Artificial Intelligence Developer
Course Curriculum:
- Foundations of Python & Mathematics for AI
- Core Concepts of Machine Learning
- Deep Learning & Neural Networks
- AI Tools, Libraries, and Frameworks
- Real-Time Projects & Model Deployment
Full Course Details
Course Name:
Artificial Intelligence Development
Overview:
This hands-on course is designed to prepare you as a job-ready AI Developer, starting from the foundational concepts in Python programming and mathematics, moving through core machine learning techniques, and diving deep into advanced AI models and neural networks. You will learn to build intelligent systems, process real-world data, and deploy AI models using modern tools and cloud platforms. Through mini-projects and a capstone project, this course ensures you are industry-ready for AI, ML, and Data Science roles.
Module Breakdown:
- Foundations of Python & Mathematics for AI
- Python Programming Basics
- Data Structures (List, Dict, Set, Tuple) and Functions
- Numpy and Pandas for Data Manipulation
- Data Visualization with Matplotlib and Seaborn
- Linear Algebra: Vectors, Matrices, Dot Products
- Probability, Statistics, Mean, Median, Variance
- Calculus: Derivatives and Gradients (Conceptual for AI)
- Core Concepts of Machine Learning
- Introduction to ML: Supervised, Unsupervised, Reinforcement Learning • Data Preprocessing, Feature Scaling, Encoding
- Algorithms:
o Linear Regression, Logistic Regression
o Decision Trees, Random Forest
o K-Nearest Neighbors, SVM
o K-Means Clustering, PCA
- Model Evaluation Metrics: Accuracy, Confusion Matrix, Precision, Recall, F1 Score • Model Tuning: Cross-Validation, Hyperparameter Tuning (GridSearchCV)
- Deep Learning & Neural Networks
- Introduction to Deep Learning and Artificial Neural Networks
- Activation Functions, Loss Functions, Optimizers
- Feedforward and Backpropagation
- TensorFlow & Keras Basics
- Convolutional Neural Networks (CNN) – Image Processing
- Recurrent Neural Networks (RNN), LSTM – Sequential Data
- Natural Language Processing (NLP)
o Tokenization, Lemmatization
o Sentiment Analysis
o Text Classification using LSTM/RNN
- AI in Real World: Chatbots, Image Classifiers, Recommendation Systems
- AI Tools, Libraries, and Frameworks
- Python Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras • Model Tracking & Experimentation: MLflow
- Git & GitHub for Version Control
- Jupyter Notebooks & Google Colab for Development
- Deployment using Flask/FastAPI
- API Testing using Postman
- Introduction to MLOps Concepts
- Basics of Cloud (AWS/GCP) for AI Model Hosting
- Real-Time Projects & Model Deployment
- Mini Projects:
o House Price Prediction
o Handwritten Digit Recognition
o Spam Email Classifier
o Movie Recommendation System
- Capstone Project: End-to-End AI System using Real-World Dataset o Data Collection → Preprocessing → Model Building → Deployment • Model Deployment using Streamlit or Flask on Heroku/Render • GitHub Portfolio Building
- Resume Building and Mock Interviews
Course Duration:
12 Weeks (3 Months)
- Modes: Weekday, Weekend, or Self-paced
- Includes Assignments, Mock Interviews, and Portfolio Projects
Learning Outcomes:
- Build a strong foundation in Python & AI Math
- Develop and train ML and DL models using Scikit-learn, TensorFlow, Keras • Apply AI to vision, text, and structured data problems
- Deploy AI projects using Flask, Streamlit, and cloud platforms
- Work confidently with real datasets, tools, and version control
- Build a professional AI project portfolio
Who Should Enroll:
- Final Year Students or Fresh Graduates
- Aspiring AI/ML Engineers and Data Scientists
- Python Developers looking to transition to AI
- Professionals aiming to upskill in Machine Learning and Deep Learning
Certification:
Upon successful completion, students will receive a Certificate of Completion in Artificial Intelligence Development, covering Python, Machine Learning, Deep Learning, NLP, Model Deployment, and Tools.