AI & Machine Learning with Python (Complete Course)

Exploring Artificial Intelligence Through Python-Based Learning

Artificial Intelligence and Machine Learning are transforming the way software applications analyze information, automate tasks, and solve complex problems. Learning AI with Python helps students understand how intelligent systems are developed using programming, data analysis, algorithms, and machine learning models.

This course introduces learners to Python programming, data handling, machine learning techniques, deep learning concepts, AI applications, and model deployment. Buresaida Dsk provides online AI & Machine Learning classes in Telugu, Hindi, and English with a structured approach that combines theoretical understanding with practical implementation.

The course is designed for learners who want to develop skills in artificial intelligence, data analytics, and machine learning using Python-based technologies

Duration: 18 Weeks

Module 1: Python Programming (Week 1–2)

• Python Installation

• Variables & Data Types

• Operators

• Input & Output

• Conditional Statements

• Loops

• Functions

• Lambda Functions

• Modules & Packages

• File Handling

• Exception Handling

• OOP in Python

• Mini Projects

Module 2: NumPy (Week 3)

• Arrays

• Array Operations

• Indexing & Slicing

• Broadcasting

• Matrix Operations

• Linear Algebra

• Random Module

Module 3: Pandas (Week 4)

• Series

• DataFrames

• Reading CSV/Excel

• Data Cleaning

• Missing Values

• GroupBy

• Merge & Join

• Pivot Tables

• Apply Function

Module 4: Data Visualization (Week 5)

• Matplotlib

• Bar Chart

• Pie Chart

• Histogram

• Scatter Plot

• Line Chart

• Box Plot

• Power BI Basics

Module 5: Statistics & Probability (Week 6)

• Mean

• Median

• Mode

• Variance

• Standard Deviation

• Normal Distribution

• Correlation

• Covariance

• Probability Basics

Module 6: Data Preprocessing (Week 7)

• Missing Values

• Duplicate Values

• Outlier Detection

• Encoding

• Scaling

• Normalization

• Feature Engineering

• Train-Test Split

• Pipelines

Module 7: Machine Learning Fundamentals (Week 8)

• AI vs ML vs DL

• Types of Machine Learning

• ML Workflow

• Bias & Variance

• Overfitting

• Underfitting

• Cross Validation

Module 8: Regression Algorithms (Week 9)

• Linear Regression

• Multiple Regression

• Polynomial Regression

• Ridge Regression

• Lasso Regression

• ElasticNet

Project: House Price Prediction

Module 9: Classification Algorithms (Week 10)

• Logistic Regression

• KNN

• Decision Trees

• Random Forest

• Naive Bayes

• Support Vector Machine (SVM)

Project: Diabetes Prediction

Module 10: Clustering (Week 11)

• K-Means

• Hierarchical Clustering

• DBSCAN

Project: Customer Segmentation

Module 11: Dimensionality Reduction (Week 12)

• PCA

• t-SNE

• Feature Selection

Module 12: Model Evaluation (Week 13)

• Accuracy

• Precision

• Recall

• F1 Score

• ROC Curve

• Confusion Matrix

• Hyperparameter Tuning

• Grid Search CV

Module 13: Deep Learning Basics (Week 14)

• Neural Networks

• TensorFlow

• Keras

• ANN

• CNN Introduction

Project: Image Classification

Module 14: Deployment (Week 15)

• Flask/FastAPI Basics

• API Development

• Model Deployment

• Streamlit

• GitHub

• Render Deployment

Module 15: Capstone Projects (Week 16)

Students will build complete end-to-end projects such as:

• House Price Prediction

• Movie Recommendation System

• Face Detection System

• Customer Churn Prediction

• Sales Forecasting

• Credit Card Fraud Detection

• Resume Screening System

• Student Performance Prediction

Bonus Modules (week 17 and 18)

• SQL for Data Science

• Git & GitHub

• Power BI Dashboard

• Exploratory Data Analysis (EDA)

• Feature Engineering

• Resume Building

• LinkedIn Optimization

• Mock Interviews

• Placement Preparation

Tools Covered

• Python

• NumPy

• Pandas

• Matplotlib

• Scikit-learn

• TensorFlow

• Keras

• OpenCV

• FastAPI

• Streamlit

• Power BI

• SQL

• Git & GitHub

• Jupyter Notebook

• Google Colab

• VS Code

FAQs

1. What will students learn in an AI and Machine Learning with Python course?

Students learn Python programming, data analysis, machine learning algorithms, deep learning basics, AI concepts, and practical model development using Python tools.

2. Is Python required before learning Machine Learning?

Basic programming knowledge is helpful, but many AI and Machine Learning courses begin with Python fundamentals before introducing advanced concepts.

3. What projects can be created after learning AI and Machine Learning?

Learners can build projects involving prediction models, recommendation systems, classification applications, data analysis solutions, and AI-based applications.