AI/ML with Python

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Training Key Features

  • Job-Oriented Skills
  • 30-Day Structured Roadmap
  • End-to-End ML Workflow
  • Beginner-Friendly Approach
Learning Format: Live Training Download Syllabus

What will be Cover ?

Module 1 – Python & Math Foundations
  • Python Basics – Variables, Data Types, Loops, Functions
  • Data Structures – Lists, Dictionaries, Tuples, Sets
  • File I/O, Exception Handling, Comprehensions
  • NumPy – Arrays, Indexing, Slicing, Operations
  • Pandas – DataFrames, Reading CSV, Cleaning Data
  • Matplotlib & Seaborn – Data Visualization
  • Math for ML – Linear Algebra & Calculus (Basics)
Module 2 – Statistics & Core Machine Learning
  • Probability, Mean, Median, Variance, Std Dev
  • Hypothesis Testing & Distributions
  • Introduction to ML – Types, Supervised vs Unsupervised
  • Scikit-learn Overview, Train/Test Split
  • Linear Regression (with project)
  • Logistic Regression & Evaluation Metrics
  • Mini Project – Regression & Classification
Module 3 – Advanced ML Algorithms
  • Decision Trees, Random Forest
  • KNN & Naive Bayes
  • SVM (Support Vector Machine)
  • K-Means Clustering (Unsupervised)
  • Dimensionality Reduction (PCA)
  • Cross Validation & Model Tuning
  • Mini Project – Model Comparison
Module 4 – Deep Learning & Capstone Project
  • Neural Networks Overview – Perceptron & ANN
  • TensorFlow/Keras Basics
  • Build Your First ANN Model (with Keras)
  • CNN for Image Classification (basics)
  • Model Evaluation & Deployment (Pickle/Joblib)
  • End-to-End ML Project (Data → Model → Output)
  • Capstone Project – Plan, Build, Finalize, Test & Deploy with Portfolio Upload
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Course Detail

Benifits 

This course is designed to help you build strong foundations in Python, Math, and Statistics while gaining hands-on experience with real-world Machine Learning and Deep Learning projects. By the end of the program, you’ll be confident in using industry-standard tools like NumPy, Pandas, Scikit-learn, and TensorFlow to build, evaluate, and deploy ML models. With multiple projects and a capstone added to your GitHub portfolio, you’ll be job-ready and equipped with practical skills for a career in Data Science and AI.

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