Deep Learning & AI Deployment

This course comprises 6 purposely designed modules that take you on a carefully defined learning journey.

Our proven learning methodology blends the best that instructor-led training and self-paced learning have to offer. Leveraging the power of instructor feedback, mentor-supported hands-on practice, and additional home-based studying, you will build the deep technical and practical understanding todays employers are looking for.

Additionally, you will enjoy learning via an interactive online classroom environment where you will be able to participate and actively engage with your peers, instructors, and mentors. Plus, you will get the opportunity to earn recognized certifications which will help your resume and LinkedIn profile stand out.

The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include reading material, hands-on labs, and online exam questions.

As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues.

Once you have successfully completed the course, you will earn your Certificate of Completion.

By the end of this course, you will have:

• Mastered transformers and attention mechanisms, including BERT and GPT.
• Trained CNNs and vision transformers, like CLIP and DINO.
• Optimized AI models with quantization and pruning.
• Deployed AI models using MLOps best practices.

• Software engineers & developers Integrating ML models into applications & AI-driven solutions.
• Data scientists & analysts Enhancing predictive modeling & data-driven decision-making.
• Data engineers Optimizing data pipelines for ML workflows & feature engineering.
• AI/ML engineers Refining model training, hyperparameter tuning & evaluation.
• BI professionals Expanding into predictive analytics for business insights.
• Financial analysts & risk managers Applying ML for fraud detection, risk assessment & algorithmic trading.

• Strong Python programming skills Experience with Python, including data manipulation with libraries like NumPy and Pandas.
• Basic machine learning knowledge Understanding of regression, classification, and model evaluation concepts.
• Familiarity with neural networks Some exposure to deep learning frameworks (e.g., TensorFlow or PyTorch) is helpful but not mandatory.
• Basic knowledge of SQL & data processing Experience in handling structured and unstructured data is beneficial.