Udemy – AI Artificial Intelligence with Python

Udemy – AI Artificial Intelligence with Python
English | Tutorial | Size: 3.59 GB

Learn how to analyse and visualize data using Python libraries

What you’ll learn
Our course ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction.
Critical thinking is very important to validate models and interpret the results. Hence, our course material emphasizes on hardwiring this similar kind
You will have good knowledge about the predictive modeling in python, linear regression, logistic regression
Learn the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package
Requirements
To get started with Predictive Modelling with Python a solid foundation in statistics is much appreciated. It takes a good amount of understanding to interpret those numbers to understand whether the numbers are adding up or not. Along with the above-mentioned knowledge, one must know to code in Python. Knowing SQL also acts as a complementary skillset. Even if someone is not well equipped with the above-mentioned skill, it should not act as a hindrance as everything is possible with an honest effort and strong will.
Description
It is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics. With the help of predictive analytics, we can connect data to effective action about the current conditions and future events. Also, we can enable the business to exploit patterns and which are found in historical data to identify potential risks and opportunities before they occur.Python is used for predictive modeling because Python-based frameworks give us results faster and also help in the planning of the next steps based on the results.Our course at EDUCBA is tailor-made for people who are willing to work with a framework that delivers the best result in comparison to the rest of the competitive tools in the market.Our course ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction. Critical thinking is very important to validate models and interpret the results. Hence, our course material emphasizes on hardwiring this similar kind of thinking ability.You will have good knowledge about the predictive modeling in python, linear regression, logistic regression, the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package, etc.

Overview

Section 1: Artificial Intelligence with Python

Lecture 1 Introduction to Predictive Analysis

Lecture 2 Random Forest and Extremely Random Forest

Lecture 3 Dealing with Class Imbalance

Lecture 4 Grid Search

Lecture 5 Adaboost Regressor

Lecture 6 Predicting Traffic Using Extremely Random Forest Regressor

Lecture 7 Traffic Prediction

Lecture 8 Detecting patterns with Unsupervised Learning

Lecture 9 Clustering

Lecture 10 Clustering Meanshift

Lecture 11 Clustering Meanshift Continues

Lecture 12 Affinity Propagation Model

Lecture 13 Affinity Propagation Model Continues

Lecture 14 Clustering Quality

Lecture 15 Program of Clustering Quality

Lecture 16 Gaussian Mixture Model

Lecture 17 Program of Gaussian Mixture Model

Lecture 18 Classification in Artificial Intelligence

Lecture 19 Processing Data

Lecture 20 Logistic Regression Classifier

Lecture 21 Logistic Regression Classifier Example Using Python

Lecture 22 Naive Bayes Classifier and its Examples

Lecture 23 Confusion Matrix

Lecture 24 Example os Confusion Matrix

Lecture 25 Support Vector Machines Classifier(SVM)

Lecture 26 SVM Classifier Examples

Lecture 27 Concept of Logic Programming

Lecture 28 Matching the Mathematical Expression

Lecture 29 Parsing Family Tree and its Example

Lecture 30 Analyzing Geography Logic Programming

Lecture 31 Puzzle Solver and its Example

Lecture 32 What is Heuristic Search

Lecture 33 Local Search Technique

Lecture 34 Constraint Satisfaction Problem

Lecture 35 Region Coloring Problem

Lecture 36 Building Maze

Lecture 37 Puzzle Solver

Lecture 38 Natural Language Processing

Lecture 39 Examine Text Using NLTK

Lecture 40 Raw Text Accessing (Tokenization)

Lecture 41 NLP Pipeline and Its Example

Lecture 42 Regular Expression with NLTK

Lecture 43 Stemming

Lecture 44 Lemmatization

Lecture 45 Segmentation

Lecture 46 Segmentation Example

Lecture 47 Segmentation Example Continues

Lecture 48 Information Extraction

Lecture 49 Tag Patterns

Lecture 50 Chunking

Lecture 51 Representation of Chunks

Lecture 52 Chinking

Lecture 53 Chunking wirh Regular Expression

Lecture 54 Named Entity Recognition

Lecture 55 Trees

Lecture 56 Context Free Grammar

Lecture 57 Recursive Descent Parsing

Lecture 58 Recursive Descent Parsing Continues

Lecture 59 Shift Reduce Parsing

This Predictive Modeling with Python Course can be taken up by anyone who shares a decent amount of interest in this field. The earlier someone starts the further they can reach. In the case of students who are pursuing a course in statistics, or computer science graduates it is a very good opportunity to direct your career in that direction. As this is a much demand skill every IT professional is looking for a good switch and entering the domain of predictive analysis. After successfully having hands-on with Predictive Analysis you get open up career opportunities within job roles like that of a Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician, etc.

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