Master the Art of Artificial Intelligence (6-Week Intensive)
Course Duration: 6 Weeks
Mentorship: Industry experts from top tech companies will guide you throughout the course.
Mode of Learning: Online, Self-paced + Weekly Live Sessions
Week 1: Introduction to AI & Python Programming
- Introduction to Artificial Intelligence: Overview of AI concepts, history, applications, and different subfields (Machine Learning, Deep Learning, Natural Language Processing).
- Python Programming Fundamentals: Setting up the Python environment, data types, variables, control flow, functions, and object-oriented programming in Python.
- Introduction to Libraries: NumPy for numerical computing and Pandas for data manipulation.
- Project 1: Basic Data Analysis with Python – Perform basic data analysis tasks using Pandas and NumPy on a given dataset.
Outcome: Foundational knowledge of AI and Python, ability to perform basic data manipulation and analysis.
Week 2: Machine Learning Fundamentals
- Supervised Learning: Introduction to supervised learning algorithms, including linear regression, logistic regression, and decision trees.
- Unsupervised Learning: Introduction to unsupervised learning algorithms, including clustering (k-means) and dimensionality reduction (PCA).
- Model Evaluation: Metrics for evaluating machine learning models (accuracy, precision, recall, F1-score).
- Project 2: Building a Classification Model – Train and evaluate a classification model on a real-world dataset.
Outcome: Understanding of fundamental machine learning concepts and algorithms, ability to build and evaluate basic ML models.
Week 3: Deep Learning with TensorFlow/Keras
- Neural Networks: Introduction to neural networks, activation functions, and backpropagation.
- Deep Learning Frameworks: Introduction to TensorFlow/Keras for building and training deep learning models.
- Convolutional Neural Networks (CNNs): Building CNNs for image classification tasks.
- Project 3: Image Classification with CNNs – Build and train a CNN to classify images from a given dataset.
Outcome: Understanding of deep learning concepts and neural networks, ability to build and train CNNs for image classification.
Week 4: Natural Language Processing (NLP)
- Text Preprocessing: Techniques for cleaning and preparing text data for NLP tasks.
- Word Embeddings: Introduction to word embeddings (Word2Vec, GloVe) for representing words as vectors.
- Recurrent Neural Networks (RNNs): Building RNNs for sequence modeling tasks, such as text classification and generation.
- Project 4: Text Classification with RNNs – Build and train an RNN to classify text data.
Outcome: Understanding of NLP concepts and techniques, ability to build and train RNNs for text-based tasks.
Week 5: Advanced Deep Learning & Model Deployment
- Advanced Architectures: Introduction to more advanced deep learning architectures, such as Generative Adversarial Networks (GANs) and Transformers.
- Model Optimization: Techniques for optimizing deep learning models, such as hyperparameter tuning and regularization.
- Model Deployment: Deploying trained machine learning models to a web application or other platform.
- Project 5: Building and Deploying a Deep Learning Model – Build a more complex deep learning model and deploy it to a simple web interface.
Outcome: Knowledge of advanced deep learning concepts and model deployment techniques.
Week 6: Final Project & Career Prep
- Project 6: Final AI Project – Develop a complete AI project of your choice, incorporating the skills learned throughout the course. This could be a more complex application of image recognition, natural language processing, or another area of interest.
- Career Preparation:
- Portfolio Building: Refine and complete your portfolio with a professional presentation of your projects.
- Interview Prep & Resume Tips: Learn how to showcase your skills, work on technical interview questions, and perfect your resume.
- Industry Insights: Get advice on the latest industry trends, frameworks, and technologies.
Outcome: By the end of the course, you’ll have a strong portfolio and the confidence to apply for jobs in AI.
Major Projects:
- Basic Data Analysis with Python (Week 1) – Core Skills: Python programming, data manipulation, and analysis.
- Building a Classification Model (Week 2) – Core Skills: Machine learning algorithms, model training, and evaluation.
- Image Classification with CNNs (Week 3) – Core Skills: Deep learning, CNNs, image processing.
- Text Classification with RNNs (Week 4) – Core Skills: NLP, RNNs, text processing.
- Building and Deploying a Deep Learning Model (Week 5) – Core Skills: Advanced deep learning, model optimization, and deployment.
- Final AI Project (Week 6) – Core Skills: Full AI lifecycle, project planning, and execution.
Mentorship at Eduveda Academy:
- Industry Mentor Assignment: You’ll be paired with a mentor from a top tech company who will provide personalized guidance on projects, career advice, and best practices.
- 1-on-1 sessions for project reviews and troubleshooting.
- Live Q&A Sessions weekly with mentors and instructors.
Final Notes:
- Weekly Live Sessions: These will cover key topics, provide updates, and allow you to ask questions.
- Peer Networking: Join the academy Slack group for collaboration and feedback.
This 6-week course is intense but designed to give you all the key skills to jumpstart your career in Artificial Intelligence. You’ll leave with solid project experience, a polished portfolio, and the confidence to apply for jobs in the field.
