COURSE DESCRIPTION:
The Machine Learning and Deep Learning course offers a comprehensive exploration of the fundamental concepts and algorithms associated with machine learning (ML) and deep learning (DL). Participants will engage in practical applications of these methodologies to address intricate real-world challenges. The curriculum encompasses both theoretical insights and hands-on experiences in ML and DL, including supervised and unsupervised learning, neural networks, natural language processing (NLP), and computer vision.
This course emphasizes the ability of machines to learn from data autonomously, with deep learning representing a specialized area of ML that utilizes multi-layered neural networks. Students will focus on constructing models through popular ML algorithms and advanced DL frameworks like TensorFlow, Keras, and PyTorch. Additionally, they will acquire skills in data preprocessing, model training, algorithm optimization, and the deployment of ML/DL models in operational environments.
CERTIFICATION
Upon finishing the Machine Learning and Deep Learning course, participants will be awarded a Certificate in Machine Learning and Deep Learning.
To qualify for certification, students must fulfill all weekly assignments and practical lab tasks, attain a satisfactory score on both the midterm and final exams, and present a capstone project that showcases the application of the ML/DL techniques acquired throughout the course. This certificate signifies expertise in machine learning and deep learning methodologies, equipping graduates for careers as data scientists, ML engineers, and AI researchers.
LEARNING OUTCOME
By the conclusion of the Machine Learning and Deep Learning course, participants will acquire the following competencies:
Participants will grasp the foundational concepts of machine learning, including its definition, subfields, and its connection to artificial intelligence. They will differentiate between supervised, unsupervised, and reinforcement learning, and learn to frame problems suitable for machine learning while selecting the right algorithms. In supervised learning, they will implement key algorithms such as Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, and Random Forests, while also evaluating their performance through metrics like accuracy, precision, recall, F1-score, and confusion matrices. They will gain insights into overfitting and underfitting, learning to mitigate these issues with cross-validation and regularization techniques. In the realm of unsupervised learning, participants will implement clustering methods like K-means, hierarchical clustering, and DBSCAN, as well as dimensionality reduction techniques such as Principal Component Analysis and t-SNE, applying these methods to real-world scenarios like customer segmentation and anomaly detection. They will also delve into reinforcement learning, understanding the fundamentals of Markov Decision Processes, rewards, and actions, while exploring key algorithms like Q-learning and policy gradient methods, applying these concepts to simple environments such as game playing and robotic control tasks. Finally, participants will learn about artificial neural networks, including their structure, activation functions, and weights, and will implement a basic neural network using frameworks like TensorFlow or Keras for binary classification tasks, while comprehending the backpropagation algorithm for training neural networks. They will also explore the architecture of Convolutional Neural Networks and their applications in computer vision.
Course Features
- Lectures 38
- Quiz 0
- Duration 54 hours
- Skill level All levels
- Language English
- Students 28
- Assessments Yes
