What you'll get
- 8+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Strong command of Python for data preparation and analytical processing.
- Clear grasp of core statistics, covering sampling, probability, and data distribution patterns.
- Ability to run matrix calculations used in machine learning model creation.
- Understanding of hypothesis checks and regression for prediction-focused modeling.
- Familiarity with key descriptive indicators like averages and variability measures.
- Knowledge of methods that support decision-making using data samples.
- Experience with T-tests, correlation, ANOVA, regression, and clustering techniques.
- Insight into the mathematical principles within complex statistical algorithms.
- Ability to deploy statistical logic through structured code.
- Skill in interpreting statistical outcomes and reducing common analysis errors.
- Hands-on exposure to Python and MATLAB/Octave programming environments.
- Understanding of ML areas, including data cleaning, clustering, classification, and predictive analysis.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning - Statistics Essentials | 8h 23m | ✔ | View Curriculum |
Description
This comprehensive course provides a strong foundation in mathematical and statistical concepts essential for machine learning, with a focus on practical implementation using Python. Participants begin with an introduction to machine learning and progress through Python programming for data manipulation, visualization, and analysis. The curriculum covers key statistical concepts, including sampling, data types, probability, random variables and distributions, as well as matrix algebra, hypothesis testing, and regression techniques.
Introduction to Machine Learning with Python
Participants are introduced to machine learning and its applications, and learn to use Python for data preprocessing, model building, and evaluation.
Importing
Learners gain practical knowledge of importing and using Python libraries crucial to machine learning, including NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
Basics of Statistics Sampling
This section explains fundamental sampling concepts, including random sampling, stratified sampling, and sampling distributions.
Basics of Statistics, Data Types, and Visualization
Participants learn about various data types and explore visualization techniques using Python libraries like Matplotlib and Seaborn.
Basics of Statistics Probability
This module covers probability fundamentals, including probability distributions, conditional probability, and Bayes' theorem.
Basics of Statistics Random Variables
Learners study random variables, probability mass functions, probability density functions, and cumulative distribution functions.
Basics of Statistics Distributions
This section introduces common probability distributions, including the normal, binomial, and Poisson distributions.
Matrix Algebra
Participants explore matrix algebra operations, including multiplication, inversion, and eigenvalues, with applications in machine learning.
Hypothesis Testing
Learners gain insight into hypothesis testing, covering null and alternative hypotheses, p-values, and significance levels.
Hypothesis Tests-Types
This module explains different types of hypothesis tests, including t-tests, chi-square tests, and ANOVA.
Regression
Participants learn regression analysis techniques, including linear, polynomial, and logistic regression, for predictive modeling.
Throughout the course, participants engage in hands-on exercises and projects to reinforce learning, applying statistical and mathematical methods to real-world machine learning problems using Python. By the end of the program, learners will be able to confidently perform data analysis, visualization, and statistical modeling in Python for machine learning applications.
Requirements
- Strong work ethic and eagerness to learn.
- No prior statistics or machine learning experience needed.
Target Audience
- Learners enrolled in statistics or machine learning programs.
- Professionals seeking to build skills in statistics and machine learning.
- Scientists are aiming to gain deeper insight into data analysis.
- Individuals interested in understanding the inner workings of machine learning.
- Students specializing in artificial intelligence (AI).
- Students pursuing studies in business intelligence.
