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As you are reading this message it means your are interested in learning python and Machine learning.

I will be posting content in a very detailed manner.( Stream file )

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👉 NO PREREQUISITES

👉 Machine Learning course has 150+hours of industry focused and extremely simplified content with no prerequisites covering Python, Maths, Data Analysis, Machine Learning and Deep Learning. 

💫 If you are familiar with python, you can skip #module - 1 - 9

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#Module 1 : Fundamental of programming

👉 1. How to utilise course

👉 2. Python for Data Science Introduction

👉 3. Python for Data Science: Functions

👉 4. Python for Data Science : Numpy

👉 5. Python for Data Science: Matploitb

👉 6. Python for Data Science: Pandas

👉 7. Python for Data Science: Computational Complexity

👉 8. SQL

Let's start our content

💫 Use #Module to travel easily throughout the channel

#Module 1 : How to utilise this course

Follow these steps to get a great result

#Module 2 : Python for Data Science Introduction
Section 2 is divided into sub sections

👉 2.1 Python, Anaconda and relevant packages installations

👉 2.2 Why learn Python?

👉 2.3 Keywords and identifiers

👉 2.4 comments, indentation and statements

👉 2.5 Variables and data types in Python

👉 2.6 Standard Input and Output

👉 2.7 Operators

👉 2.8 Control flow: if else

👉 2.9 Control flow: while loop

👉 2.10 Control flow: for loop

👉 2.11 Control flow: break and continue

#Module 3 : Python for Data Science: Data Structures
Section 3 is divided into sub sections

👉 3.1 Lists

👉 3.2 Tuples part 1

👉 3.3 Tuples part 2

👉 3.4 Sets

👉 3.5 Dictionary

👉 3.6 Strings

#Module 4 : Python for data science: Functions
Section 4 is divided into sub sections
👉 4.1 Introduction

👉 4.2 Types of functions

👉 4.3 Function arguments

👉 4.4 Recursive functions

👉 4.5 Lambda functions

👉 4.6 Modules

👉 4.7 Packages

👉 4.8 File Handling

👉 4.9 Exception Handling

👉 4.10 Debugging Python

#Module 5 : Python for Data Science: Numpy
Section 5 is divided into sub sections

👉 5.1 Numpy Introduction

👉 5.2 Numerical operations on Numpy

#Module 6 : Python for Data Science: Matplotlib

👉 6.1 Getting started with Matplotlib

#Module 7 : Python for Data Science: Pandas
Section 7 is divided into sub sections

👉 7.1 Getting started with pandas

👉 7.2 Data Frame Basics

👉 7.3 Key Operations on Data Frames

#Module 8 : Python for Data Science: Computational Complexity

Section 8 is divided into sub sections

👉 8.1 Space and Time Complexity: Find largest number in a list

👉 8.2 Binary search

👉 8.3 Find elements common in two lists

👉 8.4 Find elements common in two lists using a Hashtable/Dict

#Module 9 : SQL

Section 9 is divided into sub sections

👉 9.1 Introduction to Databases

👉 9.2 Why SQL?

👉 9.3 Execution of an SQL statement.

👉 9.4 IMDB dataset

👉 9.5 Installing MySQL

👉 9.6 Load IMDB data.

👉 9.7 USE, DESCRIBE, SHOW TABLES

👉 9.8 SELECT

👉 9.9 LIMIT, OFFSET

👉 9.10 ORDER BY

👉 9.11 DISTINCT

👉 9.12 WHERE, Comparison operators, NULL

👉 9.13 Logical Operators

👉 9.14 Aggregate Functions: COUNT, MIN, MAX, AVG, SUM

👉 9.15 GROUP BY

👉 9.16 HAVING

👉 9.17 Order of keywords.

👉 9.18 Join and Natural Join

👉 9.19 Inner, Left, Right and Outer joins.

👉 9.20 Sub Queries/Nested Queries/Inner Queries

👉 9.21 DML:INSERT

👉 9.22 DML:UPDATE , DELETE

👉 9.23 DDL:CREATE TABLE

👉 9.24 DDL:ALTER: ADD, MODIFY, DROP

👉 9.25 DDL:DROP TABLE, TRUNCATE, DELETE

👉 9.26 Data Control Language: GRANT, REVOKE

👉 9.27 Learning resources

👉 9.28 Assignment-22: SQL Assignment on IMDB data