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Course Overview

Accelerate your career in Data Science and AI with a structured certification roadmap aligned to industry-recognized credentials. This program maps each learning stage—foundation, intermediate, and advanced—to globally valued certifications. Students progress from AI fundamentals, statistics, and Python programming into machine learning, deep learning, and advanced AI topics, supported by cloud, databases, and visualization skills. Certifications from AWS, Microsoft, Google Cloud, TensorFlow, MIT, and more provide credibility at every step. The pathway concludes with deployment skills, career preparation modules, and professional credential support for real-world readiness.

Note - We will use Python as the programming language throughout the course.

For programming tasks, we will work with scikit-learn, NumPy, and Pandas.

For deep learning, we will use TensorFlow. All concepts will be taught accordingly.

Course Content

  • Basics Of Python
  • Introduction of Machine Learning
  • Basics of Statistics , Linear Algebra , Probablity
  • Problem Definition
  • Data Collection
  • Data Preprocessing
  • EDA
  • Data Partioning
  • Metrics for Regression and Cllasification
  • Machine Learning Algorithms
  • Simple Linear Regression
  • Model Underfitting , Overfitting
  • Regularisation
  • Logistics Regression
  • Clustering , PCA
  • Tree based Alogorithm
  • SVM
  • KNN
  • Ensamble Techniquies
  • Random Forest , Xtream Random Forest
  • Ada boost
  • Xgboost
  • Extre4am Xgboost
  • Time Series Analysis
  • Recommendation systems
  • Hyper parameter tuning - Gridsearch CV , Random Grid search
  • Model evaulation
  • Model Deployment
  • Model Monitoring
  • Data Drift
  • Model Retrainig
  • Deep Learning ( Only Basics with Regression and Clasification
  • Clouds and Auto ML
  • App Development by Flask ,Streamlit
  • Development through Vscode , GitHub(CI/CD)
  • Containers - Dockers

1: Do I need any experience?

Absolutely! Many AI courses are designed specifically for beginners. They start with foundational concepts and often use beginner-friendly tools and platforms that minimize complex coding. Look for courses labeled "Introductory," "No-code AI," or "AI for Everyone." These will help you build a solid understanding of the principles before you advance to more technical, programming-heavy topics.
These terms are often used interchangeably, but they represent a hierarchy of concepts:
- Artificial Intelligence (AI) is the broadest field, focused on creating machines capable of intelligent behavior.
- Machine Learning (ML) is a subset of AI that gives computers the ability to learn from data without being explicitly programmed for every task.
- Deep Learning (DL) is a further subset of ML that uses complex neural networks to solve advanced problems like image and speech recognition.
Most foundational courses will cover the relationship between these fields, while specialized courses will dive deep into one area.
Our courses are project-based to ensure you gain hands-on experience. You'll work on real-world projects such as building a movie recommendation system, developing a chatbot, or creating an image classifier. These projects form a practical portfolio that demonstrates your skills to employers. This hands-on approach prepares you for roles like AI Specialist, Machine Learning Engineer, or Data Analyst, and equips you with in-demand skills to solve business problems.

Student Reviews

Considering primary motivation for the generation of narratives is a useful concept