Integration Edu Tech

Become a certified AI / ML expert

Upgrade your tech skills with Integration Edutech’s industry-oriented AI / ML Program. 

Duration : 120 Days 
Enroll | Upskill

₹60,000/-

AI / ML Course Features

Beginner-Friendly Course

Easy to Start

IT & Non-IT Background

Open for All

Clear Explanations

Simple Language

Live Instructor-Led Training

Live Classes

Offline Classroom Sessions

Classroom Learning

Mentorship Program

Expert Guidance

Assessment-Based Evaluation

Regular Assessments

Doubt Clearing Sessions

Instant Support

How Can You Learn ?

Enroll by Paying Fee

Complete all Modules and submit required Assignments

Get Certified after Completion

  • What is AI? History and evolution 
  • Types of AI: Narrow AI, General AI, Super AI 
  • Applications of AI in real life (Healthcare, Finance, Education, E-commerce) 
  • AI vs Machine Learning vs Deep Learning 
  • Career paths in AI/ML 
  • Python basics (variables, data types, operators)
  • Control statements (if, loops)
  • Functions and modules
  • File handling
  • Exception handling
  • Introduction to Jupyter Notebook
  • Linear Algebra basics (vectors, matrices)
  • Statistics and Probability
  • Mean, Median, Mode
  • Variance and Standard Deviation
  • Probability distributions
  • Correlation and covariance
  • Introduction to NumPy
  • Data manipulation using Pandas
  • Data cleaning and preprocessing
  • Handling missing values
  • Data visualization using Matplotlib & Seaborn
  • Exploratory Data Analysis (EDA)
  • What is Machine Learning?
  • Types of ML:
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Machine Learning workflow
  • Training vs Testing data
  • Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Decision Trees
  • Random Forest
  • Support Vector Machine (SVM)
  • Clustering concepts
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN
  • Dimensionality Reduction:
  • PCA (Principal Component Analysis)
  • Performance metrics:
  • Accuracy
  • Precision
  • Recall
  • F1-Score
  • Confusion Matrix
  • Bias-Variance tradeoff
  • Cross-Validation
  • Hyperparameter tuning
  • Basics of NLP
  • Text preprocessing
  • Tokenization
  • Stemming & Lemmatization
  • Bag of Words & TF-IDF
  • Sentiment Analysis
  • Image processing concepts
  • OpenCV introduction
  • Image classification
  • Face detection basics
  • Object detection overview
  • Ethical issues in AI
  • Bias in Machine Learning
  • Data privacy and security
  • Explainable AI
  • AI regulations and future trends
  • Google Colab
  • Jupyter Notebook
  • Git & GitHub
  • Kaggle datasets
  • Model deployment basics
  • Student Performance Prediction
  • House Price Prediction
  • Spam Email Detection
  • Customer Segmentation
  • Sentiment Analysis System
  • Problem statement selection
  • Dataset collection
  • Model building & evaluation
  • Result analysis
  • Project documentation
  • Final presentation
  •  

Certify your Learning

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