Machine Learning Concepts and Application of ML using Python

Learn core concepts of Machine Learning. Apply ML techniques to real-world problems and develop AI/ML based applications
Instructor:
Uplatz Training
3,936 students enrolled
English [Auto]
Learn the A-Z of Machine Learning from scratch
Build your career in Machine Learning, Deep Learning, and Data Science
Become a top Machine Learning engineer
Core concepts of various Machine Learning methods
Mathematical concepts and algorithms used in Machine Learning techniques
Solve real world problems using Machine Learning
Develop new applications based on Machine Learning
Apply machine learning techniques on real world problem or to develop AI based application
Analyze and implement Regression techniques
Linear Algebra basics
A-Z of Python Programming and its application in Machine Learning
Python programs, Matplotlib, NumPy, basic GUI application
File system, Random module, Pandas
Build Age Calculator app using Python
Machine Learning basics
Types of Machine Learning and their application in real-life scenarios
Supervised Learning - Classification and Regression
Multiple Regression
KNN algorithm, Decision Tree algorithms
Unsupervised Learning concepts & algorithms
AHC algorithm
K-means clustering & DBSCAN algorithm and program
Solve and implement solutions of Classification problem
Understand and implement Unsupervised Learning algorithms

Uplatz offers this in-depth course on Machine Learning concepts and implementing machine learning with Python.

Objective: Learning basic concepts of various machine learning methods is primary objective of this course. This course specifically make student able to learn mathematical concepts, and algorithms used in machine learning techniques for solving real world problems and developing new applications based on machine learning.

Course Outcomes: After completion of this course, student will be able to:

1. Apply machine learning techniques on real world problem or to develop AI based application

2. Analyze and Implement Regression techniques

3. Solve and Implement solution of Classification problem

4. Understand and implement Unsupervised learning algorithms

Topics

  • Python for Machine Learning

Introduction of Python for ML, Python modules for ML, Dataset, Apply Algorithms on datasets, Result Analysis from dataset, Future Scope of ML.

  • Introduction to Machine Learning

What is Machine Learning, Basic Terminologies of Machine Learning, Applications of ML, different Machine learning techniques, Difference between Data Mining and Predictive Analysis, Tools and Techniques of Machine Learning.

  • Types of Machine Learning

Supervised Learning, Unsupervised Learning, Reinforcement Learning. Machine Learning Lifecycle.

  • Supervised Learning : Classification and Regression

Classification: K-Nearest Neighbor, Decision Trees, Regression: Model Representation, Linear Regression.

  • Unsupervised and Reinforcement Learning

Clustering: K-Means Clustering, Hierarchical clustering, Density-Based Clustering.

Detailed Syllabus of Machine Learning Course

1. Linear Algebra

  • Basics of Linear Algebra

  • Applying Linear Algebra to solve problems

2. Python Programming

  • Introduction to Python

  • Python data types

  • Python operators

  • Advanced data types

  • Writing simple Python program

  • Python conditional statements

  • Python looping statements

  • Break and Continue keywords in Python

  • Functions in Python

  • Function arguments and Function required arguments

  • Default arguments

  • Variable arguments

  • Build-in functions

  • Scope of variables

  • Python Math module

  • Python Matplotlib module

  • Building basic GUI application

  • NumPy basics

  • File system

  • File system with statement

  • File system with read and write

  • Random module basics

  • Pandas basics

  • Matplotlib basics

  • Building Age Calculator app

3. Machine Learning Basics

  • Get introduced to Machine Learning basics

  • Machine Learning basics in detail

4. Types of Machine Learning

  • Get introduced to Machine Learning types

  • Types of Machine Learning in detail

5. Multiple Regression

6. KNN Algorithm

  • KNN intro

  • KNN algorithm

  • Introduction to Confusion Matrix

  • Splitting dataset using TRAINTESTSPLIT

7. Decision Trees

  • Introduction to Decision Tree

  • Decision Tree algorithms

8. Unsupervised Learning

  • Introduction to Unsupervised Learning

  • Unsupervised Learning algorithms

  • Applying Unsupervised Learning

9. AHC Algorithm

10. K-means Clustering

  • Introduction to K-means clustering

  • K-means clustering algorithms in detail

11. DBSCAN

  • Introduction to DBSCAN algorithm

  • Understand DBSCAN algorithm in detail

  • DBSCAN program

LINEAR ALGEBRA FOR MACHINE LEARNING

1
1 INTRODUCTION TO LINEAR ALGEBRA
2
1.1 INTRODUCTION TO LINEAR ALGEBRA
3
1.2 LINEAR ALGEBRA BASICS
4
1.2.1 LINEAR ALGEBRA BASICS
5
1.2.2 LINEAR ALGEBRA BASICS
6
1.2.3 LINEAR ALGEBRA BASICS
7
1.2.4 LINEAR ALGEBRA BASICS
8
1.2.5 LINEAR ALGEBRA BASICS
9
1.2.6 LINEAR ALGEBRA BASICS
10
1.2.7 LINEAR ALGEBRA BASICS
11
1.2.8 LINEAR ALGEBRA BASICS
12
1.2.9 LINEAR ALGEBRA BASICS
13
1.2.9.1 LINEAR ALGEBRA BASICS
14
1.2.9.2 LINEAR ALGEBRA BASICS
15
1.2.9.2.1 LINEAR ALGEBRA BASICS

