Udemy

Mastering Data Science & AI with Python & Real-World Project

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  • 2,268 Students
  • Updated 8/2025
  • Certificate Available
4.5
(36 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
21 Hour(s) 51 Minute(s)
Language
English
Taught by
Temotec AI Learning
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.5
(36 Ratings)
2 views

Course Overview

Mastering Data Science & AI with Python & Real-World Project

A complete hands-on Python, Machine Learning, LLM-powered AI apps featuring 9 real-world projects, Streamlit & Ollama.

Unlock the full potential of data science and artificial intelligence with “Mastering Data Science & AI with Python”—a comprehensive, beginner-to-advanced course designed to transform you into a job-ready data scientist and AI developer. Whether you're just starting your coding journey or looking to upskill and build real-world AI-powered applications, this course covers everything you need in one complete package.

We begin with the fundamentals of Python, ensuring you're well-equipped with programming basics and critical libraries like NumPy and Pandas. You'll quickly progress to manipulating and analyzing data efficiently, including accessing, cleaning, and filtering DataFrames. Learn how to visualize your insights with powerful charts and graphs that bring your data to life.

From there, you’ll dive deep into statistics for data science, the cornerstone for understanding machine learning. You'll master core statistical concepts essential for model development and evaluation. The machine learning section then takes you through supervised and unsupervised learning—covering regression, binary and multiclass classification, clustering algorithms, and dimensionality reduction techniques like t-SNE and PCA.

Hands-on practice is the heart of this course. You’ll complete 9 end-to-end projects that simulate real industry scenarios:

  • Automate business workflows with Pandas

  • Analyze large datasets with Google Apps

  • Build a movie recommendation engine using Non-negative Matrix Factorization

  • Develop predictive models and evaluate them using advanced techniques

  • Build and deploy a credit risk prediction app with XGBoost and Streamlit

  • Create LLM-powered AI apps using Ollama, LangChain, and Streamlit—no cloud required

  • Implement local Python libraries for AI interactions

What truly sets this course apart is its focus on local LLMs and AI automation tools. You’ll explore cutting-edge frameworks like Ollama, interact with models through Web UI, LM Studio, and even build your own AI Code Assistant and RAG-based AI Research App—equipping you with the skills to develop, test, and deploy modern AI systems without relying on expensive APIs or cloud services.

All modules are crafted with a blend of theory, code-alongs, and practical exercises. You'll walk away not only with technical knowledge but also a portfolio of working applications that showcase your expertise in Python programming, machine learning, and AI.

By the end of this course, you will be able to:

  • Code in Python with confidence

  • Analyze, visualize, and model data effectively

  • Understand and implement ML algorithms from scratch

  • Build real-world projects that demonstrate your skills

  • Develop and deploy AI apps using local LLMs and tools like Ollama and Streamlit

This course is ideal for aspiring data scientists, developers, and AI enthusiasts eager to build practical, high-impact solutions. If you're looking to transition into tech, upgrade your skills, or break into AI development, this is the only course you’ll need.

Course Content

  • 29 section(s)
  • 176 lecture(s)
  • Section 1 Introduction
  • Section 2 Python Refresher
  • Section 3 Python Numpy Library
  • Section 4 Python Pandas Library
  • Section 5 Project 1 Using Pandas + Automation to Manage a Business Email List
  • Section 6 Accessing, Manipulating & Filtering DataFrames.
  • Section 7 Data Visualization
  • Section 8 Statistics for Data Science
  • Section 9 Project 2 Google App Data Analysis
  • Section 10 Introduction to ML & Supervised Learning
  • Section 11 Regression Supervised ML Algorithm
  • Section 12 Binary Classification & Multiclass ML Supervised Classifiers
  • Section 13 Feature Engineering for ML Supervised Learning Algorithms
  • Section 14 How to Evaluate Multiple Models
  • Section 15 Advanced Topics regarding ML Supervised Learning Algorithms
  • Section 16 Clustering ML Unsupervised Learning Algorithms
  • Section 17 t-SNE for 2-dimensional maps
  • Section 18 PCA ML Unsupervised Learning Algorithm
  • Section 19 Project 3 Building Recommender System using NMF
  • Section 20 Project 4 Predictive Modeling
  • Section 21 Project 5: Credit Risk Prediction with XGBoost Streamlit App
  • Section 22 Ollama and Local LLMs
  • Section 23 Different Apps to interact with Ollama: Msty, Open Web UI, LM Studio & Streamlit
  • Section 24 Ollama Python Library.
  • Section 25 Project 6: Building Real World Affordable AI Apps: RAG App.
  • Section 26 Project 7: Building Real World Affordable AI Apps: AI Code Assistant App.
  • Section 27 Project 8: Ollama Mullti-Modles Model AI Data Science Assistant.
  • Section 28 Pitch & Demo: Offline AI Summarizer with Ollama Role Play
  • Section 29 Thanks

What You’ll Learn

  • Learn to code in Python and apply core libraries like NumPy and Pandas for data manipulation and analysis.
  • Master essential statistics and data visualization techniques to extract actionable insights from data.
  • Understand and implement key machine learning algorithms including regression, classification, clustering, and dimensionality reduction.
  • Build and deploy real-world AI and machine learning projects using tools like Streamlit, and Ollama-powered LLMs.


Reviews

  • M
    Maksym Hlozhyk
    3.0

    Reading of code symbol by symbol in some chapters is little bit extra, explaining of syntaxis is enough.

  • G
    Gavin Cole
    5.0

    So beautifully balanced learning journey where Python meets real-world data challenges in the most natural way

  • F
    Finn Carmichael
    5.0

    I could feel the instructor’s depth of knowledge in every topic, yet the delivery stays smooth and engaging

  • A
    Anshul Parmar
    5.0

    great to have like these course.

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