AI Metrics & Evaluation

Unlock the fast track to machine learning mastery with our comprehensive course, "Hands-on Machine Learning in Python & ChatGPT." Dive deep into hands-on tutorials utilizing essential tools like Pandas, Numpy, Seaborn, Scikit-Learn, Python, and the innovative capabilities of ChatGPT. This course is designed to guide you seamlessly through every stage of the machine learning process, ensuring a complete workflow that empowers you to tackle tasks such as data cleaning, manipulation, preprocessing, and the development of powerful supervised and unsupervised machine learning models.In this immersive learning experience, gain proficiency in crafting supervised models, including Linear Regression, Logistic Regression, Random Forests, Decision Trees, SVM, XG Boost, and KNN. Unleash the power of unsupervised models like KMeans and DBSCAN for cluster analysis. The course is strategically structured to enable you to navigate through these complex concepts swiftly, effortlessly, and with precision.Our primary objective is to equip you with the skills to build machine learning models from scratch, leveraging the combined strength of Python and ChatGPT. You will not only learn the theoretical foundations but also engage in practical exercises that solidify your understanding. By the end of the course, you'll have the expertise to measure the accuracy and performance of your machine learning models, enabling you to make informed decisions and select the best models for your specific use case.Whether you are a beginner eager to enter the world of machine learning or an experienced professional looking to enhance your skill set, this course caters to all levels of expertise. Join us on this learning journey, where efficiency meets excellence, and emerge with the confidence to tackle real-world machine learning challenges head-on. Fast-track your way to becoming a proficient machine learning practitioner with our dynamic and comprehensive course.

Machine learning brings together computer science and statistics to build smart, efficient models. Using powerful techniques offered by machine learning, you’ll tackle data-driven problems. The effective blend of Machine Learning with Python, Scikit-Learn, and TensorFlow, helps in implementing solutions to real-world problems as well as automating analytical model. This comprehensive 3-in-1 course is your one-stop solution in mastering machine learning algorithms and their implementation. Learn the fundamentals of machine learning and build your own intelligent applications. Explore popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Contents and Overview This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python, Scikit-Learn and TensorFlow. The first course, Step-by-Step Machine Learning with Python, covers easy-to-follow examples that get you up and running with machine learning. In this course, you’ll learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. You’ll build your own models from scratch. The second course, Machine Learning with Scikit-Learn, covers effective learning algorithms to real-world problems using Scikit-Learn. You’ll build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use Scikit-Learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. The third cou

If you want to learn the process to detect whether a person is wearing a face mask using AI and Machine Learning algorithms then this course is for you.In this course I will cover, how to build a Face Mask Detection model to detect and predict whether a person is wearing a face mask or not in both static images and live video streams with very high accuracy using Deep Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model using CNNs and OpenCV.This course will walk you through the initial data exploration and understanding, Data Augumentation, Data Generators, customizing pretrained Models like Mobile NetV2, model building and evaluation. Then using the trained model to detect the presence of face mask in images and video streams.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1  :  Project Overview.Task 2  :  Introduction to Google Colab.Task 3  :  Understanding the project folder structure.Task 4  :  Understanding the dataset and the folder structure.Task 5  :  Loading the data from Google Drive.Task 6  :  Importing the Libraries.Task 7  :  About Config and Resize File.Task 8  :  Some common Methods and Utilities Task 9  :  About Data Augmentation.Task 10 :  Implementing Data Augmentation techniques.Task 11 :  About Data Generators.Task 12 :  Implementing Data Generators.Task 13 :  About Convolutional Neural Network (CNNs).Task 14 :  About OpenCV.Task 15 :  Understanding pre-trained models.Task 1

In this course, you will get to understand the foundational concepts that underlie the supervised machine-learning process. You will get to understand complex topics such as:Exploratory Data Analysis, Data Transformation and Feature Scaling, Evaluation Metrics, Algorithms, trainers, and models,Underfitting and Overfitting, Cross-validation, Regularization, and much more You will see these concepts come alive by doing a practical machine-learning exercise, rather than by looking at presentations. We will be using a non-cloud-based machine-learning tool called Model Builder, inside of Visual Studio. There will be zero coding involved (except for the very last lesson). But even though there is little coding involved, you will still get a very detailed understanding of complex machine-learning concepts.This course requires you to have at least some theoretical exposure to the concepts of supervised and unsupervised machine learning. This course is designed to build on a basic, theoretical understanding of machine learning by doing a practical machine-learning exercise. The concepts taught in this course are foundational and will be relevant in the future, regardless of what machine learning platform or programming language you use. In the process, you will also get some exposure to Visual Studio, code projects, solutions, and the Microsoft Machine Learning ecosystem. But that is just a side benefit. This course focuses on machine learning itself, not the tools that are used.If you've already done any kind of machine learning or trained a model, this course might be too basic for you. This course may contain foundational knowledge that you may not have been taught before, but please be aware that this course is geared toward beginner and intermediate-level AI enthusiasts.

