enjoyalgorithms

Project Based ML Course

A free self-paced machine learning course for students and professionals who want to build machine learning projects in Python, master ML algorithms, and prepare for tech interviews.

What will you learn?

✓ Python and Math for ML

✓ Machine Learning Concepts

✓ ML Projects with Python Code

✓ Guidance for ML Job Interview

enjoyalgorithms machine learning course
☆ 16-week live DSA course
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☆ 16-week live ML course
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☆ 10-week live DSA course
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ML Course Modules

Mentor and Author: Ravish Kumar

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I. Python Fundamentals 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 Function
7
Introduction to OOPS
8
Introduction to Numpy
9
Introduction to Pandas

II. Machine Learning Fundamentals

1
ML vs. Traditional Programming
2
AI vs Machine Learning
3
Guide to Start Machine Learning
4
How Exactly Machine Learns?
5
Gradient Descent Algorithm
6
Classification of ML models
7
Supervised and Un-supervised
8
Classification and Regression
9
Loss and Cost Function

III. Analyse and Play with Data

1
Make System ML enabled
2
Introduction to Matplotlib
3
Introduction to Seaborn
4
Data Pre-processing in ML
5
Data Pre-processing hands-on
6
Univariate, Bivariate, Multivariate
7
Pre-processing of Text Data
8
Word Vector Encoding
9
Feature Selection Techniques

IV. Performance Improvement Tools

1
Need of Feature Scaling
2
Regularization in ML
3
Bias-Variance Tradeoff
4
Evaluation of Regression Model
5
Evaluation of Classification Model
6
Guide to Build ML Projects
7
Build ML Model from Scratch
8
Introduction to Scikit-Learn

V. Supervised Learning Part 1

1
Linear Regression
2
Life Expectancy Prediction
3
Logistic Regression
4
Optical Character Recognition
5
Support Vector Machines
6
Cancer Classification
7
K-Nearest Neighbors (KNN)
8
Email Spam Filtering
9
Wine Quality Prediction

VI. Supervised Learning Part 2

1
Naive Bayes Classifier
2
Sentiment Analysis
3
Decision Tree Algorithm
4
Boosting and its Variants
5
Random Forest Algorithm
6
How Uber uses ML?
7
PUBG Cheaters Detection
8
Introduction to XG-Boost
9
Drug Discovery

VII. Unsupervised Learning

1
K-Means Clustering
2
Personality Prediction
3
Customer Segmentation
4
Principal Component Analysis
5
Image Compression using PCA
6
t-SNE Algorithm

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