You’ve probably heard about machine learning before, but have you ever wondered what that term really means? How does a machine learn? Have you thought about building a machine learning model, but didn’t know where to start? In this course, Frederick Nwanganga introduces machine learning in an approachable way and provides step-by-step guidance on how to get started with machine learning via the most in-demand language in use today, Python. Frederick starts with exactly what it means for machines to learn and the different ways they learn, then gets into how to collect, understand, and prepare data for machine learning. He also provides guided examples of how to accomplish each step using Python. Finally, he brings it all together to build, evaluate, and interpret the results of a machine learning model in Python.
Machine Learning with Python: Foundations Read More »
