Python AI/ML Job Oriented Training In Bhopal
Classroom Training Fee: ₹3000/- per month ₹5000/- per month
Online Training Fee: ₹2500/- per month ₹4000/- per month
Course Duration : 8 Months
Extras: Interview Preparation & Placement Support Included
Python AI/ML Course Syllabus and Curriculum For 2025
Interview Preparation & Placement Support – "Learn, Practice, and Land Your Dream Job with Confidence!"
Fee: ₹3000/- ₹5000/- (Classroom) | ₹2500/- ₹4000/- (Online) | Duration: 8 Months (32 Weeks)
Class Modes: Offline & Online
Weeks 1-4: Python Basics & Foundations
- Week 1: Introduction to Python, Get Started, Syntax, Comments, and Variables
- Week 2: Data Types, Numbers, Casting, Strings, and Booleans
- Week 3: Operators, Lists, Tuples, Sets, and Dictionaries
- Week 4: Control Flow: If...Else, While Loops, For Loops
Weeks 5-8: Intermediate Python Concepts
- Week 5: Functions, Lambda Functions, Arrays
- Week 6: Classes and Objects, Inheritance, Iterators, Polymorphism
- Week 7: Python Scope, Modules, Dates, Math, and JSON
- Week 8: Regular Expressions, PIP, Try...Except, User Input, String Formatting
Weeks 9-12: File Handling & Data Science with Python
- Week 9: Python File Handling (Reading, Writing, Deleting Files)
- Week 10: Introduction to NumPy, Creating Arrays, Indexing, Slicing
- Week 11: NumPy Data Types, Copy vs View, Shape, Reshaping, Iterating
- Week 12: NumPy Array Operations: Join, Split, Search, Sort, Filter
Weeks 13-16: Advanced NumPy & Data Visualization
- Week 13: NumPy Random, Data Distributions, and Seaborn
- Week 14: NumPy ufuncs (Arithmetic, Logs, Summation, Trigonometric functions)
- Week 15: Introduction to Pandas, Series, DataFrames, Reading CSV/JSON
- Week 16: Analyzing Data in Pandas: Cleaning Data, Removing Duplicates, Correlations
Weeks 17-20: Data Science Concepts
- Week 17: Data Science Fundamentals, Data Preparation, DataFrames
- Week 18: Statistics: Percentiles, Standard Deviation, Variance, Correlation
- Week 19: Advanced Data Science: Linear Functions, Plotting, Slope, Intercept
- Week 20: Linear Regression, Regression Coefficients, P-Value, R-Squared
Weeks 21-24: Machine Learning Basics
- Week 21: Introduction to Machine Learning: Types, Terminology, Clustering
- Week 22: Regression Techniques: Linear Regression, Polynomial Regression
- Week 23: Classification Algorithms: Decision Trees, Logistic Regression
- Week 24: Model Evaluation: Confusion Matrix, ROC Curve, Cross Validation
Weeks 25-28: Deep Dive into Machine Learning
- Week 25: K-means Clustering, K-nearest Neighbors
- Week 26: Bootstrap Aggregation, Grid Search, Hyperparameters
- Week 27: Neural Networks & Deep Learning Introduction
- Week 28: TensorFlow Basics, Operations, Models, Training
Weeks 29-32: Final Projects & Advanced Concepts
- Week 29: Django: Project Setup, Models, Views, Templates
- Week 30: Django CRUD Operations, Admin Panel, User Authentication
- Week 31: Introduction to MySQL & MongoDB: Basic Queries
- Week 32: Capstone Project: Build a Complete Python Data Science or Web Application
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