Machine Learning
Unlock the power of Machine Learning with Python in this bootcamp. From the fundamentals of Python to deep learning, you'll gain hands-on experience in data handling, machine learning algorithms, and deploying ML models. By the end, you'll be ready to build and deploy your own ML solutions.
$2300
🕒 Duration
12-14 weeks
📚 Format
2 sessions per week | 2-4 hours per session
📅 Next Session
May 2025
📡 Hybrid Mode
In-person: Physical
Online: Live Class
Things Good to Know
Here are some essential insights to help you understand what to expect from this course.
Prerequisites
- Curiosity and willingness to learn something new!
Learning Outcomes
- Master Python Programming
- Transform Raw Data into Actionable Insights
- Design, Build, and Deploy Machine Learning Models
- Explore Supervised, Unsupervised, Reinforcement Learnings, Neural Nets, Deep Learning Techniques
- Apply Skills Through Real-World Projects
Career Paths
- Machine Learning Engineer
- Data Scientist
- AI Specialist
- ML Application Developer
Who is this for?
- Beginners interested in machine learning
- Developers looking to specialize in machine learning
- Data enthusiasts wanting to transition into ML
Why this course?
- Beginner-friendly approach to learning machine learning
- Hands-on projects to build real-world skills
- Learn how to deploy machine learning models in production
Why us?
- Expert instructors with industry experience
- Comprehensive curriculum covering the entire ML pipeline
- Supportive community and mentorship
- Python
- Jupyter Notebook
- Django
- AWS
Tools and Resources
Roadmap
Your journey starts here
Machine Learning Introduction
An overview of machine learning, its types, and real-world applications.
- Introduction to Machine Learning
- Types of Machine Learning
- Real-world Applications
Activity: Realworld tasks
Python Fundamentals
Set up your Python environment and learn core Python programming concepts essential for machine learning.
- Environment Setup
- Basic Syntax and Variables
- Data Types and Structures
- Operators and Math
- Conditionals and Loops
- Functions
- Object-Oriented Programming
- Working with Files
Activity: Realworld tasks
Python for Machine Learning
Learn the key Python libraries and their application in ML.
- NumPy
- Pandas
- Matplotlib/Seaborn
Activity: Realworld tasks
Data Preparation and Exploratory Data Analysis
Prepare and clean your data for machine learning algorithms.
- Data Cleaning
- Feature Engineering
- Exploratory Data Analysis
Activity: Realworld tasks
Supervised Learning I
Introduction to regression models and their applications.
- Linear Regression
- Multi-feature Regression
- Polynomial Regression
Activity: Realworld tasks
Supervised Learning II
Advanced supervised learning algorithms for classification tasks.
- Logistic Regression
- Decision Trees
- Support Vector Machines
- Ensemble Methods
Activity: Realworld tasks
Unsupervised Learning & Reinforcement Learning
Explore clustering, dimensionality reduction, and reinforcement learning concepts.
- K-means Clustering
- Dimensionality Reduction
- Recommendation Systems
- Reinforcement Learning
Activity: Realworld tasks
Deep Learning (Deep Neural Networks)
Learn the foundations of neural networks and their advanced applications.
- Neural Networks Basics
- TensorFlow and Keras
- Model Training & Optimization
Activity: Realworld tasks
Web Development with Django
Learn how to deploy machine learning models as web applications using Django.
- Django Basics
- Creating a Full Stack Web App
- Deployment with AWS
Activity: Realworld tasks
Final Project Work
Develop and deploy a machine learning model and web application.
- Building an End-to-End Project
- Testing and Optimization
Activity: Realworld tasks
Presentation, Certification, and Future Pathways
Present your project, receive certification, and plan for your future career in machine learning.
- Project Presentation
- Certification
- Future Career Pathways
Activity: Realworld tasks
