Machine Learning Interview Course

A self-paced machine learning course for students and professionals who want to build projects in Python and master ML algorithms for job interviews. We recommend you follow modules in a guided order.

What will you learn?

  • Machine Learning Concepts

  • Projects with Python Code

  • Math Behind Algorithms

  • ML Interview Questions

enjoyalgorithms machine learning course

Try new look of this course

ML Course Modules

(1) Python Fundamentals

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

(2) ML Fundamentals

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

(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

(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 (coming soon)

8.

Build ML model using Python

9.

Introduction to Scikit-Learn

(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

(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 using ML

(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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