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# Machine Learning with Python

### Complete Machine Learning course covering Linear Regression, Logistic Regression, KNN, Decision Trees, SVM and XG Boost

~~₹899~~ ₹675

Price

### Machine Learning with Python

# What you'll learn

#### Introduction to Python

Build a foundation for the most in-demand programming language. Learn how to use Python for Machine Learning

#### Application Based

Become job-ready with this application-based course. Apply what you learn and build real-life projects

#### Data Pre-Processing

Step by Step guide for data preparation covering outlier treatment, missing value imputation, variable transformation & correlation

#### Basic Machine Learning

Start you Machine Learning career with basic Linear Regression, Logistic Regression, LDA and KNN models

#### Advanced Machine Learning

Learn Advanced Machine Learning models such as Decision trees, Bagging, Boosting, XGBoost, Random Forest, SVM etc.

# Our Happy Students!

Amazingly wonderful course , extremely intuitive and something that even online courses of MIT and brand colleges wont teach in depth.

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**5/5

#### Madhav Arora

This course is good for beginners of machine learners. They can learn how and when to use linear or logistic regression. The course is well-designed and suited well for data science learners.

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**5/5

#### Syed Habeeb Ullah Quadri

It gives more detailing for analysing the data . The theory explanation of the topics are too good. I would highly recommend this course for beginners.

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**4.5/5

#### Divyashree S

# Course Instructors

# Course Completion Certificate

Once you successfully complete the course, Start-Tech Academy will provide you with an industry-recognized course completion certificate

#### Still confused? Download the complete course syllabus

# Course Content

** Course Content**

**Setting up Python and Jupyter Notebook**

**Basics of Statistics**

**Introduction to Machine Learning**

**Data Preprocessing**

**Linear Regression**

**Classification Models: Data Preparation**

**The Three classification models**

**Logistic Regression**

**Linear Discriminant Analysis (LDA)**

**K-Nearest Neighbors classifier**

**Comparing results from 3 models**

**Simple Decision Trees**

**Simple Classification Tree**

**Ensemble technique 1 – Bagging**

**Ensemble technique 2 – Random Forests**

**Ensemble technique 3 – Boosting**

**Maximum Margin Classifier**

**Support Vector Classifier**

**Support Vector Machines**

**Creating Support Vector Machine Model in Python**

**Introduction**