Description Take the next step in your career! Whether you’re an up-and-coming professional, an experienced executive, Data Scientist Professional. This course is an opportunity to sharpen your Python and ML DL capabilities, increase your efficiency for professional growth and make a positive and lasting impact in the Data Related work.With this course as your guide, you learn how to:All the basic functions and skills required Python Machine Learning Transform DATA related work Make better Statistical Analysis and better Predictive Model on unseen Data.Get access to recommended templates and formats for the detail’s information related to Machine Learning And Deep Learning.Learn useful case studies, understanding the Project for a given period of time. Supervised Learning, Unsupervised Learning , ANNs,CNNs,RNNs with useful forms and frameworks Invest in yourself today and reap the benefits for years to come The Frameworks of the Course Engaging video lectures, case studies, assessment, downloadable resources and interactive exercises. This course is created to Learn about Machine Learning and Deep Learning, its importance through various chapters/units. How to maintain the proper regulatory structures and understand the different types of Regression and Classification Task. Also to learn about the Deep Learning Techniques and the Pre Trained Model.Data Preprocessing will help you to understand data insights and clean data in an organized manner, including responsibilities related to Feature Engineering and Encoding Techniques. Managing model performance and optimization will help you understand how these aspects should be maintained and managed according to the determinants and impacts of algorithm performance. This approach will also help you understand the details related to model evaluation, hyperparameter tuning, c

Machine Learning is not just technology—it’s a modern wonder. It powers self-driving cars, recommends your next favorite movie, predicts market trends, and even helps doctors detect diseases earlier.And the best part? You can learn it—easily, enjoyably, and professionally.This course transforms Machine Learning and Data Science from “intimidating tech jargon” into simple, engaging lessons packed with real-world applications, practical coding exercises, and a touch of fun that makes learning addictive.What you’ll master:Effortless data handling with Python’s most powerful libraries—Scikit-Learn, Num Py, Pandas, and Matplotlib.Data visualization that makes patterns and trends leap off the screen to make boring data colourful.Supervised &amp; unsupervised learning explained in easy to understand language, with hands-on coding.Regression, classification, and clustering—built from scratch and applied to real problems.Complete project pipelines from messy raw data to polished, predictive models with performance evaluation.Why this course works:Fun, engaging explanations that make even complex algorithms feel simple.No overwhelming theory dumps—just clear concepts and immediate application.Hands-on projects so you learn by doing, not just watching.Step-by-step guidance so you never feel lost, even if you’re starting fresh.Whether you’re aiming to start a career in AI, add Machine Learning to your professional toolkit, or simply explore one of the most exciting fields of our time—this course will guide you with clarity, confidence, and maybe even a few laughs along the way.By the end of this course, you will:</

&lt; Step-by-step explanation of more than 7 hours of video lessons on Supervised Machine Learning: Complete Masterclass [2023]&gt;&lt;Instant reply to your questions asked during lessons&gt;&lt;Weekly live talks on Supervised Machine Learning: Complete Masterclass [2023]. You can raise your questions in a live session as well&gt;&lt;Helping materials like notes, examples, and exercises&gt;&lt;Solution of quizzes and assignments&gt; Welcome to the Machine Learning course!In this comprehensive course, you will learn the fundamental concepts and techniques used in Machine Learning. We will cover a range of topics from data preprocessing to model evaluation and selection, with hands-on exercises and projects to help you build and solidify your understanding of the concepts.The course is designed for beginners, but it will also be valuable for those who have some experience in programming and data analysis. You will be guided through the basics of Python programming and the most commonly used libraries for data manipulation and visualization, such as Pandas and Matplotlib.Once you have mastered the basics, we will delve into the core concepts of Machine Learning, including supervised and unsupervised learning, decision trees, random forests, clustering, neural networks, and deep learning. You will learn how to preprocess data, train and evaluate models, and optimize them for better performance.In addition to the theory, you will also have hands-on practice using real-world datasets and implementing Machine Learning algorithms with Python. By the end of the course, you will be able to apply Machine Learning techniques to solve a wide range of problems and use cases, and have the skills to further your studies in this exciting and rapidly growing field.Whether you are a student, a researcher, or a professional