Cyber Pathshala

Welcome to Cyber Pathshala

Machine Learning is transforming industries by enabling systems to learn and improve from experience. This comprehensive course covers the fundamentals of ML algorithms, statistical modeling, and practical implementation using modern tools and frameworks.

Eligibility

Programming Knowledge (Python)
Strong Mathematics Background
Statistics and Probability
Linear Algebra Basics
Analytical Thinking
STEM Background (Preferred)

Requirements

Laptop with Minimum 8GB RAM
Python Development Environment
ML Libraries (scikit-learn, TensorFlow)
Jupyter Notebook
Data Analysis Tools

Machine Learning Tracks

Supervised Learning

Unsupervised Learning

Reinforcement Learning

ML Deployment

Comprehensive Machine Learning Program

1. Supervised Learning

Master prediction and classification algorithms with labeled data.

Topics covered:

* Linear Regression
* Logistic Regression
* Decision Trees
* Random Forests
* Support Vector Machines
* Neural Networks Basics

2. Unsupervised Learning

Learn pattern recognition and data structure discovery techniques.

Topics covered:

* Clustering Algorithms
* Dimensionality Reduction
* Principal Component Analysis
* Association Rules
* Anomaly Detection
* Feature Learning

3. Reinforcement Learning

Explore decision-making algorithms and reward-based learning.

Topics covered:

* Markov Decision Processes
* Q-Learning
* Deep Q Networks
* Policy Gradient Methods
* Multi-Agent Systems
* Game Theory Basics

4. ML Engineering & Deployment

Learn to deploy and maintain machine learning models in production.

Topics covered:

* Model Deployment
* MLOps Practices
* API Development
* Model Monitoring
* Pipeline Development
* Cloud Deployment

Career Opportunities

* Machine Learning Engineer
* Data Scientist
* ML Research Scientist
* ML Operations Engineer
* Quantitative Analyst
* AI Developer
* ML Solutions Architect
* Research Engineer

Course Benefits

* Industry-standard curriculum
* Hands-on project experience
* Real-world datasets
* Industry expert mentorship
* Kaggle competition participation
* Research paper implementation
* Interview preparation
* Career guidance