Preliminary Module Task & Topics

1. Machine Learning

1.1 ML Basics

• 1.1.1 What is Machine Learning?

• 1.1.2 Basics of ML

• 1.1.3 Why is ML used?

• 1.1.4 Where ML is used?

• 1.1.5 Application level of ML

1.2 Types of ML

• 1.2.1 Supervised Learning

• 1.2.2 Unsupervised Learning

• 1.2.3 Difference between types of ML

1.3 ML Workflow

• 1.3.1 ML Workflow

• 1.3.2 Workflow structure

• 1.3.3 Data preprocessing

• 1.3.4 EDA

• 1.3.5 Difference in workflow

1.4 Comparison

• 1.4.1 Machine Learning vs Data Science

• 1.4.2 Machine Learning vs Data Analyst

---

2. Python Basics

2.1 Fundamentals

• 2.1.1 Variables

• 2.1.2 Data types

• 2.1.3 List

• 2.1.4 Tuple

2.2 Operations

• 2.2.1 Insert

• 2.2.2 Delete

2.3 Operators and Functions

• 2.3.1 Operators

• 2.3.2 Inbuilt functions

2.4 Control Flow

• 2.4.1 Control statements

• 2.4.2 Loops in Python

• 2.4.3 Difference between loops

2.5 Input and Output

• 2.5.1 User input

• 2.5.2 Output

2.6 Practice Logical Problems

• 2.6.1 Prime numbers

• 2.6.2 Unique numbers

---

3. Basic Statistics

3.1 Measures

• 3.1.1 Mean

• 3.1.2 Median

• 3.1.3 Mode

• 3.1.4 Variance

• 3.1.5 Standard deviation

• 3.1.6 Range

• 3.1.7 Quartile

3.2 Differences and Equations

• 3.2.1 Difference

• 3.2.2 Equations

3.3 Statistics in ML

• 3.3.1 Where statistics are used in ML

  • Mean

  • Mode

---

4. Basic Mathematics

4.1 Vectors

• 4.1.1 Vectors

• 4.1.2 Addition

• 4.1.3 Multiplication

• 4.1.4 Equations

• 4.1.5 Usages in ML

4.2 Matrices

• 4.2.1 Matrix

• 4.2.2 Addition

• 4.2.3 Multiplication

• 4.2.4 Equations

• 4.2.5 Usages in ML

4.3 Probability

• 4.3.1 Probability

• 4.3.2 Independent events

• 4.3.3 Dependent events