The “AI/ML Expert” course is a six-month program designed to equip learners with comprehensive skills in artificial intelligence and machine learning. Beginning with the fundamentals of machine learning and Python programming, the course covers data manipulation, exploratory data analysis, and data visualization. Students will learn key techniques in both supervised and unsupervised learning, such as regression, classification, clustering, and dimensionality reduction.

In the fourth month, the focus shifts to deep learning, where learners build and train neural networks using frameworks like TensorFlow and Keras. The fifth month delves into natural language processing (NLP) and reinforcement learning (RL), covering topics like text and sentiment analysis, as well as foundational RL algorithms. The final month prepares students for real-world applications, including model deployment on cloud platforms and big data processing with Apache Spark.

The course includes over 15 hands-on projects and culminates in a capstone project, allowing learners to apply their knowledge in practical scenarios and gain the expertise needed to become proficient in AI and machine learning.

What You’ll Learn

• Foundations of AI and Machine Learning: Understand the core concepts of artificial intelligence (AI) and machine learning (ML), including key algorithms and techniques.
• Python for Data Science: Gain proficiency in Python, the primary language for AI/ML development, and learn how to use it for data analysis, manipulation, and visualization.
• Data Analysis and Visualization: Master techniques for manipulating datasets, performing exploratory data analysis (EDA), and visualizing data insights using libraries like Pandas, Matplotlib, and Seaborn.
• Supervised Learning: Learn essential supervised learning algorithms such as regression and classification techniques, and apply them to real-world datasets.
• Unsupervised Learning: Dive into unsupervised learning methods, including clustering and dimensionality reduction, to identify patterns in unlabeled data.
• Deep Learning: Develop and train neural networks using TensorFlow and Keras, understanding advanced topics such as backpropagation, optimization, and overfitting.
• Natural Language Processing (NLP): Explore text-based data processing techniques, including text classification, sentiment analysis, and sequence modeling, as well as key NLP algorithms.
• Reinforcement Learning (RL): Gain an introduction to reinforcement learning, including core concepts like rewards, states, actions, and popular algorithms such as Q-learning.
• Big Data and Cloud Deployment: Learn how to deploy machine learning models on cloud platforms and process large datasets using tools like Apache Spark.
• Capstone Project: Apply your knowledge to a comprehensive project that demonstrates your ability to solve real-world AI/ML challenges.