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Keep shoppingMachine Learning: Beginner to Expert using Python (2024)
28 students
Machine Learning: Beginner to Expert using Python (2024)
Machine Learning Made Simple with Python: Develop Skills from Basic Concepts to Advanced Model Building
Created byVenkata Reddy AI Classes
Last updated 11/2024
English
What you'll learn
- Python Programming, Data Handling and Cleaning, Basic Statistics, Classical Machine Learning Algorithms, Model Selection and Validation, Advanced ML
- Write your own Python scripts and work in Python Environment.
- Import, manipulate, clean up, sanitize and export datasets.
- Understand basic statistics and implement using Python.
- Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models.
- Do powerful analysis on data, find insights and present them in visual manner.
- Learn classical algorithms like Linear Regression, Logistic Regression, Decision Trees and advance machine learning algorithms like RF and GBM
- Know how each machine learning algorithm works and which one to choose according to the type of problem.
- Build more than one powerful machine learning model and be able to select the best one and improve it further.
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Course content
13 sections • 23 lectures • 17h 17m total length
Requirements
- Familiarity with high school mathematics.
Description
This course is designed to take students from beginner to expert in machine learning using Python. It starts with essential topics like core statistics and regression techniques, including both linear and logistic regression. Students will learn about model validation to help ensure the accuracy and reliability of their models. As the course progresses, they’ll explore advanced concepts such as decision trees, artificial neural networks (ANN), random forests, and boosting methods, which are used to improve model performance.
Alongside these modeling techniques, students will gain hands-on experience in feature engineering, learning how to prepare and transform data for better model results. The course also covers natural language processing (NLP), text mining, and sentiment analysis, giving students the skills to work with text data. These techniques are crucial for understanding the emotions and insights hidden in language data.
Another key area is hypothesis testing, which helps students verify assumptions and ensure their analyses are backed by statistical evidence. The course culminates in a complete machine learning project, allowing students to put all their newly acquired skills into practice. This project simulates a real-world setting where students will gather data, prepare it, build and test models, and finally, evaluate their solutions.
By the end of the course, students will have a strong foundation in machine learning and the confidence to build their own models. They’ll be prepared to tackle a wide range of machine learning problems and apply these skills across different industries. This course is perfect for anyone looking to master machine learning from the ground up with practical, hands-on learning using Python.
Who this course is for:
- Anyone interested in Data Science and Machine Learning.
- Students who want a head start in Data Science field.
- Data analysts who want to upgrade their skills in Machine Learning.
- People who want to add value to their work and business by using Machine Learning.
- People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it.
- People interested in understanding application of machine learning algorithms on real business problems.
- People interested in understanding how a machine learning algorithm works and what's the math behind it.