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Complete Machine Learning 2025 A-Z™: 10 Real World Projects

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  • 6,415 Students
  • Updated 11/2025
4.4
(534 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Language
English
Taught by
MG Analytics
Rating
4.4
(534 Ratings)
2 views

Course Overview

Complete Machine Learning 2025 A-Z™: 10 Real World Projects

Complete Beginner to Expert Guide-Data Visualization,EDA,Numpy,Pandas,Math,Statistics,Matplotlib,Seaborn,Scikit,NLP-NLTK

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.

It gives detailed guide on the Data science process involved and Machine Learning algorithms. All the algorithms are covered in detail so that the learner gains good understanding of the concepts. Although Machine Learning involves use of pre-developed algorithms one needs to have a clear understanding of what goes behind the scene to actually convert a good model to a great model.

Our exotic journey will include the concepts of:

  1. Comparison between Artificial intelligence, Machine Learning, Deep Learning and Neural Network.

  2. What is data science and its need.

  3. The need for machine Learning and introduction to NLP (Natural Language Processing).

  4. The different types of Machine Learning – Supervised and Unsupervised Learning.

  5. Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease.

  6. All the important libraries you would need to work on Machine learning lifecycle.

  7. Full-fledged course on Statistics so that you don’t have to take another course for statistics, we cover it all.

  8. Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course.

  9. All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future.

  10. Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems.

  11. More that 20 assignments and assessments allow you to evaluate and improve yourself on the go.

  12. Total 10 beginner to Advance level projects so that you can test your skills.

Course Content

  • 10 section(s)
  • 137 lecture(s)
  • Section 1 Introduction to Data Science and Machine Learning
  • Section 2 Python Basics, Decision Making and Loops
  • Section 3 Python Data Structures
  • Section 4 Python Practice Questions
  • Section 5 OOPS
  • Section 6 Descriptive Statistics
  • Section 7 Inferential Statistics: Intro, Central Limit Theorem,Z-Score,CI
  • Section 8 Hypothesis Testing
  • Section 9 T-Test, chi-Square , AnOVa Test and more
  • Section 10 Case Study: Statistics on House Pricing Data Set

What You’ll Learn

  • Python
  • Machine Learning
  • Statistics and Math
  • Data Science
  • Natural Language Processing
  • Data Analysis
  • Data Visualization


Reviews

  • M
    Mustafa A.
    5.0

    Great course! The theory was explained in a simple way, and the practical part was well-structured Best part is if you have no prior experience in ML, it covers a diverse range of algorithm and models.

  • P
    Punit Mehta
    2.0

    The definition for AI and Machine learning seem ambiguous. There is no clear delineation illustrated between two. Also as a first chapter was expecting more details on advent if neural networks, data science , machine learning and how they merged with each other to give rise to AI . There was no ideation of fundamentals - voice over seemed very robotic and it seemed like a bot doing speech-to-text

  • D
    Deepak Sharma
    5.0

    Comprehensive and engaging! The blend of theory with real-world implementation makes it truly effective. Appreciate the tutor’s methodical teaching style.

  • A
    Ayush Gupta
    5.0

    Well-balanced course with a solid combination of theory and practical projects. The instructor’s organized teaching style really stands out. Highly recommended!

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