Understand the concepts of big data and the 3V's (Volume, Velocity, Variety).

Learn to handle real-time data streams and perform data cleaning and encoding.

Apply data preprocessing skills to real-world datasets.

Differentiate between descriptive and inferential statistics.

Conduct hypothesis testing and effectively communicate statistical results.

Apply various visualization techniques to explore and communicate data insights.

Analyze real-world data using statistics and EDA.

Gain proficiency in regression and classification models.

Learn model selection techniques and evaluate model performance.

Apply machine learning concepts to real-world prediction tasks.

Explore advanced regression techniques and support vector machines (SVM).

Build and evaluate machine learning models for image classification.

Apply regression and SVM to solve real-world problems.

Master clustering techniques and feature engineering.

Segment a market using unsupervised learning techniques.

Apply unsupervised learning to real-world data.

Understand lexical, syntactic, and semantic processing in NLP.

Work with pre-trained language models.

Conduct sentiment analysis on social media data.

Apply NLP techniques to real-world text data.

Explore neural network architectures and Convolutional Neural Networks (CNNs).

Build deep learning models for digit recognition.

Discuss advanced AI concepts in class.

Dive into advanced deep learning concepts, including RNNs, GANs, and transfer learning.

Apply advanced deep learning techniques to gesture recognition.

Collaboratively present group projects.

Understand classical reinforcement learning techniques like Q-Learning and Markov Decision Processes.

Apply reinforcement learning to game simulation.

Solve reinforcement learning problems through assignments and quizzes.

Explore deep reinforcement learning techniques, including DQN and advanced gradient methods.

Apply deep reinforcement learning to robot navigation.

Assess your understanding through assignments and tests.

Delve into advanced topics like transfer learning and time series forecasting.

Apply advanced AI and ML techniques to real-world problems.

Complete assignments to demonstrate proficiency.

Understand distributed computing and its role in handling big data.

Deploy machine learning models in cloud environments.

Assess your knowledge through assignments and quizzes.

Apply the complete data science workflow to real-world industry problems.

Collaborate on a capstone project related to a specific industry challenge.

Present your project and provide a comprehensive report.

These learning outcomes demonstrate the skills and knowledge you will gain as you progress through each module of the AI & ML program, ensuring a well-rounded understanding of artificial intelligence and machine learning concepts and their practical applications.