Sree Technologies

Python with AI/ML

Learn Python Programming, Data Analysis, Machine Learning, Deep Learning, and Artificial Intelligence through hands-on projects, real-world datasets, and industry-standard tools such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch.

50,000 75,000

Duration

150 Days

Mode

Offline / Online

Projects

2 Real-Time

Placement

Supported

Certification

Included

AI Enabled

Yes

COURSE OVERVIEW

Why Choose This Program?

Python with AI/ML is one of the most in-demand technologies, empowering businesses to build intelligent applications, automate processes, and make data-driven decisions. At Sree Technologies, you'll master Python programming, data analysis, machine learning, deep learning, and AI frameworks through hands-on projects and real-world datasets. Our industry-focused curriculum, expert mentorship, and practical training equip you with the skills to develop intelligent AI solutions and build a successful career in Artificial Intelligence and Machine Learning.

WHAT YOU'LL LEARN

Course Curriculum

Python Programming Fundamentals
  • Introduction to Python & AI/ML Ecosystem
  • Python Installation & Environment Setup
  • Variables & Data Types
  • Operators
  • Conditional Statements
  • Loops
  • Functions
  • Lambda Functions
  • Modules & Packages
  • Exception Handling
  • File Handling
  • Object-Oriented Programming (OOP)
  • Virtual Environments
  • Python Best Practices

Python Libraries for Data Science
  • NumPy
  • Arrays & Operations
  • Mathematical Functions
  • Data Manipulation
  • Pandas
  • Series & DataFrames
  • Data Cleaning
  • Data Transformation
  • Data Analysis
  • Matplotlib & Seaborn
  • Data Visualization
  • Charts & Graphs
  • Exploratory Data Analysis

Statistics & Mathematics for AI/ML
  • Descriptive Statistics
  • Probability Fundamentals
  • Probability Distributions
  • Mean, Median, Mode
  • Variance & Standard Deviation
  • Correlation & Covariance
  • Linear Algebra Basics
  • Matrix Operations
  • Calculus Fundamentals

Data Preprocessing & Analysis
  • Data Collection
  • Data Cleaning
  • Handling Missing Values
  • Feature Selection
  • Feature Engineering
  • Data Encoding
  • Data Scaling
  • Exploratory Data Analysis (EDA)
  • Data Visualization Techniques

Machine Learning Fundamentals
  • Introduction to Machine Learning
  • AI vs ML vs Deep Learning
  • Machine Learning Workflow
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning Basics
  • Supervised Learning Algorithms
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Unsupervised Learning Algorithms
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN
  • Principal Component Analysis (PCA)

Machine Learning Model Development
  • Training & Testing Data
  • Model Evaluation
  • Cross Validation
  • Hyperparameter Tuning
  • Overfitting & Underfitting
  • Bias & Variance
  • Feature Importance
  • Model Deployment Basics

Deep Learning with Python
  • Introduction to Neural Networks
  • Artificial Neural Networks (ANN)
  • Activation Functions
  • Forward & Back Propagation
  • Loss Functions
  • Optimizers
  • TensorFlow & Keras
  • PyTorch Fundamentals

Computer Vision
  • Introduction to Computer Vision
  • Image Processing Basics
  • OpenCV
  • Image Classification
  • Object Detection
  • CNN Architecture
  • Transfer Learning
  • Face Detection Applications

Natural Language Processing (NLP)
  • Introduction to NLP
  • Text Processing
  • Tokenization
  • Stemming & Lemmatization
  • Stop Words Removal
  • Text Vectorization
  • TF-IDF
  • Word Embeddings
  • Sentiment Analysis
  • Text Classification

Generative AI & Large Language Models
  • Introduction to Generative AI
  • LLM Fundamentals
  • Transformers Architecture
  • Attention Mechanism
  • Prompt Engineering
  • OpenAI APIs
  • Hugging Face Models
  • LangChain Basics
  • Retrieval Augmented Generation (RAG)
  • Vector Databases
  • AI Application Development

AI Agents & Advanced AI
  • Introduction to AI Agents
  • Agent Architecture
  • Autonomous AI Workflows
  • LangGraph
  • CrewAI
  • AI Agent Development
  • Tool Calling
  • Multi-Agent Systems
  • AI Automation

ML Deployment & MLOps
  • Model Saving & Loading
  • Flask/FastAPI for ML APIs
  • Streamlit Applications
  • Docker Basics
  • Cloud Deployment Basics
  • Model Monitoring
  • ML Pipelines
  • Version Control with Git & GitHub

AI Tools & Development Environment
  • Jupyter Notebook
  • Google Colab
  • VS Code
  • Git & GitHub
  • Anaconda
  • Python Virtual Environments
  • AI Coding Assistants
  • ChatGPT for AI Development
  • GitHub Copilot

Real-Time Projects & Career Preparation
  • House Price Prediction System
  • Recommendation System
  • Image Classification Application
  • Chatbot Using NLP
  • GitHub Portfolio Development
  • Industry Use Cases
  • AI/ML Project Discussions
  • Mock Interviews

CAREER OUTCOMES

Roles You Can Target

  • Python Developer
  • Machine Learning Engineer
  • Deep Learning Engineer
  • AI Application Developer
  • Generative AI Engineer
  • Computer Vision Engineer

ELIGIBILITY

Who Should Join?

  • Basic computer knowledge
  • Familiarity with using the internet and software applications
  • No prior programming experience required
  • Basic mathematics and logical thinking are helpful but not mandatory