Fabrice djoukang tagong’s Post

Machine learning is a subfield of artificial intelligence 🤘 #AI #ML

I am thrilled to share that I have earned the Skillsoft® Digital Badge in Artificial Intelligence and Machine Learning! This achievement represents my dedication to advancing my knowledge and skills in these transformative fields. Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the way we interact with technology, solve problems, and create innovative solutions. As we continue to explore the potential of AI and ML, it's crucial to stay updated with the latest advancements and best practices.

About Machine Learning #SNSInstitutions #SNSDesignThinkers #DesignThinking Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on enabling systems to learn from data without being explicitly programmed. Here are some key points about ML: 1. Types of Machine Learning: Supervised Learning: The algorithm learns from labeled data, with input-output pairs provided for training. Unsupervised Learning: The algorithm learns patterns from unlabeled data, finding hidden structures or intrinsic relationships. Semi-supervised Learning: Combination of supervised and unsupervised learning, where the algorithm learns from a small amount of labeled data and a large amount of unlabeled data. Reinforcement Learning: The algorithm learns through trial and error, receiving feedback in the form of rewards or penalties to optimize its actions in a dynamic environment.

Sharing my Machine Learning Module Key Points: > Machine Learning Module: Machine Learning (ML) is a subset of Artificial Intelligence (AI) that employs algorithms and data to predict or forecast outcomes. ML, along with its more advanced form Deep Learning (DL), is widely used in AI applications and applied to various data types, including images, videos, text, and numerical data. > Types of Machine Learning ML can be broadly categorized into two primary types: > Supervised Learning: Applied when the true value of the prediction is known. Supervised learning is further divided into: >>Regression: Used when the target feature is a continuous variable. >>Classification: Used when the target feature is a discrete variable. Unsupervised Learning: Applied when there's no target variable to predict. >Unsupervised learning is used for tasks like: >>Clustering: Grouping data into similar clusters. >>Dimensionality Reduction: Reducing the number of features in a dataset. >>Anomaly Detection: Identifying unusual data points. >Linear Regression >>Linear Regression is a simple supervised ML model used for regression. It fits a straight line to the data and aims to minimize the error between the predicted line and the actual data points. >Overall Impression The session was excellent! Mahnoor Salman effectively explained the concepts and used visualizations to make the topics easier to understand. #MachineLearning #AI #DataScience #AtomCamp #Bootcamp #Atomcamp #SupervisedLearning #UnsupervisedLearning #LinearRegression #MahnoorSalman

Excited to share that I've earned a badge in Artificial Intelligence and Machine Learning! 🎓 These transformative fields are reshaping industries and unlocking endless possibilities. Grateful for the opportunity to dive deep into this fascinating realm and expand my skill set. #Artificial Intelligence #Ai #Artificialintelligence

🎓 Completed Certification: Introduction to Artificial Intelligence (AI) and Machine Learning (ML) 🎓 I am thrilled to announce that I have successfully completed the course "รู้จักปัญญาประดิษฐ์ (AI) และการเรียนรู้ของเครื่อง (ML)". This course provided me with a foundational understanding of artificial intelligence and machine learning, including essential tools, practical applications, and model evaluation techniques. I am excited to apply these new skills and knowledge to my future projects and continue my journey in the world of AI and ML. 🚀 #AI #MachineLearning #ArtificialIntelligence #ContinuousLearning #ProfessionalDevelopment

#Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. This course will demystify the world of artificial intelligence (AI) and machine learning (ML), taking you from foundational concepts to practical applications. You'll learn to distinguish AI and ML, explore how algorithms learn, and perform common tasks like classification and clustering. You will begin by learning to confidently distinguish between the broad umbrella of AI and the specific subset of ML, understanding how each contributes to the landscape of intelligent systems. Next, you'll explore the milestones that shaped AI. Then you will discover how to classify the diverse approaches of machine learning. Finally, you will explore the practical aspects of common machine learning problems. You'll learn the meaning of regression, classification, and clustering and how they're applied in real-world scenarios. Discover how to evaluate model performance and explore the workings of popular traditional models like linear regression and decision trees. You'll also be introduced to ensemble learning, where the "wisdom of the crowds" fuels even more accurate predictions. #AI #machinelearning https://lnkd.in/da6QK4jC

Hey!! 🎉 Excited to share that I've completed the "Fundamentals of AI & ML: Introduction to Artificial Intelligence" course! 🤖 This course has deepened my understanding of AI principles, machine learning algorithms, and their real-world applications. It's an exciting step forward in my journey to stay at the forefront of technology and innovation. Looking forward to applying these insights in future projects! 🚀 #AI #MachineLearning #ArtificialIntelligence #LifelongLearning

Very insightful and enriching session on AI and ML.

I've recently expanded my knowledge base by delving into the realm of #artificialintelligence (AI) and #machinelearning (ML). Through this endeavour, I have gained insights into distinguishing between #AI and #ML, as well as exploring the mechanisms by which algorithms acquire knowledge and execute fundamental tasks such as classification and clustering. #Accenture #Sikllsoft