Welcome To
Our Advanced Data Science & AI Applications (Track2)
This 6-month intensive program is designed to equip learners with the skills needed to break into high-paying data science roles. This course builds on and assumes that you’ve already successfully completed the perquisite Foundations For Data Science. By mastering Python, machine learning, deep learning, and big data technologies, students will graduate with industry-recognized certifications and real-world projects that showcase your expertise.
This 6-month intensive program is designed to equip learners with the skills needed to break into high-paying data science roles. This course builds on and assumes that you’ve already successfully completed the perquisite Foundations For Data Science. By mastering Python, machine learning, deep learning, and big data technologies, students will graduate with industry-recognized certifications and real-world projects that showcase their expertise.
✅ Track 2 Curriculum Breakdown
📌 Objective:
Track 2 builds upon Foundations for Data Science (Track 1) and dives into advanced machine learning, deep learning, and AI applications. Learners will gain expertise in predictive modeling, NLP, time-series forecasting, and scalable AI deployment.
🔷 Chapter 1 – Advanced Machine Learning
✔ Lesson 1.1 – Supervised Learning: Classification & Regression
✔ Lesson 1.2 – Unsupervised Learning: Clustering & Dimensionality Reduction
✔ Lesson 1.3 – Ensemble Learning & Model Optimization
💡 Hands-On Project: Building AI-Powered Predictive Models
🔷 Chapter 2 – Deep Learning Foundations
✔ Lesson 2.1 – Neural Networks & Backpropagation
✔ Lesson 2.2 – Convolutional Neural Networks (CNNs) for Image Processing
✔ Lesson 2.3 – Recurrent Neural Networks (RNNs) & LSTMs for Time-Series
💡 Hands-On Project: Developing AI Models for Image & Text Processing
🔷 Chapter 3 – Natural Language Processing (NLP)
✔ Lesson 3.1 – Text Preprocessing & Tokenization
✔ Lesson 3.2 – Word Embeddings & Transformers
✔ Lesson 3.3 – Sentiment Analysis & Chatbot Development
💡 Hands-On Project: Building AI-Powered NLP Applications
🔷 Chapter 4 – Time-Series Forecasting & AI for Finance
✔ Lesson 4.1 – Time-Series Analysis & Feature Engineering
✔ Lesson 4.2 – ARIMA, LSTMs & Transformer-Based Forecasting
✔ Lesson 4.3 – AI for Financial Risk Prediction & Fraud Detection
💡 Hands-On Project: Developing AI Models for Financial Forecasting
🔷 Chapter 5 – Scalable AI Deployment & MLOps
✔ Lesson 5.1 – Cloud-Based AI Deployment (AWS, GCP, Azure)
✔ Lesson 5.2 – Model Explainability & Interpretability (SHAP, LIME)
✔ Lesson 5.3 – MLOps for Scalable AI Systems
💡 Hands-On Project: Deploying AI Models for Real-World Applications
🎯 Final Review – Are We Missing Anything?
✔ This track covers all core skills needed for advanced AI applications!
🚀 Things to check before moving forward:
🔹 Are all coding projects fully implemented with clear documentation?
🔹 Have we integrated cloud-based AI deployment for scalability?
🔹 Do we need additional reinforcement learning before deep learning?
🔹 Should we add more case studies for applied AI in healthcare & finance?
Main Benefits:
- Comprehensive curriculum covering Python, AI, big data, and cloud computing.
- Hands-on labs & internships with Kaggle competitions and industry projects.
- Certifications from top providers like IBM, Google, and TensorFlow.
- Career-oriented training for roles in data science, ML engineering, and AI research.
Topics of Study:
- Python & Data Wrangling: Coding fundamentals, Pandas, and data preprocessing.
- Data Visualization & Exploratory Analysis: Tableau, Matplotlib, and Seaborn.
- Machine Learning & AI: Regression, classification, clustering, neural networks.
- Big Data & Cloud Computing: Apache Spark, Hadoop, AWS.
- Capstone Project & Internships: Solve real-world business challenges in tech.
🎯 Who Is It For?
This course is ideal for:
- Aspiring data scientists looking to gain practical skills in AI and analytics.
- Career changers transitioning into tech with structured learning.
- Tech professionals wanting to upskill in machine learning and data engineering.
Next Steps: Start Chapter 1 – Advanced Machine Learning
- Before starting this course you must complete the required prerequisite course: Foundations for Data Science (Prerequisite Track1)