Ai Artificial Intelligence With Python

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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