Complete Machine Learning Package

🏅 Ranked as one of the top data science repositories on GitHub!

Techniques, tools, best practices, and everything you need to to learn machine learning!

Complete Machine Learning Package is a comprehensive repository containing 35 notebooks on Python programming, data manipulation, data analysis, data visualization, data cleaning, classical machine learning, Computer Vision and Natural Language Processing(NLP).

All notebooks were created with the readers in mind. Every notebook starts with a high-level overview of any specific algorithm/concept being covered. Wherever possible, visuals are used to make things clear.

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What's New 🔥

• May 10th, 2023: Added a comprehensive guide on MLOps. Enjoy the guide!!

• June 23th, 2022: Many people have asked how they can support the package. You can buy us a coffee ☕️

• May 18th, 2022: Complete Machine Learning Package is now available on web. It's now easy to view all notebooks!

• April 9th, 2022: Updated Transfer Learning with Pretrained Convolutional Neural Networks with additional things and added further resources.

• November 25th, 2021: Updated Fundamentals of Machine Learning: Added introductory notes, ML system design workflow, and challenges of learning systems.

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Tools Overview

The following are the tools that are covered in Complete Machine Learning Package. They are popular tools that most machine learning engineers and data scientists need in one way or another and day to day.

• Python is a high level programming language that has got a lot of popularity in the data community and with the rapid growth of the libraries and frameworks, this is a right programming language to do ML.

• NumPy is a scientific computing tool used for array or matrix operations.

• Pandas is a great and simple tool for analyzing and manipulating data from a variety of different sources.

• Matplotlib is a comprehensive data visualization tool used to create static, animated, and interactive visualizations in Python.

• Seaborn is another data visualization tool built on top of Matplotlib which is pretty simple to use.

• Scikit-Learn: Instead of building machine learning models from scratch, Scikit-Learn makes it easy to use classical models in a few lines of code. This tool is adapted by almost the whole of the ML community and industries, from the startups to the big techs.

• TensorFlow and Keras for deep learning: TensorFlow is a popular deep learning framework used for building models suitable for different fields such as Computer Vision and Natural Language Processing. Keras is a high level neural network API that makes it easy to design deep learning models. TensorFlow and Keras have a great community and ecosystem that include tools like TensorBoard, TF Datasets, TensorFlow Lite, TensorFlow Extended, TensorFlow Hub, TensorFlow.js, TensorFlow GNN, and much .

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Complete Outline

Part 1 - Intro to Python and Working with Data

0 - Intro to Python for Machine Learning

1 - Data Computation With NumPy

• Creating a NumPy Array
• Selecting Data: Indexing and Slicing An Array
• Performing Mathematical and other Basic Operations
• Performing Basic Statistics
• Manipulating Data

[You can find detailed notes about NumPy here]

2 - Data Manipulation with Pandas

• Basics of Pandas
  • Series and DataFrames
  • Data Indexing and Selection
  • Dealing with Missing data
  • Basic operations and Functions
  • Aggregation Methods
  • Groupby
  • Merging, Joining and Concatenate
• Beyond Dataframes: Working with CSV, and Excel
• Real World Exploratory Data Analysis (EDA)

3 - Data Visualization

• Data Visualization with Matplotlib
• Data Visualization with Seaborn
• Data Visualization with Pandas

4 - Real World Data Exploratory Analysis and Data Preparation

• Exploratory Data Analysis

• Intro to Data Preparation

• Handling Categorical Features

• Feature Scaling

• Handling Missing Values

Part 2 - Machine Learning

5 - Machine Learning Fundamentals

• What is Machine Learning?
• The Difference Between Artificial Intelligence, Data Science, Machine Learning, and Deep Learning
• The Difference Between Machine Learning and Ordinary Programming
• Applications of Machine Learning
• When to Use and When not to Use Machine Learning
• Types of Machine Learning
• A Typical Machine Learning Project Workflow
• Evaluation Metrics
• The Challenges of Machine Learning Systems

6 - Classical Machine Learning with Scikit-Learn

• Intro to Scikit-Learn for Machine Learning

• Linear Models for Regression

• Linear Models for Classification

• Support Vector Machines: Intro and Regression

• Support Vector Machines for Classification

• Decision Trees: Intro and Regression

• Decision Trees for Classification

• Random Forests: Intro and Regression

• Random Forests for Classification

• Beyond Random Forests: More Ensemble Models

• Intro to Unsupervised Learning with KMeans Clustering

• A Practical Intro to Principal Component Analysis

Part 3 - Deep Learning

7 - Intro to Artificial Neural Networks and TensorFlow

• Intro to Articial Neural Networks

  • Why Deep Learning

  • A Single Layer Neural Network

  • Activation Functions

  • Types of Deep Learning Architectures

    • Densely Connected Networks
    • Convolutional Neural Networks
    • Recurrent Neural Networks
    • Transformers

  • Challenges in Training Deep Neural Networks

• Intro to TensorFlow for Deep Learning

  • What is TensorFlow?
  • TensorFlow Model APIs
  • A Quick Tour into TensorFlow Ecosystem
  • Basics of Tensors

• Neural Networks for Regression with TensorFlow

• Neural Networks for Classification with TensorFlow

8 - Deep Computer Vision with TensorFlow

• Intro to Computer Vision with Convolutional Neural Networks(CNNs)

  • Intro to Computer Vision and CNNs
    • What is Convolutional Neural Networks?
    • A Typical Architecture of Convolutional Neural Networks
  • Coding ConvNets: Image Classification

• ConvNets for Real World Data and Image Augmentation

  • Intro - Real World Datasets and Data Augmentation
  • Getting Started: Real World Datasets and Overfitting
  • Image Augmentation with Keras Image Augmentation Layers

• Transfer Learning with Pretrained Convolutional Neural Networks

  • Motivation for Transfer Learning
  • Introduction to Transfer Learning
  • A Typical Flow of Transfer Learning
  • Quick Image Classification with Pretrained Models
  • Transfer Learning and FineTuning in Practice
  • Quick Image Classification and Transfer Learning with TensorFlow Hub

  [Updated notebook of Transfer Learning is found here]

9 - Natural Language Processing with TensorFlow

• Intro to NLP and Text Processing with TensorFlow

  • Intro to Natural Language Processing
  • Text Processing with TensorFlow
  • Using TextVectorization Layer

• [Using Word Embeddings to Represent Texts](https://nya