Introduction to Python for Econometrics, Statistics and Data Analysis 3rd Edition PDF 2024.

  Introduction to Python for Econometrics, Statistics and Data Analysis 3rd Edition PDF 2024.

Definition of Machine Learning:

Machine Learning is a field of study of AI that aims to give machines the ability to learn. This very powerful technology has enabled the development of autonomous cars, voice recognition, and all the so-called «intelligent» systems for the last 10 years. 

Machine Learning was invented by Arthur Samuel in 1959, after he developed the first Ladies game program with artificial intelligence. 

This program had learned to play the Ladies all alone, without receiving any instruction from its developer. Machine Learning is the new electricity, it will revolutionize everything. 

In 2019, Machine Learning is already present all around us. In fact, you probably use it hundreds of times a day without even realizing it. Every time you search Google, it’s a Machine Learning model that has learned how to rank the most relevant results on the front page among millions of possible web pages. 

When you post a photo of yourself on Facebook, there is a Machine Learning algorithm that can identify you because it has learned to recognize faces on photos. The Learning machine has already begun to change the face of our world. It revolutionizes the transport industry with the autonomous car and makes our connected objects work: 

iphone, voice recognition and computer vision. It diagnoses cancer better than a team of multiple doctors and improves banking security, IT security, makes fairer court decisions… Even the agricultural industry and the art world are affected by machine learning. 

 If you have read all this article, it means that you have an important interest in Machine Learning and I congratulate you! 

Do not hesitate to contact me for any questions, and if you want to train at Machine Learning, then I advise you to download my book and follow the various free trainings that I have created (and that have already convinced thousands of people around the world)

Python Programming:

What is programming, introduction to the jupyter notebook, collections, keywords and variables, control flow instructions and python functions. what are you going to learn – Python is a very simple language to learn and is the best language for data science and machine learning because of extremely powerful libraries. learn the basics of python programming and libraries is (pandas, numpy, matplotlib). 1. Data collection and clean-up Reading data from a local file, CSV and Excel file, reading a JSON file, reading data from an API, detecting missing data, processing missing data. what are you going to learn The most important task of the Data Scientist is to collect data from different sources and to clean and prepare this data for analysis. You will learn how to collect data from local files, CSV and Excel, JSON file and API. But when you collect it, it will definitely be in a messy format. You will also learn how to clean this data in a simple way to spend less time cleaning the data and more time exploring and modeling the data.

2. Machine Learning:

What is machine learning, supervised or non superior learning and all the common machine learning techniques or algorithms in Python. what are you going to learn Machine learning is everywhere, all the tech giants like Google, Facebook and Amazon are using the machine learning model to provide users with a personalized experience. In this section, you will learn different machine learning models from which you can train your data and learn from it. In this course, you will learn the complete timeline of the data science project step by step. And with a simple explanation language and the use of real examples for better explanations will help you understand important concepts in a simple and understandable way. Hand-picked curriculum, specially designed for all levels of learners. Continuous evaluation through stimulating quizzes. Regular updates of the program. Different aspects of data science explored. Step by step implementation with explanations included. Understand how to solve data science problems in real life.

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