Data Science Full Course Notes - Learn Data Science in 1 Notebook
table of contents
1. Basic Terminologies and Importance in Statistics
2. Different Sampling Techniques
3. Measures of Central Tendency: Mean, Median, Mode
4. Measures of Variability: Range, Interquartile Range, Variance, Covariance,
Standard Deviation
5. Information Gain and Entropy
6. Statistics and Probability: Interconnected Fields
7. Probability: Measure of Event Likelihood
8. Random Experiment, Sample Space, and Event
9. Probability Distributions: PDF, Normal, Central Limit Theorem
10. Types of Probability: Marginal, Joint, Conditional
11. Bayes Theorem: Relation between Conditional Probabilities and Inverse
12. Importance of Machine Learning
13. Machine Learning Definitions
14. Machine Learning Process
15. Types of Machine Learning
16. Problem Solving with Machine Learning
17. Machine Learning: A Subset of AI Learning from Experience
18. Algorithm: Set of Rules for Learning Patterns
19. Model: Machine Learning Process Representation
20. Predictor Variable: Feature to Predict the Outcome
21. Response Variable: Output Feature
22. Introduction to Regression Analysis and Types of Regression
23. Logistic Regression: Definition, Purpose & Examples
24. Comparing Linear Regression and Logistic Regression
25. Implementing Logistic Regression using Python and scikit-learn
26. Introduction to Logistic Regression: A Straight Line to Binary Output
27. Classification Problems: AnAnswer to Discrete Outcomes
28. Titanic Data Analysis: Predicting Passenger Survival
29. Titanic - Passenger Survival Analysis
, 30. Gender & Survival Rate
31. Passenger Class & Survival Rate
32. Titanic Data Analysis: Predictive Modeling for Survival
33. SUV Data Analysis: Logistic Regression and Prediction
34. Decision Tree: Classification Algorithm Overview
1. Basic Terminologies and Importance in Statistics
Introduction
Statistics and probability are interconnected fields.
Statistics is a subset of AI, which learns from experience.
Machine learning is the application of statistical techniques to allow machines to
improve with experience.
Terminologies
Algorithm: A set of rules for learning patterns.
Model: A representation of the machine learning process.
Predictor Variable: A feature used to predict the outcome.
Response Variable: The output feature.
Importance of Machine Learning
Problem-solving with machine learning involves the following steps: identifying
the problem, selecting a model, training the model, and testing the model.
Implementing logistic regression using Python and scikit-learn is a common
application.
Probability and Statistics
Probability is the measure of event likelihood.
,Measures of central tendency include mean, median, and mode.
Measures of variability include range, interquartile range, variance, covariance,
and standard deviation.
Random experiment, sample space, and event are important concepts in
probability.
Probability Distributions
Probability distributions include PDF, normal, and central limit theorem.
Types of probability include marginal, joint, and conditional.
Bayes theorem is the relation between conditional probabilities and inverse.
Types of Machine Learning
Machine learning is divided into three types: supervised, unsupervised, and
reinforcement learning.
Regression Analysis
Regression analysis is a set of statistical processes for estimating the relationships
between a dependent variable and one or more independent variables.
Types of regression include linear, polynomial, and logistic regression.
Logistic Regression
Logistic regression aims to estimate the probability of an event by fitting data to
a logit function.
Logistic regression is used for classification problems, which involve predicting
discrete outcomes.
Titanic Data Analysis
, The Titanic dataset is an example of a machine learning problem where the goal
is to predict passenger survival.
The analysis can involve predicting survival based on factors such as gender,
passenger class, and age.
The machine learning process involved in this analysis includes problem
formulation, data preparation, model selection, evaluation, and deployment.
2. Different Sampling Techniques
Introduction
Sampling is a crucial aspect of data analysis and machine learning.
It involves selecting a subset of data from a larger population to represent the
whole.
Types of Sampling Techniques
Probability Sampling: Every member of the population has a known, non-zero
chance of being selected.
Simple Random Sampling: Each member of the population is selected randomly
and independently, with equal probability.
Stratified Random Sampling: The population is divided into strata based on
certain criteria, then a simple random sample is taken from each stratum.
Cluster Sampling: The population is divided into clusters or groups, then a
random sample of clusters is selected. All members of the chosen clusters are
included.
Non-Probability Sampling: Not all members of the population have an equal
chance of being selected.
Convenience Sampling: Members are chosen based on their convenient accessibility
and proximity to the researcher.
Quota Sampling: Pre-determined quotas are set for different subgroups of the
population, and members are chosen to fill those quotas.
