angles-rightTopics

1. Machine Learning

1.1 Machine Learning Basics

  1. What is Machine Learning?

  2. Basics of Machine Learning

  3. Why is Machine Learning used?

  4. Where Machine Learning is used?

  5. Application level of Machine Learning

1.2 Types of Machine Learning

  1. Supervised Learning

  2. Unsupervised Learning

  3. Difference between Supervised and Unsupervised Learning

1.3 Machine Learning Workflow

  1. ML Workflow Steps

  2. Workflow Structure

  3. Data Preprocessing

  4. Exploratory Data Analysis (EDA)

  5. Difference between Workflow Stages

  1. Machine Learning vs Data Science

  2. Machine Learning vs Data Analyst


2. Python Basics

2.1 Python Fundamentals

  1. Variables

  2. Data Types

    • int

    • float

    • string

    • boolean

2.2 Python Data Structures

  1. List

  2. Tuple

2.3 Operations in Python

  1. Insert Operation

  2. Delete Operation

2.4 Operators and Functions

  1. Operators

  2. Inbuilt Functions

2.5 Control Flow

  1. Control Statements

  2. Loops in Python

  3. Difference between Loops

2.6 Input and Output

  1. User Input

  2. Output

2.7 Practice Logical Problems

  1. Prime Number Program

  2. Unique Number Program

  3. Other Basic Logical Problems


3. Basic Statistics

3.1 Measures of Central Tendency

  1. Mean

  2. Median

  3. Mode

3.2 Measures of Dispersion

  1. Variance

  2. Standard Deviation

  3. Range

3.3 Quartiles

  1. Quartile (Q1)

  2. Quartile (Q2)

  3. Quartile (Q3)

3.4 Differences and Equations

  1. Mean vs Median vs Mode

  2. Variance vs Standard Deviation

  3. Statistical Equations

3.5 Statistics in Machine Learning

  1. Use of Mean in ML

  2. Use of Median in ML

  3. Use of Mode in ML

  4. Use of Variance in ML

  5. Use of Standard Deviation in ML


4. Basic Mathematics

4.1 Vectors

  1. Vector

  2. Vector Addition

  3. Vector Multiplication

  4. Vector Equations

  5. Usage of Vectors in Machine Learning

4.2 Matrices

  1. Matrix

  2. Matrix Addition

  3. Matrix Multiplication

  4. Matrix Equations

  5. Usage of Matrices in Machine Learning

4.3 Probability

  1. Probability

  2. Independent Events

  3. Dependent Events

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