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: Python fundaments for ML

1.

Variable and data types

2.

Tuples and Lists

3.

Sets and Dictionaries

4.

Conditions and Branching

5.

Introduction to Loop

6.

Introduction to Functions

7.

Introduction to OOPS

8.

Introduction to Numpy

9.

Introduction to Pandas

Module 2: Fundamentals of Machine Learning

1.

ML vs. Traditional programming

2.

AI vs Machine learning

3.

Guide to start ML journey

4.

How exactly machine learns?

5.

Gradient Descent Algorithm

6.

Classification of ML models

7.

Supervised vs. Unsupervised

8.

Classification vs. Regression

9.

Loss and Cost Function

Module 3: Analyse and Play with Data

1.

Make system ML enabled

2.

Introduction to Matplotlib

3.

Introduction to Seaborn

4.

Structured data pre-processing

5.

Data pre-processing hands-on

6.

Exploratory data analysis

7.

Pre-processing of text data

8.

Word Vector Encoding

9.

Feature selection techniques

Module 4: ML Model Performance Improvement Tools

1.

Need of Feature scaling

2.

Regularization in ML

3.

Bias-Variance Tradeoff

4.

Evaluate regression model

5.

Evaluate classification model

6.

Guide to build ML Projects

7.

Hyperparameter Tuning

8.

Build ML model using Python

9.

Introduction to Scikit-Learn

Module 5: Basic Supervised Learning Algorithms and Projects

1.

Linear Regression

2.

Life-Expectancy Prediction

3.

Logistic Regression

4.

Optical Character Recognition

5.

Support Vector Machines

6.

Cancer Classification Model

7.

K-Nearest Neighbors (KNN)

8.

Email Spam Filtering

9.

Wine Quality Prediction

Module 6: Advanced Supervised Learning Algorithms and Projects

1.

Naive Bayes Classifier

2.

Sentiment Analysis

3.

Decision Trees

4.

Bagging and Boosting

5.

Random Forest Algorithm

6.

How Uber uses ML?

7.

PUBG Cheaters Detection

8.

Introduction to XG-Boost

9.

Drug Discovery

Module 7: Unsupervised Learning Algorithms and Projects

1.

K-Means Clustering

2.

Personality Prediction

3.

Customer Segmentation

4.

PCA for Dimension Reduction

5.

Image Compression using PCA

6.

t-SNE for Dimension Reduction

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