### Day- 1

You have to start by learning some basics of a programming language. In Machine Learning we majorly use Python.

If you want to learn Machine Learning with Python. You should know the below concepts:

Variables

Mathematical Operators

Control Statements

Data Structures (List, Set, Dict, etc.)

Work with files

Functions

Object-Oriented Programming

The following playlist on youtube will help you:

Python Programming Beginner Tutorials - YouTube

Or you can watch this full video:

Python Tutorial - Python Full Course for Beginners - YouTube

Or you can do some online courses on Coursera, Udemy, etc.

### Day- 2

Let's introduce you to different libraries used in Machine Learning.

On Day 2 you can just explore the most important libraries NumPy and Pandas.

NumPy is a Python library that allows you to work with multidimensional arrays, linear algebra, statistical operations and much more. NumPy means **Num**erical **Py**thon. Numpy provides us with a variety of features that simplify the handling of Python data.

The following video will help you learn the basics of NumPy:

Python NumPy Tutorial for Beginners - YouTube

### Day- 3:

Pandas is the top library you need to know before you get your hands on machine learning programming. Pandas is built on top of NumPy and well integrates with the scientific computing environment. It also supports time series-specific functionalities.

When handling structured data, the first library you need is Pandas. You can achieve the following tasks and many more with the help of Pandas.

Import the dataset into the workspace

Identify the size and shape of the data

Fetch rows and columns

Add or remove columns

Handle missing values

Analyze NaN values

Groupby operations

Merge and join datasets

Data slicing (fetching a particular portion of the dataset), indexing

It ticks every checkbox of a data scientist’s needs – data cleaning, data analysis, and data organization. You can take away a perfect dataset from here for your data plotting and data modeling needs.

The following playlist will help you in learning the basics of Pandas:

Data analysis in Python with pandas - YouTube

### Day- 4:

Now before applying different machine learning algorithms we should know the math behind them.

You should know the following concepts:

Categorical & Numerical Data

Mean, Mode and Median

Standard Deviation and Variance

Co-Variance

Correlation

Skewness

Random Variables

Distributions

Classic Probability

Conditional Probability

Most importantly statistics. The following playlist covers the basics of statistics which will be needed (Videos 1- 10 ( Can watch all the videos of this playlist if you are interested) ):

### Day- 5:

Finally, you have to learn data visualization. Visualization takes a huge complex amount of data to represent charts or graphs for quick information to absorb and better understandability. It avoids hesitation on large data sets table to hold audience interest longer.

The following playlist will cover all the basic visualization techniques to use:

Introduction to Seaborn - YouTube

### Day- 6:

There is one basic algorithm that you have to cover and that is Linear Regression.

Linear regression is a basic and commonly used type of predictive analysis. The simplest form of the regression equation with one dependent and one independent variable is defined by the formula y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable.

Three major uses for regression analysis are:

Determining the strength of predictors

Forecasting an effect

Trend forecasting

The following playlist covers the basics of Linear Regression Full Lecture 4 (After completing this 7-day plan, I recommend you to watch this full playlist:

Machine Learning — Andrew Ng, Stanford University [FULL COURSE] - YouTube

The following Links will help you in writing code for Linear Regression:

Linear regression without scikit-learn — Scikit-learn course (inria.github.io)

Linear regression using scikit-learn — Scikit-learn course (inria.github.io)

### Day- 7:

Complete the things which you may have missed. This day is just for you to explore new things and do something extra.

In the end, I would recommend checking this website:

AI News & Robotics News - Unite.AI

By this, we come to the end of the 7 Day plan for a beginner in Machine Learning.