Python Books Python Courses Udemy Courses

Complete Python Bootcamp 2020: With Practical Projects

What you’ll learn
  • Learn to build your own QR Code Scanner using Computer Vision.
  • Learn to use Google’s API for speech recognition using python.
  • Learn file handling by making an marks database project
  • Learn about functions by making an advanced calculator
  • Learn to use Object Oriented Programming with classes.
  • Learn to use Python 3 professionally
  • Learn advanced Python features, handle errors and work with modules
  • Understand how to use both the Jupyter Notebook and create .py files
  • Access to a computer with internet facility
  • A burning desire to learn.

Become a Python Programmer and learn one of employer’s most requested skills of 2020!

This is  a crisp, clear and comprehensive course for the Python programming language! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3.

With over 50 lectures and more than 2 hours of high quality video this refresher course leaves no stone unturned! This course includes a lot of interesting quizzes, and homework assignments as well as 2 major projects to create your own portfolio right away!

This course will teach you Python in a practical manner, with every lecture comes a full coding screen-cast, corresponding code notebook, interesting quizzes and homework assignment! Learn in whatever manner is best for you!

We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we’ve got you covered!

We cover a wide variety of topics, including:

  • Command Line Basics
  • Installing Python
  • Running Python Code
  • Strings
  • Lists
  • Dictionaries
  • Tuples
  • Sets
  • Number Data Types
  • Print Formatting
  • Functions
  • args/kwargs
  • Debugging and Error Handling
  • Modules
  • Object Oriented Programming
  • File I/O
  • and more lectures will be added as required to keep the course updated!

Why this course is only 2.5 hrs long? Can we learn python in this duration?

This is the question I’m frequently asked from a lot of beginners. There are a number of Python courses on Udemy which extend upto 30 hours!!! But what you need to know here is that Python is an extremely easy language and you don’t need to waste much time learning python. Python is just the first step towards a number of technologies which you may learn after this. The technology you want learn depends on your interest and this course aims to prepare you for that in a very short amount of time but in a very powerful manner. By taking up the course you will feel confident about the python language and you will be able to tackle anything you desire.

You will get lifetime access to over 50 lectures plus corresponding Notebooks for the lectures!

In case you don’t believe me…. This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you’ll get your money back. No questions asked!!

So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!

Who this course is for:
  • Beginners who are getting into programming for the first time
  • Beginners who want to start a career in Artificial Intelligence/ Data Science/ Machine Learning/ Robotics
  • Programmers who want to switch to Python
  • Everyone who wants to learn how to code and apply the knowledge in real life
  • Everyone who wants to practice real world python projects

Take Course

Django Tutorials Python Python Books

Machine Learning with Python Cookbook Practical Solutions from Preprocessing to Deep Learning

What you’ll learn

Over the last few years machine learning has become embedded in a wide variety of day-to-day business, nonprofit, and government operations. As the popularity of machine learning increased, a cottage industry of high-quality literature that taught applied machine learning to practitioners developed. This literature has been highly successful in training an entire generation of data scientists and machine learning engineers. This literature also approached the topic of machine learning from the perspective of providing a learning resource to teach an individual what machine learning is and how it works. However, while fruitful, this approach left out a different perspective on the topic: the nuts and bolts of doing machine learning day to day. That is the motivation of this book—not as a tome of machine learning knowledge for the student but as a wrench for the professional, to sit with dog-eared pages on desks ready to solve the practical day-to-day problems of a machine learning practitioner

More specifically, the book takes a task-based approach to machine learning, with almost 200 self-contained solutions (you can copy and paste the code and it’ll run) for the most common tasks a data scientist or machine learning engineer building a model will run into.

The ultimate goal is for the book to be a reference for people building real machine learning systems. For example, imagine a reader has a JSON file containing 1,000 categorical and numerical features with missing data and categorical target vectors with imbalanced classes, and wants an interpretable model. The motivation for this book is to provide recipes to help the reader learn processes such as:

  • 2.5 Loading a JSON file
  • 4.2 Standardizing a Feature
  • 5.3 Encoding Dictionaries of Features
  • 5.4 Imputing Missing Class Values
  • 9.1 Reducing Features Using Principal Components
  • 12.2 Selecting Best Models Using Randomized Search
  • 14.4 Training a Random Forest Classifier
  • 14.7 Selecting Random Features in Random Forests

The goal is for the reader to be able to:

1. Copy/paste the code and gain confidence that it actually works with the included toy datasets.

2. Read the discussion to gain an understanding of the theory behind the technique the code is executing and learn which parameters are important to consider.

3. Insert/combine/adapt the code from the recipes to construct the actual application.

Who This Book Is For

This book is not an introduction to machine learning. If you are not comfortable with the basic concepts of machine learning or have never spent time learning machine learning, do not buy this book. Instead, this book is for the machine learning practitioner who, while comfortable with the theory and concepts of machine learning, would benefit from a quick reference containing code to solve challenges he runs into working on machine learning on an everyday basis.

This book assumes the reader is comfortable with the Python programming language and package management.

  Who This Book Is Not For

As stated previously, this book is not an introduction to machine learning. This book should not be your first. If you are unfamiliar with concepts like cross-validation, random forest, and gradient descent, you will likely not benefit from this book as much as one of the many high-quality texts specifically designed to introduce you to the topic. I recommend reading one of those books and then coming back to this book to learn working, practical solutions for machine learning.

Terminology Used in This Book

Machine learning draws upon techniques from a wide range of fields, including computer science, statistics, and mathematics. For this reason, there is significant variation in the terminology used in the discussions of machine learning:

Python Python Books


So, you want to learn programming. Welcome to one of the great adventures of the twenty-first century. Programming requires little in the way of specialized equipment; the software tools can all be downloaded for free off the Internet, and it can be practiced in the safety and comfort of your own home, without having to ask anyone’s permission.

This will ease you in gently by introducing you to the software you will need to create your programs: a command-line interface, which allows you to use Python in interactive mode, and a text editor for writing scripts—nothing more complicated than that.

I will also show you where to go to find help and documentation, so you can decode the sometimes-impenetrable jargon that seems to surround this, the geekiest of all technical disciplines.

To begin with, you will need to make sure that you have a decently recent version of Python installed on your machine or follow the steps later in this chapter to install it (see “Choosing the Right Python Version” for a definition of decently recent).

This chapter explains how to make sure that you have everything set up correctly and that you have suitable references at hand before you start your journey.

Size: 5MB

PHP Scripts | Source Code Python Books Python Courses

Social Distancing Detector in Python with Source code

Free Download Advance Social Distancing Detector in Python with Deep learning Tutorial & Source code and Database. Advance Social Distancing Detector is Develop in Python with open_CV , Deep Learning that can detect if people are keeping a safe distance from each other by analyzing real time video streams from the CCTV or Safety camera.

Face Recognition system Develop Using 


How to perform Object Detection Using ImageAI

  • Install Python on your computer system
  • Install ImageAI and its dependencies
  •  Download the Object Detection model file
  •  Run the sample codes

How to Start

1-Install Python 3 in your System From Python Official website

2-Install the following dependencies via pip


pip3 install tensorflow


pip3 install opencv-python


pip3 install keras


pip3 install imageai –upgrade

3-Download the RetinaNet model file that will be used for object detection

For More Detail You Can Download Full Tutorial and Source code  

Size: 340 MB

Machine Learning, AI & Deep Learning PHP Scripts | Source Code Python Books Python Courses

Python Face Detection System with Source Code

More Over