VetsinTech AI & ML Accelerator

This course is for those that seek to excel in AI & Machine Learning. Through 8 weeks of instruction students will be taught AI & ML Principles as well as content that will greatly assist in landing job interviews for these positions and subsquently passing those reviews!

This course is valued at more than $20,000 and is being provided at scholarship pricing!

This course is an 8-week commitment with 4 days per week of instruction

Registration Instructions:

• Submit application through this
• Schedule 1-on -1 applicant interview
• After approval (3-5 business days), complete this Eventbrite Registration

Registration Requirements:

1. MUST be Active Duty, Trainsitioning Veteran, Separated Veteran, or Spouse of one of these
2. MUST have an active account at
3. MUST have validated through the at the website above
4. MUST commit to attending the Orientation and all daily sessions. Orientation will be held the week prior to the course beginning. This Orientation will be at 9AM Pacific. Details will be shared upon acceptance into the course.
5. MUST have a Github (or similar) version control account

Students that do not attend the Orientation will not be allowed to attend the course unless arrangements are made through the Education Director or Education Coordinator.

Priority seating for the course will be for unemployed students. If remaining slots are available, seats will be assigned accordingly.

Required:

Must have working knowledge of Python, Python libraries, functional programming, & OOP.

Schedule

Monday, Wednesday, Friday, 8-10PM Eastern // Saturday 12PM - 2PM. Students will have 10 hours of live instruction each week as well as 4-hours of guided learning in group settings (14 hours weekly).

IMPORTANT MESSAGE: Pending successful completion of the course, students will get a FULL REFUND

Please be prepared to have cameras on during class, and to be intereactive in the video chat & Slack. We highly recommend using a second monitor so that you can be watching the instructor on one screen and completing activities on the other. (This is important for the purpose of becoming proficient and efficient with the programming).

If you have any questions or encounter any issues during the registration process, please do not hesitate to contact our support team at [email protected]. We are here to assist you in your journey to a successful tech career.

Learn the in-demand skills of artificial intelligence (AI) and machine learning (ML) in this immersive cohort program.

This program is designed to give you the skills you need to launch a career in AI and ML, or to advance your current career in these fields. You will learn from experienced instructors and industry experts, and you will have the opportunity to work on real-world projects.

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This comprehensive curriculum aims to not only provide a strong theoretical foundation but also ensure that students are well-prepared for real-world interviews in the AI/ML domain.

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Curriculum for a 64-Hour Machine Learning Interview Prep Course

Week 1: Introduction, Setup & Essential Python (8 hours)

• Course Overview & ML Job Market Analysis (1 hour)

• Overview of the AI/ML landscape, job roles, and industry demand.

• Python & Jupyter Notebook Setup (1 hour)

• Installation, basic operations, and familiarization with the Jupyter environment.

• Essential Python for Data Science: Pandas, Numpy (3 hours)

• Data structures, indexing, aggregation, and basic operations. Introduction to vectorized computations in Numpy.

• Data Visualization: Matplotlib, Seaborn (3 hours)

• Creating plots, histograms, scatter plots, and custom visualizations. Aesthetics and storytelling through data.

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Week 2: Machine Learning Foundations (8 hours)

• ML Types: Supervised, Unsupervised, Semi-supervised, Reinforcement (2 hours)

• Detailed understanding and differences between each type, with practical examples.

• Key Concepts: Bias-Variance, Overfitting, Regularization (2 hours)

• Deep dive into foundational ML concepts, their implications, and mitigation strategies.

• Logistic Regression, k-Nearest Neighbors (2 hours)

• Algorithm workings, assumptions, strengths, weaknesses, and interview-centric questions.

• Decision Trees, Random Forests, and Ensemble Techniques (2 hours)

• Introduction to tree-based algorithms, bagging, boosting, and their applications.

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Week 3: Unsupervised Learning & Feature Engineering (8 hours)

• Clustering: k-means, Hierarchical (2 hours)

• Understanding clustering algorithms, applications, and common interview questions.

• Dimensionality Reduction: PCA, t-SNE (2 hours)

• Why dimensionality reduction is essential, algorithmic understanding, and use-cases.

• Feature Engineering & Selection Techniques (3 hours)

• Creating new features, transforming variables, and selecting the most impactful features.

• Model Evaluation: Cross-Validation, Metrics (1 hour)

• Understand different model evaluation metrics, their use cases, and when to apply them.

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Week 4: Advanced Topics & Deep Learning Introduction (8 hours)

• Advanced Algorithms: SVM, Gradient Boosting Machines (2 hours)

• Understanding the math, application, and common interview pitfalls.

• Recommender Systems: Collaborative Filtering, Matrix Factorization (2 hours)

• Understanding recommendation engines, challenges, and business applications.

• Intro to Neural Networks & Deep Learning (2 hours)

• Perceptrons to deep networks, activation functions, and backpropagation basics.

• Introduction to PyTorch: Basics, Tensors (2 hours)

• Getting started with PyTorch, understanding tensors, and building a basic neural network.

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Week 5: Dive into Deep Learning (8 hours)

• Deep Learning Architectures: CNNs, RNNs, LSTMs (3 hours)

• Applications, architectural understanding, and use-cases. Interview-centric challenges.

• Natural Language Processing: Word Embeddings, Basics (2 hours)

• Tokenization, embeddings, and introduction to sequence models.

• Transformers & Huggingface for Large Language Models (2 hours)

• Dive into the transformer architecture, attention mechanism, and using Huggingface.

• Practical Session: Building a Deep Learning Model (1 hour)

• Hands-on session to implement learnings from the week.

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Week 6: Interview-specific Topics & Soft Skills (8 hours)

• Machine Learning System Design (2 hours)

• How to design ML systems, scale them, and address business constraints.

• Behavioral & Situational Questions Preparation (2 hours)

• How to tackle "tell me about a time" questions and other HR-centric queries.

• Problem-solving & Whiteboarding Sessions (3 hours)

• Practice solving ML problems on the spot, whiteboarding, and code implementation.

• Mock Technical Interviews with Peer Review (1 hour)

• Simulated interviews with feedback loops.

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Week 7: Real-World Projects & Case Studies (8 hours)

• Image Classification, NLP, or Time Series Prediction Project (5 hours)

• End-to-end project building, from data preprocessing to model deployment.

• Case Studies: Real-world ML/AI Applications & Solutions (2 hours)

• Discussion of real-world applications, business impacts, and challenges.

• Review Session & Q&A (1 hour)

• Addressing doubts, clarifying concepts, and providing additional resources.

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Week 8: Mock Interviews & Final Assessment (8 hours)

• Mock Interviews: ML Algorithm, Deep Learning, System Design (4 hours)

• Conducting extensive mock interviews, replicating real interview environments.

• Course Assessment: Written Test on Algorithms & Concepts (2 hours)

• A comprehensive test to assess the understanding and readiness of students.

• Feedback & Improving After Rejections (1 hour)

• Strategies to handle rejection, improve continuously, and stay updated in the ML field.

• Closing Remarks & Next Steps in the ML/AI Journey (1 hour)

• Guidance on next steps, advanced courses, and continuous learning.

Event Venue

Online