Data Science & AI Engineering Masterclass

Transform raw big data into intelligent predictive models. Master Python Analytics, Machine Learning, and AI Tools.

Detailed Curriculum Modules

  • NumPy Engine: Vector operations, multi-dimensional coordinate metrics, and array math
  • Pandas DataFrames: Importing external CSV/Excel sheets, parsing rows, data filtering setups
  • Data Wrangling: Neutralizing missing values, drop duplicates maps, and dataset merges

  • Mathematical Bases: Mean, Median, Mode, Standard Deviation, Standard Variance, and Probability
  • Visual Data Plots: Building Histograms, Box plots chart grids, and Heatmaps via Seaborn / Matplotlib
  • Hypothesis Processing: Normal Distributions analysis, Z-Scores validations, and P-Value checks

  • Regression Models: Linear Regression vs Multiple Regression analytics equations
  • Data Classification: Logistic Regression, Decision Tree systems, and Random Forest estimators
  • Model Evaluators: Confusion Matrix evaluation, Precision metrics, Recall loops, and F1-Scores

  • Clustering Tactics: K-Means Clustering grouping, and Elbow method center configurations
  • Scaling Pipelines: Feature Extraction, Data Normalization standardizations using Scikit-Learn

  • Intro to Deep Learning Frameworks: Perceptrons model layout, Activation functions, and weights
  • Overview of Neural Network Layers: Dense layers blueprints, forward/backward prop base

  • Business Intelligence Layouts: Connecting databases to Power BI / Tableau to map reports
  • AI Toolkits 2026 Integration: Utilizing ChatGPT APIs, Prompt Automation scripts execution

Batch Overview

Duration6 Months
Syllabus ModeAdvanced Math & Lab Tracks
Projects2 Machine Learning Projects
EligibilityGraduates / BCA / B.Tech / MCA
Book Free Seat Now