Ayush-Machine-Learning
Learn Machine Learning Course by Ayush with comprehensive video tutorials and hands-on projects.
Meet Your Instructor: Ayush
Ayush is a skilled machine learning instructor and data scientist specializing in practical ML workflows, model deployment, and real-world AI applications. With extensive experience in developing production-ready machine learning solutions, Ayush brings industry expertise to his teaching. His courses focus on building end-to-end ML systems, from data preprocessing and model training to deployment and monitoring. Ayush emphasizes hands-on learning through real-world projects, helping students develop the practical skills needed to succeed as ML engineers in the industry.
Experience: 5+ years
Students Helped: 12,000+
Specialization: Machine Learning & AI Application Development
Course Overview
This comprehensive course is designed to take you from foundational concepts to advanced implementation in machine learning & ai application development. You'll learn through application-oriented learning with hands-on projects, real-world case studies, and focus on production-ready ml workflows, building real-world projects that demonstrate your skills and enhance your portfolio.
Whether you're looking to start a new career in technology or advance your current skills, this course provides the structured learning path and practical experience you need to succeed in today's competitive tech industry.
Course Curriculum
Course Content
1. Say Hi to Ayush!
2.What to expect from the course
3.What things not to do while doing the course
4.Study Tips from Ayush
Lecture 1
Lecture 2
Lecture 3
Lecture 4
Lecture 01 - Everything you need to know about Linear Algebra
Lecture - 02 Linear Algebra Part-02
Lecture - 03 Linear Algebra Part-03
Lecture - 04 Types of Matrices
Lecture - 05 Determinant
Lecture - 06 Cofactor, Adjugate & Inverse of a Matrix
Lecture - 07 Trace of a Matrix, Hadamard & Kronecker product
Lecture - 08 Systems of Equations & Solving It
Lecture 1 - Intro to Python Day - 01
Lecture 2 - More About to Python Day - 01
Lecture 3 - The Atoms Of Python Day - 02
Lecture 4 - Variables Day - 02
Lecture 5 - String Day - 02
Lecture 6 - Numbers Day - 02
Lecture 7 - Truthiness Day - 02
Lecture - 8 Input & Output Day - 02
Lecture 9 - OPERATORS. the workers of python Day - 03
Lecture 10 - Conditional Flow Day- 03
Lecture 11 - Lists Day - 04
Lecture 12 - Tuples & Mutability Day - 04
Lecture 13 - Dictionaries Day - 04
Lecture 14 - Sets & Nesting Day - 04
Lecture 15 - Repetition is BAD Day - 04
Lecture - 16 Transferring State Day - 04
Lecture 17
Lecture 18
Lecture 19
Lecture 20
1.1 What is NumPy
1.2 NumPy Arrays and Python List
2.1 Creation of Arrays
2.2 Basic Operations
2.3 Concept of Slicing and Indexing
2.4 Reshaping, Splitting, Stacking Arrays
2.5 Broadcasting
Plotting Numpy Arrays
IO Handling with Numpy
5.1 Masking of Arrays
5.2 Structured Arrays
1. Introduction to Pandas
1.2 Pandas Data Structures
2.1 Data Transformation with Pandas - Grouping, Merging, and Concatenating
2.3 Sorting, Filtering, Mapping of Data
2.2 Indexing Slicing
Data Cleaning
Data Exploration
Time Series Analysis with Pandas
Time Series Analysis with Pandas - Part - 02
Lecture 01 What_is_Calculus
Lecture _- 02 ( Reviewing Functions For Calculus)
Lecture _- 03 ( Reviewing Trigonometry )
Lecture _- 04 ( Introduction to Limits & Continuity )
Lecture _- 05 ( Evaluating Limits )
Lecture _- 06 ( Differentiation Part-1 )
Lecture _- 07 ( Basic Differentiation Rules ) __ Chapter- Calculus
Lecture - 08 ( Product Rule and Quotient Rule )
Lecture- 09 ( Chain Rule of Differentiation )
Module 01 Introduction to Databases.izsGLrm4
Module 02 Introduction to SQL
Module 03 SQL Core
Module 04 SQL Operators
Lecture 01 Comprehensive Intro to ML
Lecture 02 Comprehensive Intro to ML
Lecture 03 Comprehensive Intro to ML
Lecture 04 Comprehensive Intro to ML
Lecture 05 Comprehensive Intro to ML
Lecture_01 - Regression Analysis Foundations
Lecture_02 - Regression Analysis Intermediate
Lecture 03 - MLR Intermediate
Lecture 04 - Regression Advanced
Lecture 05 - Simple Linear Regression Project
Lecture 06 - End to End Linear Regression Project(1)
Lecture 01 - Logistic Regression
Lecture 02 - Logistic Regression
Lecture 03 - Logistic Regression
Logistic Regression Practicals
MLOps Fundamentals Lecture-01
MLOps Fundamentals Lecture-02
MLOps Fundamentals Lecture-03
MLOps Fundamentals Lecture-04
MLOps Fundamentals Lecture-05
MLOps Fundamentals Lecture-06
MLOps Fundamentals Lecture-07
MLOps Fundamentals Lecture-08
Lecture1
Lecture2
Lecture3
Lecture4
Lecture5
Lecture6
Lecture7
Lecture8
Lecture1
Lecture2
Lecture3
Lecture4
Lecture5
Lecture6
Lecture7
Lecture8
Lecture9
Lecture2
Lecture3
Classification Measures Lecture-04
Lecture1
Lecture2
4.Stacking Ensemble Learning
Bias & Variance Tradeoff, Expectation Minimization
Lecture1
Lecture2
Lecture3
Lecture1
Lecture2
Lecture3
Lecture4
Lecture5
Lecture6
Lecture7
Lecture8
Lecture9
1.0 Introduction
1.1 Exploring Data
1.2 Processing Data
1.3 Training of Model
1.4 Model Tuning
Requirements
- Basic understanding of Python programming
- Knowledge of basic mathematics and statistics
- Internet connection for video streaming
- Python environment with Jupyter Notebook or VS Code
- Understanding of data analysis and visualization concepts
Course Features
Course Details
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