Full Stack Data Science
Designed for freshers and working professionals to build deep expertise in artificial intelligence and transition into full-time data science roles
200+ Hours of teaching
200+ Hours of expert teaching, providing in-depth coverage of the industry-relevant content
Taught by Industry Experts
Unique opportunity to work with industry experts leading AI ML projects
10+ Projects
We will be taking you through 10+ real-world projects covering various algorithms
Modules Covered
1. Python Programming
Master Python Programming
- Python Programming Basics
- Python Data Structures
- Key Packages
- Object Oriented Python
- Fast API
- Flask
- Hands-on Projects
2. Data Engineering
Understand Data Engineering
- Data Engineering Basics
- Data Visualisation
- Basic Data Analysis with Python
- Data Pipelines
- Airflow
- SQL Programming
- Data Lakes and Warehousing
- Hands-on Projects
3. Maths Foundation for AI
Understand Math for Data Science
- Probability Basics
- Linear Algebra Basics
- Descriptive Statistics
- Inferential Statistics
- Hypothesis Testing
4. Supervised Learning
ML Basics
- Machine learning life cycle
- Supervised Learning
- Classification Algorithms
- Regression Algorithms
- Model Evaluation
- Ensemble learning
- Hands-on Projects
5. Unsupervised Learning
ML Basics
- Unsupervised learning Basics
- Cluster Algorithms
- Principle Component Analysis
- Singular Value Decomposition
- Hands-on Projects
6. Feature Engineering
Master Deep Learning and AI
- Feature Engineering Basics
- Core Tasks in Feature Engineering
- Feature Engineering Pipeline
- Scaling
- Normalizers
- Hands-on-Projects
7.Recommendation Systems
ML Basics
- Recommendation System Basics
- Popularity Based Recommender
- Content-Based Recommendation Systems
- Collaborative Filtering Techniques
- Hybrid Recommender
- Hands-on Projects
8.Hyperparameter Tuning
ML Basics
- Hyperparameter Tuning Basics
- Gird Search
- Random Search
- Hyperparameter Tuning Frameworks
- Hands-on Projects
9.Deep Learning Basics
Master Deep Learning and AI
- Tensorflow and Keras
- Artificial Neural Networks
- Perceptron
- Neural Networks Key Terms
- Forward and Back-Propagation
- Activation Functions
- Optimizers
- Hands-on Projects
10. Convolutional Neural Networks
Deep Learning
- Computer Vision Basics
- CNN Intro
- Convolution and Filters
- CNN Architectures
- Object Detection
- Segmentation
- Transfer Learning
- Advanced CNNs
- Hands-on Projects
11. RNNs and Transformers
Deep Learning
- Recurrent Neural Networks
- LSTM Architectures
- Sequential Modelling
- GANs
- Advanced RNN Architectures
- Hands-on Projects
12. ML Engineering
ML Engineering
- Basics of ML Engineering
- Key Tools
- MLOps
- ML Pipelines
- Productionising ML Models
- Hands-on Projects
13. Capstone Project
Hands-on AI
- Defining an AI Problem
- Project Architecture
- Data Collection
- Model Building
- Model Deployment
- Model Integration
- Evaluation
14. AI Research
AI Career Building
- Identifying a research phase
- Mentoring
- Submitting Research Paper