Predictive Analysis | AI Artificial Intelligence | Python

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EDUCBA Bridging the Gap

6:21:25

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1 - Introduction to Predictive Analysis.mp4

08:51

2 - Random Forest and Extremely Random Forest.mp4

10:50

3 - Dealing with Class Imbalance.mp4

06:41

4 - Grid Search.mp4

09:20

5 - Adaboost Regressor.mp4

07:40

6 - Predicting Traffic Using Extremely Random Forest Regressor.mp4

02:04

7 - Traffic Prediction.mp4

06:41

8 - Detecting patterns with Unsupervised Learning.mp4

05:17

9 - Clustering.mp4

06:40

10 - Clustering Meanshift.mp4

03:59

11 - Clustering Meanshift Continues.mp4

06:21

12 - Affinity Propagation Model.mp4

05:27

13 - Affinity Propagation Model Continues.mp4

04:57

14 - Clustering Quality.mp4

04:57

15 - Program of Clustering Quality.mp4

07:25

16 - Gaussian Mixture Model.mp4

04:28

17 - Program of Gaussian Mixture Model.mp4

08:28

18 - Classification in Artificial Intelligence.mp4

03:13

19 - Processing Data.mp4

08:31

20 - Logistic Regression Classifier.mp4

02:52

21 - Logistic Regression Classifier Example Using Python.mp4

06:40

22 - Naive Bayes Classifier and its Examples.mp4

10:52

23 - Confusion Matrix.mp4

03:41

24 - Example os Confusion Matrix.mp4

05:37

25 - Support Vector Machines ClassifierSVM.mp4

05:08

26 - SVM Classifier Examples.mp4

07:31

27 - Concept of Logic Programming.mp4

10:49

28 - Matching the Mathematical Expression.mp4

06:35

29 - Parsing Family Tree and its Example.mp4

09:16

30 - Analyzing Geography Logic Programming.mp4

05:20

31 - Puzzle Solver and its Example.mp4

05:34

32 - What is Heuristic Search.mp4

06:13

33 - Local Search Technique.mp4

08:44

34 - Constraint Satisfaction Problem.mp4

08:42

35 - Region Coloring Problem.mp4

04:48

36 - Building Maze.mp4

07:16

37 - Puzzle Solver.mp4

08:42

38 - Natural Language Processing.mp4

06:17

39 - Examine Text Using NLTK.mp4

04:25

40 - Raw Text Accessing Tokenization.mp4

11:22

41 - NLP Pipeline and Its Example.mp4

07:00

42 - Regular Expression with NLTK.mp4

04:34

43 - Stemming.mp4

06:38

44 - Lemmatization.mp4

06:23

45 - Segmentation.mp4

05:43

46 - Segmentation Example.mp4

03:14

47 - Segmentation Example Continues.mp4

03:59

48 - Information Extraction.mp4

08:33

49 - Tag Patterns.mp4

02:53

50 - Chunking.mp4

08:49

51 - Representation of Chunks.mp4

04:52

52 - Chinking.mp4

07:14

53 - Chunking wirh Regular Expression.mp4

07:43

54 - Named Entity Recognition.mp4

05:55

55 - Trees.mp4

06:42

56 - Context Free Grammar.mp4

02:41

57 - Recursive Descent Parsing.mp4

06:25

58 - Recursive Descent Parsing Continues.mp4

06:15

59 - Shift Reduce Parsing.mp4

07:38

Description

Analyze data quickly and easily with Python library and understand well the basics of the techniques used in prediction

What You'll Learn?

Organize, filter, clean, aggregate, and analyze DataFrames
How to use Python as a programming tool to perform data analysis and exploration
Perform a multitude of data operations in Python's popular pandas library including grouping, pivoting, joining and more
Differentiate between a prediction and forecasting problem scenario and apply these concepts towards data led decision making.

Who is this for?

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.

Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician

What You Need to Know?

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.

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.

More details

Description
Predictive Analysis 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 Analysis is also called predictive modeling/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 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.
In this course, you will get an introduction to Predictive Modelling with Python. You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge on Linear Regression, Salary Prediction, Logistic Regression. You will get to work on various datasets dealing with Credit Risk and Diabetes.
Who this course is for:
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.
Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician

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EDUCBA Bridging the Gap

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