#### Benishia B Christo

Senior Trainer, Lema Labs

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#
Machine Learning Assessment

## Highlights

## Skills that you will learn

## Course Curriculum

## Who is this program for?

## Pre-requisites

## Course Staff

#### Benishia B Christo

#### Pawan

### Frequently Asked Questions

##### I am a beginner, will I be able to understand the course?

##### I already know a few basics, is the course right fit for me?

Enrollment in this course is by invitation only

This crash course is exclusively for all MS aspirants who want to gain knowledge on ML & get some projects under your belt

We will help you learn from basics of Machine Learning, understand the math behind algorithms and get hands-on experience working on ML algorithms.

- Designed for Engineering students
- Expert trainers capable of making learning-technology simple
- Work on over 15+ real-time data sets
- A seamless transition between theoretical concepts and practical hands-on
- Continuous Assessment and Mentorship

- Python programming
- Clear understanding of ML algorithms & math behind them
- Data cleaning and Pre-Processing of numerical and text data
- Predictive Analytics and statistics

Click on the headings below to view the detailed curriculum.

- What is Artificial Intelligence

- Machine Learning & Types

- Fundamentals of Python

- Data Preprocessing in python

- Defining a Model

- Error Calculation

- Gradient Descent Algorithm

**Problem:** Predicting Housing Prices based on the size of the house using Gradient Descent Algorithm

– Classification problem

– Data Preprocessing

– Defining a Model

– One vs. All

– Error & Accuracy Calculation

– Gradient Descent Algorithm

– Prediction

**Problem:** Handwritten Digit Recognition using Gradient Descent Algorithm

– Introduction to Scikit Learn

– Label Encoding

– Data Preprocessing

– Gradient Descent Algorithm, TNC

– Regularization Parameter

– Hyperparameter Grid Search

– Bias, Variance, Accuracy, Precision

**Problem:** Solve Kaggle Datasets

– What is KNN?

– Example KNN Problem

– Defining the Objective function

– Optimize Objective function

– Prediction & Accuracy

**Problem:** Online shoppers' buying intention.

– NLP Basics

– N Gram Model, Bag of Words

– TF-IDF Vectorisation

– Bayes Theorem

– Multinomial & Bernoulli Naive Bayes

– SMOTE

– Prediction & Accuracy

**Problem:** Spam Classifier

– What is Decision Tree?

– Calculating Entropy

– Calculating Information gain

– Cost Complexity Pruning

– Optimising the tree

**Problem:** Breast cancer detection.

– What is SVM?

– Intuition Behind SVM

– Defining the Objective function

– Optimize Objective function

– Kernels

– Prediction & Accuracy

– HOG SVM

– Sliding Window technique

– OpenCV

– Optimisation

**Problem:** Online shoppers' buying intention, vehicle detection, face detection & object detection.

– K Means Clustering

– Hierarchical Clustering

– K Means for non-separated clusters

– Principle Component Analysis

**Problem:** Movie Recommendation

– Insurance Claim Fraud Detection

– Gold Price Prediction

– Credit Card Fraud Detection

– Natural Scene Text Detection

– IDB - Income Qualification Prediction

– What is Neural network?

– Defining a Model

– Back Propagation Algorithm

– Classification Problem

- Engineers
- Software/IT/Data Professionals
- Engineering students/Professors
- Predictive Analytics and statistics

- Engineers
- Software/IT/Data Professionals
- Engineering students/Professors
- Predictive Analytics and statistics

Senior Trainer, Lema Labs

Team Lead, Lema Labs

Yes! The course is designed keeping you (Beginners) in mind. Frequent quizzes, Interactive videos and other activites are planned to ensure you are able to easily learn all the fundamentals

Yes! The course is designed keeping you too in mind! You can skip through the portions that you are already comfortable with and directly attempt Knowdge checks to unlock the next lessons.