AI and ML with TensorFlow

COURSE DESCRIPTION

The AI and ML with TensorFlow course spans twelve weeks, providing a thorough introduction to artificial intelligence and machine learning using the TensorFlow framework. Students start by setting up their development environment and installing essential libraries and tools. The course begins with foundational concepts in Python programming and linear algebra, crucial for understanding machine learning algorithms.

Early weeks focus on fundamental AI and ML concepts, including supervised and unsupervised learning, data preprocessing, and exploratory data analysis. Students learn to handle datasets using Pandas and NumPy and visualize data with Matplotlib and Seaborn. The course then transitions to understanding TensorFlow’s architecture, covering key components like tensors, computational graphs, and sessions.

As the course progresses, students delve into building and training neural networks using TensorFlow. They explore various neural network architectures, including convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) for sequence prediction. Emphasis is placed on understanding and implementing different types of layers, activation functions, and optimization algorithms.

Midway through the course, students engage in hands-on projects, applying their knowledge to real-world datasets. They learn to fine-tune hyperparameters, perform model evaluation, and implement techniques to prevent overfitting, such as regularization and dropout. The course also covers advanced topics like transfer learning, reinforcement learning, and natural language processing (NLP) using TensorFlow.

Towards the end, students explore deployment strategies for AI models, including exporting TensorFlow models for production environments and using TensorFlow Serving for model serving. They also learn about TensorFlow Lite for deploying models on mobile and edge devices. The course concludes with a comprehensive project where students design, implement, and deploy an AI solution, demonstrating their proficiency in AI and ML with TensorFlow.

Through lectures, supervised labs, assignments, and quizzes, students gain practical experience and develop the skills needed for AI and ML development. The course also includes sessions on industry best practices, ethical considerations in AI, and career guidance, preparing students for roles in AI and machine learning.

CERTIFICATION

The AI and ML with TensorFlow course spans twelve weeks, teaching you the essentials of artificial intelligence and machine learning using TensorFlow. Starting with setting up your development environment, you will learn foundational concepts in Python programming, linear algebra, and key machine learning techniques.

LEARNING OUTCOMES

– Gain proficiency in Python programming and linear algebra for machine learning.
– Understand supervised and unsupervised learning, data preprocessing, and exploratory data analysis.
– Master TensorFlow’s architecture, including tensors, computational graphs, and sessions.
– Build and train various neural network architectures like CNNs and RNNs.
– Apply techniques for hyperparameter tuning, model evaluation, and preventing overfitting.
– Explore advanced AI topics like transfer learning, reinforcement learning, and NLP.
– Deploy AI models using TensorFlow Serving and TensorFlow Lite.
– Complete a capstone project, designing, implementing, and deploying an AI solution.

This course equips you with the knowledge and hands-on experience to excel in AI and ML development, preparing you for advanced roles in the industry.