ELINAUT

E L I N A U T

Address
Vennala puthiya Road Thykauv jun, opp. Clearway Appt, Arakkakadavu, Kakkanad, Kerala 682028
Email
mail@elinaut.com
Contact
+91 9567863498

Select Curriculum:

Introduction to AI and ML - 1-month Intermediate AI and ML - 3 Month Advanced AI and ML - 6 month

Target Audience

Learners with basic AI/ML knowledge or completion of the 1-month course.

Objective

Develop intermediate skills in advanced ML algorithms, deep learning, and model deployment.

What You'll Learn

240 hours of comprehensive training

20 hours per week

Hands-on practical experience

Industry-relevant curriculum

Expert instructors

Week 1-3: Advanced Supervised Learning

Advanced Supervised Learning (12 hours)
- Decision trees, random forests, and support vector machines (SVM).
- Ensemble methods: Bagging and boosting (e.g., XGBoost).
Model Evaluation and Optimization (10 hours)
- Cross-validation, hyperparameter tuning, and grid search.
- Handling imbalanced datasets (e.g., SMOTE).
Lab: Implementing a random forest model with hyperparameter tuning (14 hours).

Week 4-6: Deep Learning Fundamentals

Introduction to Deep Learning (10 hours)
- Neural network architectures: Feedforward, convolutional neural networks (CNNs).
- Backpropagation and gradient descent.
Deep Learning with TensorFlow/Keras (12 hours)
- Building and training CNNs for image classification.
- Introduction to transfer learning (e.g., using pre-trained models like VGG16).
Lab: Developing a CNN for image classification (e.g., MNIST dataset) (14 hours).

Week 7-9: Unsupervised Learning and NLP Basics

Advanced Unsupervised Learning (12 hours)
- Advanced clustering: DBSCAN, Gaussian Mixture Models.
- Anomaly detection and dimensionality reduction techniques.
Introduction to Natural Language Processing (NLP) (10 hours)
- Text preprocessing: Tokenization, stemming, lemmatization.
- Basics of NLP models: Bag of Words, TF-IDF.
Lab: Implementing an NLP model for sentiment analysis (14 hours).

Week 10-12: Model Deployment and Projects

Model Deployment Basics (10 hours)
- Introduction to model deployment (e.g., Flask, FastAPI).
- Saving and loading ML models.
Project Management in AI/ML (8 hours)
- Planning AI/ML projects and data pipelines.
- Ethics in AI: Bias, fairness, and transparency.
Lab: Deploying a simple ML model as a web application (18 hours).
Capstone Project: Develop and deploy an ML model for a real-world problem (e.g., customer churn prediction) (10 hours).

Prerequisites

Basic programming knowledge (Python preferred) for the 1-month course; completion of prior modules or equivalent for 3-month and 6-month courses.

Tools & Software

Python

TensorFlow

Keras

scikit-learn

PyTorch

Jupyter Notebook

Flask

AWS

Docker.

Delivery Mode

Combination of lectures, hands-on labs, and project work.

Assessment

Weekly quizzes, lab assignments, and capstone projects.

Certification

Certificate of completion for each module; preparation for industry-recognized certifications in the 6-month course.

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Duration 240 hours
Weekly 20 hours
Level All Levels

Available Curricula

Introduction to AI and ML - 1-month

Duration: 1-month

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Intermediate AI and ML - 3 Month

Duration: 3-month

Selected

Advanced AI and ML - 6 month

Duration: 6-month

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Course Highlights

Expert instructors with industry experience
Hands-on projects and real-world applications
Flexible learning schedule
Placement assistance

Artificial Intelligence and Machine Learning

Category