Importance of Sampling
table of contents
1. Basic Terminologies and Importance in Statistics
2. Different Sampling Techniques
3. Measures of Central Tendency: Mean, Median, Mode
4. Measures of Variability: Range, Interquartile Range, Variance, Covariance,
Standard Deviation
5. Information Gain and Entropy
6. Statistics and Probability: Interconnected Fields
7. Probability: Measure of Event Likelihood
8. Random Experiment, Sample Space, and Event
9. Probability Distributions: PDF, Normal, Central Limit Theorem
10. Types of Probability: Marginal, Joint, Conditional
11. Bayes Theorem: Relation between Conditional Probabilities and Inverse
12. Importance of Machine Learning
13. Machine Learning Definitions
14. Machine Learning Process
15. Types of Machine Learning
16. Problem Solving with Machine Learning
17. Machine Learning: A Subset of AI Learning from Experience
18. Algorithm: Set of Rules for Learning Patterns
19. Model: Machine Learning Process Representation
20. Predictor Variable: Feature to Predict the Outcome
21. Response Variable: Output Feature
22. Introduction to Regression Analysis and Types of Regression
23. Logistic Regression: Definition, Purpose & Examples
24. Comparing Linear Regression and Logistic Regression
25. Implementing Logistic Regression using Python and scikit-learn
26. Introduction to Logistic Regression: A Straight Line to Binary Output
27. Classification Problems: AnAnswer to Discrete Outcomes
28. Titanic Data Analysis: Predicting Passenger Survival
29. Titanic - Passenger Survival Analysis
, 30. Gender & Survival Rate
31. Passenger Class & Survival Rate
32. Titanic Data Analysis: Predictive Modeling for Survival
33. SUV Data Analysis: Logistic Regression and Prediction
34. Decision Tree: Classification Algorithm Overview
1. Basic Terminologies and Importance in Statistics
Introduction
Statistics and probability are interconnected fields.
Statistics is a subset of AI, which learns from experience.
Machine learning is the application of statistical techniques to allow machines to
improve with experience.
Terminologies
Algorithm: A set of rules for learning patterns.
Model: A representation of the machine learning process.
Predictor Variable: A feature used to predict the outcome.
Response Variable: The output feature.
Importance of Machine Learning
Problem-solving with machine learning involves the following steps: identifying
the problem, selecting a model, training the model, and testing the model.
Implementing logistic regression using Python and scikit-learn is a common
application.
Probability and Statistics
Probability is the measure of event likelihood.
,Measures of central tendency include mean, median, and mode.
Measures of variability include range, interquartile range, variance, covariance,
and standard deviation.
Random experiment, sample space, and event are important concepts in
probability.
Probability Distributions
Probability distributions include PDF, normal, and central limit theorem.
Types of probability include marginal, joint, and conditional.
Bayes theorem is the relation between conditional probabilities and inverse.
Types of Machine Learning
Machine learning is divided into three types: supervised, unsupervised, and
reinforcement learning.
Regression Analysis
Regression analysis is a set of statistical processes for estimating the relationships
between a dependent variable and one or more independent variables.
Types of regression include linear, polynomial, and logistic regression.
Logistic Regression
Logistic regression aims to estimate the probability of an event by fitting data to
a logit function.
Logistic regression is used for classification problems, which involve predicting
discrete outcomes.
Titanic Data Analysis
, The Titanic dataset is an example of a machine learning problem where the goal
is to predict passenger survival.
The analysis can involve predicting survival based on factors such as gender,
passenger class, and age.
The machine learning process involved in this analysis includes problem
formulation, data preparation, model selection, evaluation, and deployment.
2. Different Sampling Techniques
Introduction
Sampling is a crucial aspect of data analysis and machine learning.
It involves selecting a subset of data from a larger population to represent the
whole.
Types of Sampling Techniques
Probability Sampling: Every member of the population has a known, non-zero
chance of being selected.
Simple Random Sampling: Each member of the population is selected randomly
and independently, with equal probability.
Stratified Random Sampling: The population is divided into strata based on
certain criteria, then a simple random sample is taken from each stratum.
Cluster Sampling: The population is divided into clusters or groups, then a
random sample of clusters is selected. All members of the chosen clusters are
included.
Non-Probability Sampling: Not all members of the population have an equal
chance of being selected.
Convenience Sampling: Members are chosen based on their convenient accessibility
and proximity to the researcher.
Quota Sampling: Pre-determined quotas are set for different subgroups of the
population, and members are chosen to fill those quotas.
Importance of Sampling
