The Machine Learning Course is a comprehensive and practical introduction to the field of machine learning. Designed for both beginners and those with some prior experience, this course covers the key concepts, algorithms, and techniques used in machine learning. Through a combination of theoretical learning, hands-on coding exercises, and real-world projects, participants will gain a deep understanding of machine learning and its applications.
Schedule: Monday, Wednesday, Friday - 18h00 to 21h00
Get trained by Industry expertsOur courses are delivered by professionals with years of experience having learned first-hand the best, in-demand techniques, concepts, and latest tools.
Official Certification curriculumOur curriculum is kept up to date with the latest official Certification syllabus and making you getting ready to take the exam.
Tax CreditClaim up to 25% of tuition fees and education tax credit from your taxes.
Discount on Certification VoucherUpto 50 percent discount voucher will be provided.
24/7 Lab accessOur students have access to their labs and course materials at any hour of the day to maximize their learning potential and guarantee success.
This module initiates the understanding of machine learning, emphasizing its significance in problem-solving and technology. It delves into the distinctions among supervised, unsupervised, and reinforcement learning paradigms. Additionally, it showcases machine learning's versatile applications across diverse industries, providing a comprehensive outlook on its real-world implementations.
This module covers data preprocessing and exploration, emphasizing techniques for preparing data for machine learning tasks. It includes handling missing values, data normalization, encoding categorical variables, and exploratory data analysis (EDA) techniques like visualization and statistical analysis to comprehend data structures and patterns.
This module focuses on supervised learning algorithms, exploring models that learn from labeled data pairs. It covers various algorithms such as linear regression, logistic regression, decision trees, support vector machines (SVM), k-nearest neighbors (KNN), and ensemble methods like random forests and gradient boosting, providing a comprehensive understanding of supervised learning techniques.
This module delves into unsupervised learning algorithms, which operate on unlabeled data. It covers techniques like clustering (K-means, hierarchical clustering), dimensionality reduction. Unsupervised methods aim to reveal patterns, structures, or relationships within data without predefined labels, fostering a comprehensive understanding of data properties and relationships.
This module focuses on model evaluation and validation techniques in machine learning. It covers methods like cross-validation, train-test splits, performance metrics (accuracy, precision, recall, F1-score), and learning curves. It aims to assess model performance, reliability, and generalizability, ensuring robustness and accuracy in predictive models.
This module delves into neural networks and deep learning, exploring multi-layered networks capable of learning complex patterns. It covers artificial neural networks, deep learning architectures (CNNs, RNNs), activation functions, optimization techniques (SGD, Adam), and frameworks like TensorFlow or PyTorch. Deep learning enables modeling intricate data structures, making it integral in various fields for tasks like image recognition, natural language processing, and more.
This module centers on computer vision, encompassing techniques to enable computers to interpret and understand visual information from images or videos. It covers image processing, feature extraction, object detection, segmentation, and recognition using methods like CNNs (Convolutional Neural Networks) and frameworks such as OpenCV. Computer vision finds applications in fields like autonomous vehicles, healthcare imaging, security, and augmented reality.
This module focuses on the deployment of machine learning models into real-world applications and the ethical considerations surrounding their usage. It covers techniques for deploying models in production environments, ensuring scalability, efficiency, and reliability. Additionally, it delves into ethical considerations, addressing issues like bias, fairness, transparency, and privacy, fostering responsible and ethical AI implementations.
This module explores advanced topics and future trends in the realm of machine learning and artificial intelligence. It may cover cutting-edge techniques like generative adversarial networks (GANs), transfer learning, reinforcement learning advancements, and emerging trends in AI research. Additionally, it might delve into topics such as AI ethics, explainable AI, and the integration of AI with emerging technologies, providing insights into the evolving landscape of machine learning and its potential future directions.
While we encourage all interested applicants to apply, to enter our certification program you must be :
Interested in gaining IT knowledge and enter into real world IT domain, switching carees in IT or applying for entry level positions
Machine Learning Certification.
Upon completing this certification course you will: