Home Courses Applied Machine Learning Online Course Python, Anaconda and relevant packages installations

Python, Anaconda and relevant packages installations

Instructor: Applied AI Course Duration: 23 mins

Updated instructions for installing packages: https://www.youtube.com/watch?v=hbUJ6nd-9lA

CLick here to download the instructions of Installation

NOTE: tensorflow doesn't work with python 3.7, please install python version 3.6 only. the current anaconda installs with python 3.7 use this link Windown 64 bit: https://repo.continuum.io/archive/Anaconda3-5.2.0-Windows-x86_64.exe Windown 32 bit: https://repo.continuum.io/archive/Anaconda3-5.2.0-Windows-x86.exe Mac : https://repo.continuum.io/archive/Anaconda3-5.2.0-MacOSX-x86_64.sh Linux 64 bit: https://repo.continuum.io/archive/Anaconda3-5.2.0-Linux-x86_64.sh Linux 32 bit: https://repo.continuum.io/archive/Anaconda3-5.2.0-Linux-x86.sh Check the previous Archives of Anaconda: https://repo.continuum.io/archive/

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1.1
How to Learn from Appliedaicourse

35 min
1.2
How the Job Guarantee program works
16 min
3.1
Lists

38 min
3.2
Tuples part 1

10 min
3.3
Tuples part-2

4 min
3.4
Sets

16 min
3.5
Dictionary

21 min
3.6
Strings

16 min
4.1
Introduction
13 min
4.2
Types of functions
25 min
4.3
Function arguments
10 min
4.4
Recursive functions
16 min
4.5
Lambda functions
8 min
4.6
Modules
7 min
4.7
Packages
6 min
4.8
File Handling
23 min
4.9
Exception Handling
15 min
4.10
Debugging Python
15 min
5.1
Numpy Introduction
41 min
5.2
Numerical operations on Numpy
41 min
6.1
Getting started with Matplotlib
20 min
7.1
Getting started with pandas
8 min
7.2
Data Frame Basics
9 min
7.3
Key Operations on Data Frames
31 min
8.1
Space and Time Complexity: Searching for a number in a list
20 min
8.2
Binary search
17 min
8.3
Find elements common in two lists
6 min
8.4
Find elements common in two lists using a Hashtable/Dict
12 min
9.1
Introduction to Databases
22 min
9.2
Why SQL?
15 min
9.3
Execution of an SQL statement.
7 min
9.4
IMDB dataset
12 min
9.5
Installing MySQL
11 min
9.6
Load IMDB data.
4 min
9.7
USE, DESCRIBE, SHOW TABLES
15 min
9.8
SELECT
20 min
9.9
LIMIT, OFFSET
10 min
9.10
ORDER BY
6 min
9.11
DISTINCT
10 min
9.12
WHERE, Comparison operators, NULL
13 min
9.13
Logical Operators
27 min
9.14
Aggregate Functions: COUNT, MIN, MAX, AVG, SUM
8 min
9.15
GROUP BY
13 min
9.16
HAVING
12 min
9.17
Order of keywords.
4 min
9.18
Join and Natural Join
12 min
9.19
Inner, Left, Right and Outer joins.
23 min
9.20
Sub Queries/Nested Queries/Inner Queries
24 min
9.21
DML:INSERT
7 min
9.22
DML:UPDATE , DELETE
6 min
9.23
DDL:CREATE TABLE
12 min
9.24
DDL:ALTER: ADD, MODIFY, DROP
4 min
9.25
DDL:DROP TABLE, TRUNCATE, DELETE
3 min
9.26
Data Control Language: GRANT, REVOKE
10 min
9.27
Learning resources
3 min
10.1
Code Walkthrough: Basic programming & bug-fixing in Python (for AI)

10.2
Code Walkthrough: Numerical algorithms using Python (for AI)

10.3
Code Walkthrough: Numerical methods in Python (for AI) -II

10.4
Code Walkthrough: Problems in Python [ Strings and Regex ]

10.5
Code Walkthrough: Problems in Python [ Strings and Regex -II]

10.6
Code Walkthrough: Dynamic Programming & Python in-built data-structures

10.7
Code Walkthrough: OOP in Python (for AI)- I

10.8
Code Walkthrough: OOP in Python for AI -II

10.9
Code Walkthrough: Pandas in Python
10.10
Code Walkthrough: Pandas& NumPy- II
10.11
How to code effectively and build a web-scraper

10.12
Using Web-APIs in Python for Machine Learning

10.13
How to use Github?

10.14
Multi-Processing & Multithreading in Python for AI/ML

10.15
Parallel programming for training and productionization of ML/AI systems [Flask & Gunicorn]

10.16
SQL: Importance and Sample Problems

10.17
Interactive Interview Session on Python programming for ML/AI

10.18
Programming problems for AI/ML/DataScience
10.19
Smart data acquisition for ML and AI

10.20
MongoDB for Documents (NoSQL)- Part 1
10.21
MongoDB for Documents (NoSQL)- Part 2