# Machine Learning & Data Science in Python For Beginners

## Machine Learning & Data Science in Python For Beginners

Learn · Master **Machine Learning **using **Python **· Demystify Artificial Intelligence, **Machine Learning**, **Data Science** · ML Business Solution Blueprint.

### Go Link On Udemy

#### What you'll learn

- What is Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-Supervised Machine Learning
- Types of Supervised Learning: Classification
- Regression
- Types of Unsupervised Learning: Clustering
- Association
- Data Collection
- Data Preparing
- Selection of a Model
- Data Training and Evaluation
- HPT in Machine Learning
- Prediction in ML
- DPP in ML
- Need of DPP
- Steps in DPP
- Python Libraries
- Missing, Encoding, and Splitting Data in ML
- Python, Java, R,and C ++
- How to install python and anaconda?
- Interface of Jupyter Notebook
- Mathematics in Python
- Euler's Number and Variables
- Degree into Radians and Radians into Degrees in Python
- Printing Functions in Python
- Feature Scaling for ML
- How to Select Features for ML
- Filter Method
- LDA in ML
- Chi Square Method
- Forward Selection
- Training and Testing Data Set for ML
- Selection of Final Model
- ML Applications
- Practical Skills in ML: Mastery
- Process of ML
- What is Extension in ML
- ML Tradeoff
- ML Variance Error
- Logistic Regression
- Data Visualization
- Pandas and Seaborn-Library for ML

### Description

Get instant access to a 69-page Machine Learning workbook containing all the reference material

Over 9 hours of clear and concise step-by-step instructions, practical lessons, and engagement

Introduce yourself to our community of students in this course and tell us your goals

Encouragement & celebration of your progress: 25%, 50%, 75%, and then 100% when you get your certificate

#### What will you get from doing this course?

This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyse raw real-time data, identify trends, and make predictions. You will explore key techniques and tools to build Machine Learning solutions for businesses.

You don’t need to have any technical knowledge to learn these skills.

#### What will you learn:

- What is Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-Supervised Machine Learning
- Types of Supervised Learning: Classification
- Regression
- Types of Unsupervised Learning: Clustering
- Association
- Data Collection
- Data Preparing
- Selection of a Model
- Data Training and Evaluation
- HPT in Machine Learning
- Prediction in ML
- DPP in ML
- Need of DPP
- Steps in DPP
- Python Libraries
- Missing, Encoding, and Splitting Data in ML
- Python, Java, R,and C ++
- How to install python and anaconda?
- Interface of Jupyter Notebook
- Mathematics in Python
- Euler's Number and Variables
- Degree into Radians and Radians into Degrees in Python
- Printing Functions in Python
- Feature Scaling for ML
- How to Select Features for ML
- Filter Method
- LDA in ML
- Chi-Square Method
- Forward Selection
- Training and Testing Data Set for ML
- Selection of Final Model
- ML Applications
- Practical Skills in ML: Mastery
- Process of ML
- What is Extension in ML
- ML Tradeoff
- ML Variance Error
- Logistic Regression
- Data Visualization
- Pandas and Seaborn-Library for ML
- ...and more!

#### Contents and Overview

You'll start with the What is Machine Learning; Supervised Machine Learning; Unsupervised Machine Learning; Semi-Supervised Machine Learning; Example of Supervised Machine Learning; Example of Un-Supervised Machine Learning; Example of Semi-Supervised Machine Learning; Types of Supervised Learning: Classification; Regression; Types of Unsupervised Learning: Clustering; Association.

Then you will learn about Data Collection; Data Preparation; Selection of a Model; Data Training and Evaluation; HPT in Machine Learning; Prediction in ML; DPP in ML; Need of DPP; Steps in DPP; Python Libraries; Missing, Encoding, and Splitting Data in ML.

We will also cover Feature Scaling for ML; How to Select Features for ML; Filter Method; LDA in ML; Chi Square Method; Forward Selection; Training and Testing Data Set for ML; Selection of Final Model; ML Applications; Practical Skills in ML: Mastery; Process of ML; What is Extension in ML; ML Tradeoff; ML Variance Error; What is Regression; Logistic Regression.

This course will also tackle Python, Java, R,and C ++; How to install python and anaconda?; Interface of Jupyter Notebook; Mathematics in Python; Euler's Number and Variables; Degree into Radians and Radians into Degrees in Python; Printing Functions in Python.

This course will also discuss Random Selection; Random Array in Python; Random Array and Scattering; Scattering Plot; Jupyter Notebook Setup and Problem; Random Array in Python; Printing Several Function in Python; Exponential and Logarithmic Function in Python.

Next, you will learn about Simple Line Graph with Matplotlib; Color Scheme with Matplotlib; Dot and Dashed Graph; Scattering 1-Data visualization; Labelling-Data Visualization; Color Processing-Data Visualization; Seaborn Scatter Plot; Import DataFrame by Pandas.