Description

Topics Covered:

✅Introduction to Python

✅Variables, Data Types, Operators

✅Conditional Statements & Loops

✅Functions & Lambda Expressions

✅File Handling

✅Exception Handling

✅Python Libraries Overview

Tools:

✅Python 3.x

✅Jupyter Notebook

✅VS Code

Description

Topics Covered:

✅NumPy

✅Arrays, Indexing, Slicing

✅Mathematical Operations

✅Pandas

✅Series & DataFrames

✅Data Cleaning

✅Handling Missing Values

✅Data Transformation

✅Exploratory Data Analysis (EDA)

Hands-on:

✅Data cleaning using real datasets

✅Exploratory data analysis project

Description

Mathematics:

✅Linear Algebra Basics

✅Vectors & Matrices

✅Dot Product

✅Eigenvalues (Conceptual)

Statistics:

✅Mean, Median, Mode

✅Variance & Standard Deviation

✅Probability Distributions

✅Hypothesis Testing

✅Correlation & Covariance

✅Bias–Variance Tradeoff

Description

Topics Covered:

✅Matplotlib

✅Seaborn

✅Plotly (Intro)

✅Data storytelling

✅Dashboard concepts

Hands-on:

✅Visualization of business datasets

Description

Topics Covered:

✅Introduction to Databases

✅SQL Basics

✅SELECT, WHERE, GROUP BY

✅JOINs

✅Subqueries

✅Introduction to NoSQL (MongoDB)

Description

Core Concepts:

✅What is Machine Learning?

✅Types of ML

✅ML Workflow

✅Feature Engineering

✅Train-Test Split

✅Cross Validation

Description

Algorithms:

✅Linear Regression

✅Logistic Regression

✅Decision Tree

✅Random Forest

✅Support Vector Machine (SVM)

✅K-Nearest Neighbors (KNN)

Hands-on:

✅Prediction & classification projects

Description

Algorithms:

✅K-Means Clustering

✅Hierarchical Clustering

✅DBSCAN

✅Principal Component Analysis (PCA)

Description

Topics Covered:

✅Confusion Matrix

✅Accuracy, Precision, Recall, F1 Score

✅ROC-AUC Curve

✅Overfitting & Underfitting

✅Hyperparameter Tuning

✅GridSearch & RandomSearch

Description

Topics Covered:

✅Introduction to AI

✅Intelligent Agents

✅Search Algorithms

✅Rule-Based Systems

✅AI vs ML vs Deep Learning

Description

Neural Networks:

✅Perceptron

✅Activation Functions

✅Loss Functions

✅Backpropagation

✅Optimizers

Frameworks:

✅TensorFlow

✅Keras

✅PyTorch (Basics)

Description

Topics Covered:

✅Text Preprocessing

✅Tokenization & Lemmatization

✅Bag of Words & TF-IDF

✅Word Embeddings

Description

Topics Covered:

✅Image Processing Basics

✅OpenCV

✅CNN Architecture

✅Image Classification

✅Object Detection Basics

Description

Topics Covered:

✅Model Serialization

✅Flask / FastAPI

✅REST APIs

✅Docker Basics

✅Cloud Deployment Overview

✅Model Monitoring

Description

Topics Covered:

✅Data Privacy

✅Bias in AI

✅Fairness

✅Explainable AI (XAI)

Description

Mandatory Projects:

✅Data Cleaning & EDA Project

✅ML Prediction Project

✅NLP Project

✅Computer Vision Project

✅End-to-End AI/ML Deployment Project

Description

INTERNSHIP OUTCOMES

✅Industry-ready skills

✅Hands-on experience

✅GitHub portfolio

✅Internship Certificate

✅Interview preparation

TOOLS & TECHNOLOGIES

✅Python

✅SQL

✅Pandas, NumPy

✅Scikit-learn

✅TensorFlow / PyTorch

✅Git & GitHub

✅Flask / FastAPI

✅Docker

Description

Software Internship – Data Science + AI + Machine Learning

Congratulations - You have been awarded the our pretigiuos internship certification along with other certificates from coursematter that you can attach on your LinkedIn profile.

WHAT YOU GET?

BELOW LINKEDIN COMPATIBLE CERTIFICATES & LETTERS

✅INTERNSHIP OFFER LETTER

✅INTERNSHIP EXPERIENCE LETTER

✅PROGRAM CERTIFICATE