Machine Learning In 9 Days Beginner To Advance Using Python

Learn to understand NumPy, Pandas, Matplotlib, Sklearn, model deployment, forecasting, prediction through ML models

What you'll learn

Learn Machine Learning from beginner to advance level

Explain all the concepts theoretically as well as practically

First we brush up Python and then start the concept of Machine Learning

Create multiple ML projects

Explain the statistical part in easiest way

Define data analytics, arrays, data visualizations and many more…

Create multiple ML models and understand the usage

All the notes are available in recourses

Requirements

Beginner level of Python required

Helpful if you have any programming background

Explain each and every line very clearly so if you are beginner then also do not worry

Description

In this course I am going to describe what is exactly Machine Learning, I will explain different types of Machine Learning with real world examples so you understands the concepts easily. Then I start with NumPy which we use to make arrays in python and that too with different dimensions of array. we together apply multiple operations on arrays, try to access desired information/data from the array and then multiple operations on array. Also we then try to figure out how NumPy array is better than Python list in case of machine Learning.After that we start working on Pandas library of Python to start working on different datasets. we understand types of pandas data type like series, DataFrame and panels and we store our data in data frames and apply different operations on DataFrame. We will try to handle missing values, data normalization, One hot encoding and much more.Then we start doing data visualization using Matplotlib library of Python which is very interesting, and by using it we are able to gather hidden insights of datasets.Then we start working on different project of Machine Learning for future prediction and concepts of forecasting.And at the end we will try to deploy our model on a webpage so anyone can use that ML model by abstracting the code.

Overview

Section 1: Day 1 as a Machine Learning Learner

Lecture 1 What is Machine Learning, Types of ML and Supervised Machine Learning?

Lecture 2 Explaining Unsupervised Machine Learning

Lecture 3 Explaining Reinforcement machine learning and a Quiz for you

Lecture 4 How machine Learning Works?

Section 2: Day 2 as a Machine Learning Learner

Lecture 5 NumPy Part 1

Lecture 6 NumPy Part 2

Lecture 7 NumPy Part 3

Lecture 8 NumPy Part 4

Section 3: Day 3 as a Machine Learning Learner

Lecture 9 Pandas Part 1

Lecture 10 Pandas Part 2

Section 4: Day 4 as a machine Learning Learner

Lecture 11 Pandas Part 3

Lecture 12 Pandas Part 4

Section 5: Day 5 as a Machine Learning Learner

Lecture 13 Data Visualization through Matplotlib Library of Python

Section 6: Day 6 as a Machine Learning Learner

Lecture 14 Model demonstration

Lecture 15 Project 1

Lecture 16 Project 2

Lecture 17 Project 3

Section 7: Day 7 as a Machine Learning Learner

Lecture 18 train-test-split method

Lecture 19 One Hot Encoding and Label Encoding

Lecture 20 Project 5

Section 8: Day 8 as a machine Learning Learner

Lecture 21 Project 6

Lecture 22 Crucial Concept

Lecture 23 Project 7

Section 9: Day 9 as a machine Learning Learner

Lecture 24 Model Deployment

This course is for the person who want to start exploring Machine Learning field,Beginner Python developers who wants to explore data