Artificial Intelligence Training Program

• Overview

Methodology

Codexra Training Method (Theoretical explanation, task completion, real-world projects)

Weekly Class Hours

10 Hour

Week 1: Introduction to Artificial Intelligence and Machine Learning (10 hours)

Day (5 hours)

• What is Artificial Intelligence (AI)? History and application areas.
• Supervised, unsupervised, and reinforcement learning concepts.
• Lab Activity: Data analysis and your first AI model with Python.



Day (5 hours)

• Basic machine learning concepts: Model, training, validation, and testing.
• Task: Build and analyze a simple linear regression model.

Week 2: Data Processing and Preparation (10 hours)

Day (5 hours)

• Understanding, cleaning, and processing datasets.
• Data manipulation with Pandas and NumPy.
• Lab Activity: Filling missing data and applying data transformations.



Day (5 hours)

• Visualization tools: Data analysis with Matplotlib and Seaborn.
• Task: Analyze the main features of a dataset with graphs.

Week 3: Fundamental Algorithms and Models (10 hours)

Day (5 hours)

• Linear regression, logistic regression, decision trees.
• Lab Activity: Apply these algorithms to datasets using Scikit-learn.



Day (5 hours)

• K-Means and other clustering algorithms.
• Task: Develop a customer segmentation model.

Week 4: Machine Learning Model Evaluation (10 hours)

Day (5 hours)

• Model performance metrics: MSE, MAE, Accuracy, Precision, Recall, F1 Score.
• Lab Activity: Evaluate a model's performance with various metrics.



Day (5 hours)

• Overfitting and underfitting issues, regularization methods.
• Task: Optimize and improve model performance.

Week 5: Introduction to Deep Learning (10 hours)

Day (5 hours)

• What are Artificial Neural Networks (ANN)? Layers and activation functions.
• Lab Activity: Create a simple neural network model with TensorFlow or PyTorch.



Day (5 hours)

• Introduction to Convolutional Neural Networks (CNN).
• Task: Image classification with the MNIST dataset.

Week 6: Natural Language Processing (NLP) (10 hours)

Day (5 hours)

• NLP basics: Text processing, tokenization, stop words, stemming.
• Lab Activity: Build a text cleaning pipeline.



Day (5 hours)

• Text classification and LSTM usage.
• Task: Develop a sentiment analysis model.

Week 7: Reinforcement Learning and AI Applications (10 hours)

Day (5 hours)

• Basics of Reinforcement Learning, Markov Decision Processes (MDP).
• Lab Activity: Develop AI for a simple game scenario.



Day (5 hours)

• Real-world applications: Recommendation systems, chatbot development.
• Task: Design and test a recommendation system.

Week 8: Real-World Project (10 hours)

Day (5 hours)

• Project preparation: Problem definition, dataset selection.
• Task: Develop an AI solution for a real business problem.



Day (5 hours)

• Project presentation and evaluation.
• Lab Activity: Visualize and report model results.

Participants will advance from fundamental AI and ML skills to developing advanced models and real-world projects. They will acquire theoretical and practical knowledge to participate in AI projects.