PYTHON PROGRAMMING

1
2.1 INTRODUCTION TO PYTHON
2
2.1.1 INTRODUCTION TO PYTHON
3
2.1.2 PYTHON DATATYPES
4
2.1.3 PYTHON OPERATORS
5
2.1.4 ADVANCED DATA TYPES
6
2.1.5 SIMPLE PYTHON PROGRAM
7
2.1.6 PYTHON CONDITION STATEMENTS
8
2.1.7 PYTHON LOOPING STATEMENTS
9
2.1.8 - BREAK AND CONTINUE KEYWORDS IN PYTHON
10
2.1.9 FUNCTIONS IN PYTHON
11
2.1.9.1 FUNCTION ARGUMENTS
12
2.1.9.2 FUNCTION REQUIRED ARGUMENTS
13
2.1.9.3 DEFAULT ARGUMENTS
14
2.1.9.4 VARIABLE ARGUMENTS
15
2.2 BUILT IN FUNCTIONS
16
2.2.1 BUILT IN FUNCTIONS
17
2.2.2 SCOPE OF VARIABLES
18
2.2.3 PYTHON MATH MODULE
19
2.2.3.1 PYTHON MATH MODULE
20
2.2.4 PYTHON MATPLOTLIB MODULE
21
2.2.5 A BASIC GUI APPLICATION
22
2.2.6 A BASIC GUI APPLICATION
23
2.3 NUMPY BASICS
24
2.3.1 NUMPY BASICS
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2.3.2 NUMPY BASICS
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2.3.3 NUMPY BASICS
27
2.3.4 NUMPY BASICS
28
2.3.5 NUMPY BASICS
29
2.3.6 NUMPY BASICS
30
2.3.7 NUMPY BASICS
31
2.3.8 NUMPY BASICS
32
2.3.9 NUMPY BASICS
33
2.3.9.1 NUMPY BASICS
34
2.3.9.2 NUMPY BASICS
35
2.3.9.3 NUMPY BASICS
36
2.3.9.4 NUMPY BASICS
37
2.3.9.5 NUMPY BASICS
38
2.3.9.6 NUMPY BASICS
39
2.3.9.7 NUMPY BASICS
40
2.3.9.8 NUMPY BASICS
41
2.3.9.9 NUMPY BASICS
42
2.3.9.11 NUMPY BASICS
43
2.3.9.12 NUMPY BASICS
44
2.3.9.13 NUMPY BASICS
45
2.3.9.14 NUMPY BASICS
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2.3.9.15 NUMPY BASICS
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2.3.9.16 NUMPY BASICS
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2.3.9.17 NUMPY BASICS
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2.3.9.18 NUMPY BASICS
50
2.3.9.19 NUMPY BASICS
51
2.3.9.21 NUMPY BASICS
52
2.4 FILE SYSTEM
53
2.4.1 FILE SYSTEM WITH STATEMENT
54
2.4.2 FILE SYSTEM READ AND WRITE
55
2.5 RANDOM MODULE BASICS
56
2.5.1 RANDOM MODULE BASICS
57
2.5.2 RANDOM MODULE BASICS
58
2.5.3 RANDOM MODULE BASICS
59
2.5.4 RANDOM MODULE BASICS
60
2.5.5 RANDOM MODULE BASICS
61
2.5.6 RANDOM MODULE BASICS
62
2.6 PANDAS BASICS
63
2.6.1 PANDAS BASICS
64
2.6.2 PANDAS BASICS
65
2.6.3 PANDAS BASICS
66
2.6.4 PANDAS BASICS
67
2.6.5 PANDAS BASICS
68
2.6.6 PANDAS BASICS
69
2.6.7 PANDAS BASICS
70
2.7 MATPLOTLIB BASICS
71
2.7.1 MATPLOTLIB BASICS
72
2.7.2 MATPLOTLIB BASICS
73
2.7.3 MATPLOTLIB BASICS
74
2.7.4 MATPLOTLIB BASICS
75
2.7.5 MATPLOTLIB BASICS
76
2.7.6 MATPLOTLIB BASICS
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2.7.7 MATPLOTLIB BASICS
78
2.7.8 MATPLOTLIB BASICS
79
2.7.9. MATPLOTLIB BASICS
80
2.7.9.1 MATPLOTLIB BASICS
81
2.7.9.11 MATPLOTLIB BASICS
82
2.8 AGE CALCULATOR APP
83
2.8.1 AGE CALCULATOR APP
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