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Learn AI Python Machine Learning Data Science Big Data
Rating: 4.5 out of 5(11 ratings)
3,225 students

Learn AI Python Machine Learning Data Science Big Data

Complete Guide to AI, Python, Machine Learning, Data Science and Big Data Analytics for Real-World Applications
Created byLearnify IT
Last updated 4/2026
English
English [Auto],

What you'll learn

  • Master Python programming for AI, Machine Learning, and Data Science
  • Build, train, and deploy Machine Learning models
  • Perform data analysis, visualization, and predictive modeling
  • Work with Big Data tools and frameworks to process and analyze large datasets
  • Apply AI and Machine Learning techniques to real-world projects
  • Gain understanding of Deep Learning, Neural Networks, and advanced analytics

Course content

1 section • 17 lectures • 7h 47m total length

  • Introduction3:31

    Explore AI, machine learning, data science, and big data analytics with Python fundamentals and data visualization. Build skills in NumPy, pandas, and exploratory data analysis to drive data-driven decisions.

  • Introduction to AI, ML, DS, Big Data & Data-Driven Decisions28:49

    Explore AI, machine learning, data science, and big data, with real‑world applications and data‑driven decision making, plus the data science workflow and key subfields.

  • Introduction to Python & Variables, Data Types, Operators27:59

    Learn Python foundations for AI, ML, and data science by mastering variables, data types, and arithmetic, comparison, assignment, and logical operators with practical examples in PyCharm or VS Code.

  • Conditional Statements & Loops29:58

    Explore conditional statements and loops in Python for AI, ML, data science, and big data, learning if elif else, for and while loops, range, and practical examples.

  • Functions & Lists, Tuples, Dictionaries21:02

    Explore functions, lists, tuples, and dictionaries in Python, learn their uses in data science and machine learning, and practice with beginner-friendly code in PyCharm or VS Code.

  • File Handling, OOP Basics, Virtual Environments & Package Management30:17

    Learn how Python handles file I/O, object oriented programming basics, and how to use virtual environments and Pip for package management in AI, machine learning, data science, and big data.

  • Introduction to NumPy with NumPy Arrays and Operations36:21

    Explore numpy fundamentals, arrays, and vectorized operations for ai, machine learning, data science and big data. Learn to install, create 1d/2d arrays, index, slice, reshape, and perform arithmetic.

  • Introduction to Pandas & DataFrames Load, Clean, Transform36:43

    Learn pandas and dataframes to load, clean, and transform data across CSV, Excel, and JSON formats. Master practical steps for loading, cleaning, removing duplicates, and transforming data in Python.

  • Handling Missing Values & Data Merging, Grouping and Aggregation35:42

    Learn to clean and merge data with pandas: handle missing values via drop and imputation, compute mean/median/mode, then group, aggregate, and merge student and grade data.

  • Exploratory Data Analysis (EDA) & Data Visualization with Matplotlib30:24

    Explore exploratory data analysis (EDA) and data visualization with matplotlib using pandas, covering loading csv data, inspecting shape and info, handling missing values, and plotting charts.

  • Advanced Visualization with Seaborn & Feature Engineering & Preprocessing37:39

    Explore data visualization with Seaborn, including histograms, KDE, box and violin plots, scatter plots with regression, pair plots, and heat maps, paired with feature engineering and preprocessing techniques.

  • Descriptive Statistics & Probability Basics36:31

    Learn descriptive statistics to summarize data and assess uncertainty with probability basics, covering mean, mode, median, range, variance, and standard deviation, plus visualizations with pandas, matplotlib, and seaborn.

  • Distributions (Normal, Poisson, Binomial) & Hypothesis Testing40:52

    Explore normal distribution, Poisson distribution, and binomial distribution and perform hypothesis testing using one-sample and two-sample t-tests in Python for AI, machine learning, data science, and big data.

  • Correlation and Covariance & Linear Algebra for ML39:33

    Explore correlation and covariance and the role of linear algebra in ML, using vectors, matrices, and dot products with NumPy and Python tools for PCA, linear regression, and neural networks.

  • Class project 0116:41

    Explore gradient descent in Python to optimize a simple function, using numpy and matplotlib, detailing steps from project setup to visualizing the descent path and saving results.

  • Class project 0215:10

    Join class project two to build your first machine learning model in Python with pandas, scikit-learn, and matplotlib, following a beginner-friendly workflow from data loading to training, testing, and predicting.

  • How to Use AI to Learn Python, AI, Machine Learning, Data Science, and Big Data

Requirements

  • No experience required

Description

Unlock the power of Artificial Intelligence, Python, Machine Learning, Data Science, and Big Data Analytics in this comprehensive, hands-on course. Whether you’re a beginner or an aspiring data professional, this course equips you with the practical skills and knowledge to solve real-world problems using cutting-edge technologies.

What You Will Learn:

  • Fundamentals of Python programming for AI and data analysis

  • Building and deploying Machine Learning models from scratch

  • Exploring Data Science techniques, including data cleaning, visualization, and analysis

  • Working with Big Data Analytics tools to handle massive datasets

  • Implementing AI solutions for real-world projects and business applications

  • Understanding key concepts in Deep Learning, Neural Networks, and Predictive Analytics

Who This Course is For:

  • Anyone passionate about leveraging AI and Big Data to make smarter decisions

Why Choose This Course:

  • Hands-on projects and real-world examples

  • Learn from beginner-friendly to advanced concepts in a structured way

  • Focused on practical applications that can boost your career or business

  • Certificate after course complete

By the end of this course, you will have the confidence and skills to design and implement AI-powered solutions, build machine learning models, analyze complex datasets, and tackle big data challenges.

Start your journey to becoming an AI, Machine Learning, and Data Science expert today!

Who this course is for:

  • Anyone eager to learn practical AI, Machine Learning, and Big Data skills

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