Learn Machine Learning for Interview

A text based machine learning course for students and professionals who want to design high-performance applications and master ml for job interviews. We recommend you follow each module in a guided order.

What will you learn?

  • Machine learning concepts

  • Projects with python code

  • Math behind algorithms

  • ML interview questions

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Module 1: Learning and Project Guidance

1.

Introduction to ML

2.

AI vs ML

3.

Guide to start ML journey

4.

Make system ML enabled

5.

Guide to build ML projects

6.

Build ML model using Python

Module 2: Basics of Machine Learning

1.

Process of learning in ML

2.

The idea of Gradient Descent

3.

ML models classification

4.

Supervised and Unsupervised

5.

Classification and Regression

6.

Loss and Cost functions

Module 3: Understand and Play with Data

1.

Data pre-processing part 1

2.

Data pre-processing part 2

3.

Exploratory Data Analysis

4.

Pre-processing of text data

5.

Word vector encoding

6.

Need of Feature Scaling

Module 4: Performance Improvement Tools

1.

Regularization in ML

2.

Bias-Variance Tradeoff in ML

3.

Evaluate regression model

4.

Evaluate classification model

5.

Introduction to Scikit-Learn

Module 5: Supervised and Unsupervised

1.

Linear Regression

2.

Logistic Regression

3.

K-Nearest Neighbors (KNN)

4.

Support Vector Machines

5.

Naive Bayes Classifier

6.

Decision Tree

7.

Bagging and Boosting

8.

Random Forest Algorithm

9.

Introduction to XG-Boost

10.

K-Means Clustering

11.

Principal component analysis

Module 6: Machine Learning Projects

1.

Life Expectancy Prediction

2.

Character recognition

3.

Spam and non-spam filtering

4.

Wine Quality Prediction

5.

Cancer Classification Model

6.

How Uber uses ML?

7.

PUBG Cheaters Detection

8.

Credit Card Fraud Detection

9.

Soil Fertility Prediction

10.

Sentiment Analysis

11.

Personality Prediction

12.

Customer Segmentation

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