# ML Level 1 | September Weekdays

Enrollment in this course is by invitation only

Learn & Master Machine Learning from basics with our Online ML Course

Online ML Course consists of two levels. This course is the Level 1 in which you will get started with ML & Python Programming working on algorithms like Linear Regression, Logistics Regression, Multi Variate Linear Regression, K Nearest Neighbour, Decision Tree & Random Forest.

## Highlights

• Designed for Working professionals, College students & Faculties
• Expert trainers capable of making learning-technology simple
• Python Programming
• Linear Regression & Classification
• K Nearest Neighbour, Decision Tree & Random Forest
• Real time projects based on every concept that you learn

## Course Curriculum

#### Week 1: Linear Regression

• Fundamentals of ML
• Types of Machine Learning
• Linear Regression Problem
• Python Programming basics
• Matplot and Pandas libraries

#### Week 2: Logistics Regression

• Classification Problem
• Logistic Regression Model
• Introduction to libraries: Scipy and Scikit
• Handwriting Digit Recognition
• Convert Images to raw data
• Raw data to CSV
• Accuracy Prediction and Optimization

#### Week 3: Multi Variate Linear Regression

• Multi variate Linear Regression
• Introduction to Scikit Learn
• Label Encoding
• Data Preprocessing
• Regularization Parameter
• Hyperparameter Grid Search
• Bias, Variance, Accuracy, Precision

#### Week 4: K Nearest Neighbour

• What is KNN?
• Example KNN Problem
• Defining the Objective function
• Optimize Objective function
• Prediction & Accuracy

#### Week 5: Decision Tree & Random Forest

• What is Decision Tree?
• Calculating Entropy
• Calculating Information gain
• Gini Index & Objective Function
• What is Random Forest & Bagging?
• Advantages of using Random Forest
• Pruning & Objective function
• Prediction & Accuracy

## Course Staff #### Benishia

Senior Trainer, Lema Labs

1. Course Number

ML1
2. Classes Start

3. Enrollment in this course is by invitation only