This comprehensive AI ML with Deep Learning and Supervised Models specialization equips you with the skills to excel in roles across AI, machine learning, and deep learning. Through in-depth modules, you'll master regression, classification, clustering, neural networks, and advanced AI frameworks to solve real-world challenges.
By the end of this course, you will be able to:
Master AI and ML Fundamentals: Learn key AI concepts, machine learning techniques, and applications in supervised, unsupervised, and reinforcement learning.
Build and Optimize Neural Networks: Develop feedforward, convolutional, and recurrent neural networks using TensorFlow and Keras for diverse applications.
Implement RNNs and LSTMs: Apply advanced models like Recurrent Neural Networks and Long Short-Term Memory networks for sequential data tasks.
Analyze AI's Transformative Impact: Understand ethical considerations, emerging trends, and AI’s potential to innovate across industries.
Guided by industry experts, you’ll gain hands-on experience and practical knowledge, preparing you to leverage AI and machine learning technologies effectively in your career.
Applied Learning Project
Project 1 Overview: Creating Cohorts of Songs
In this project, you will explore Spotify song data to understand and group tracks based on their features. You will clean and analyze data, visualize key trends, and identify patterns in song popularity. By applying clustering techniques, you will learn how to segment songs into meaningful cohorts, enhancing recommendation systems and gaining hands-on experience in data science and machine learning.
Project 2 Overview: Text Classification Using LSTM
In this project, you will classify text data using Long Short-Term Memory (LSTM) networks. You will preprocess and tokenize text, convert it into sequences, and train a deep learning model for classification. By implementing LSTM-based text classification, you will gain hands-on experience in NLP, deep learning, and model evaluation techniques.
