CERTIFICATIONS

Key Points: 

• Learn how to train RL agents for safe, human-like autonomous driving behavior.

• Understand how LLMs enhance decision-making and interpretability in AI systems.

• Gain practical experience in designing and applying reward functions for real-world autonomous environments.

• Design RL agents using techniques like Deep Q-Networks (DQN), experience replay, and integrate LLMs to enhance decision-making

Key Points: 

• Hands-on experience with Docker containers

• Create a customised Docker image using Dockerfile

• Perform data persistence in Docker using volumes and the copy command

• Create a Docker image tar file and extract the image from the tar file 

• Learn from industry professionals

• Solve real-world tasks and challenges

• Boost confidence with modern development tools

Key Points: 

• Hands-on experience with Docker containers

• Understanding Docker architecture

• Docker commands to manage images, containers, volumes, and networks

• Deploying a web application as a Docker container

• Learn from industry professionals

• Solve real-world tasks and challenges

• Boost confidence with modern development tools

Key Points: 

• Hands-on experience with Docker containers

• Create and run containers using Docker Hub images

• Build and share custom Docker images

• Learn from industry professionals

• Solve real-world tasks and challenges

• Boost confidence with modern development tools

Key Points: 

• Build machine learning models in Python (NumPy & scikit-learn)

• Build & train supervised machine learning models (Prediction and binary classification tasks, linear regression, and logistic regression)

• Build & train a neural network with TensorFlow (multi-class classification)

• Apply best practices for machine learning development (real world)

• Build & use decision trees, random forests, and boosted trees

• Use unsupervised learning techniques (clustering & anomaly detection)

Key Points: 

• Learned the foundational concepts of NN and DL 

• Worked in developing & training DNN, identifying key architecture parameters

• Implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture

• Build a CNN (Detection and Recognition)

• Train test sets, analyze variance for DL applications

• Learned to use standard techniques & different optimization algorithms

• Learned to build neural networks in TensorFlow

Key Points: 

• Learned Python Basics

• Python Data Structures

• Working with Data in Python

• Working with Numpy Array and Simple APIs

Key Points: 

• Worked NumPy For Data Analysis 

• Worked NumPy For Data Science

• Worked on numerical computation using Python 

• Learned how to work in N-dimensional arrays

Key Points: 

• Learned foundation in data analysis with Python

• Worked different methods and attributes in pandas

• Worked in Pandas data structures: Series, DataFrame, and Index Objects

• Analysis of large and messy data files