Machine Learning Tutorial

Machine Learning tutorial covers basic and advanced concepts, specially designed to cater to both students and experienced working professionals.

Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn or improve performance, based on the data. Artificial intelligence is a broad word that refers to systems or machines that resemble human intelligence. A crucial distinction is that, while all machine learning is AI, not all AI is machine learning. We mainly use machine learning to achieve AI.

Features of Machine Learning

• Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.
• It is similar to data mining, as both deal with substantial amounts of data.
• For large organizations, branding is crucial, and targeting a relatable customer base becomes easier.
• Given a dataset, ML can detect various patterns in the data.
• Machines can learn from past data and automatically improve their performance.
• Machine learning is a data-driven technology. A large amount of data is generated by organizations daily, enabling them to identify notable relationships and make better decisions.

Introduction

We start by exploring the foundational concepts of machine learning, including its history, key terminology, and the types of problems it can solve.

1. Introduction to Machine Learning
2. ML – Applications
3. Machine learning VS Artificial Intelligence

Data Processing

Data is the foundation of machine learning. The quality and quantity of data you have directly impact the performance of your machine learning models. In this section, we will explore various aspects of data and its processing, which are crucial for building robust ML systems.

1. Introduction to Data in ML
2. Understanding Data Processing
3. Python | Create Test DataSets using Sklearn
4. Python | Generate test datasets for Machine learning
5. Python | Data Preprocessing in Python
6. Data Cleaning
7. Feature Scaling
8. Label Encoding of datasets
9. One Hot Encoding of datasets

Supervised learning

Supervised learning is a fundamental approach in machine learning where models are trained on labeled datasets. This technique is used to predict outcomes based on input features, making it invaluable for various applications, from spam detection to medical diagnosis. In this section, we will cover key concepts and methodologies related to supervised learning, including classification and regression techniques.

1. What is Supervised Learning
2. Getting started with Classification
3. Regression Techniques - Introduction & Types
4. Classification vs Regression
5. Multiclass classification using scikit-learn
6. Gradient Descent :
   • Gradient Descent algorithm and its variants
   • Stochastic Gradient Descent (SGD)
   • Mini-Batch Gradient Descent
   • Optimization techniques for Gradient Descent
   • Introduction to Momentum-based Gradient Optimizer
7. Linear Regression :
   • Introduction to Linear Regression
   • Gradient Descent in Linear Regression
   • Mathematical explanation for Linear Regression working
   • Normal Equation in Linear Regression
   • Linear Regression (Python Implementation)
   • Linear Regression using sklearn
   • Boston Housing Kaggle Challenge with Linear Regression
   • Univariate Linear Regression in Python
   • Multiple Linear Regression using Python
   • Locally weighted Linear Regression
   • Generalized Linear Models
8. Python | Implementation of Polynomial Regression
9. Softmax Regression using TensorFlow
10. Logistic Regression :
    • Understanding Logistic Regression
    • Logistic Regression using Python
    • Cost function in Logistic Regression
11. Naive Bayes Classifiers
12. Support Vector:
    • Support Vector Machines(SVMs) in Python
    • SVM Hyperparameter Tuning using GridSearchCV
    • Using SVM to perform classification on a non-linear dataset
13. Decision Tree:
    • Decision Tree
    • Decision Tree Regression using sklearn
    • Decision Tree Introduction with example
    • Decision tree implementation using Python
14. Random Forest:
    • Random Forest Regression in Python
    • Ensemble Classifier
    • Voting Classifier using Sklearn
      
    • Bagging classifier

Unsupervised learning

Unsupervised learning is a key area of machine learning that focuses on discovering hidden patterns and structures in data without labeled outputs. Unlike supervised learning, where models learn from labeled examples, unsupervised learning relies on the inherent structure of the input data. This section will delve into various techniques and applications of unsupervised learning, primarily focusing on clustering methods.

1. What is Unsupervised Learning
2. Supervised and Unsupervised learning
3. Clustering in Machine Learning
4. Different Types of Clustering Algorithm
5. K means Clustering – Introduction
6. Elbow Method for optimal value of k in KMeans
7. Random Initialization Trap in K-Means
8. K-means++ Algorithm
9. Analysis of test data using K-Means Clustering in Python
10. Mini Batch K-means clustering algorithm
11. Mean-Shift Clustering
12. DBSCAN – Density based clustering
13. Implementing DBSCAN algorithm using Sklearn
14. Fuzzy Clustering
15. Spectral Clustering
16. OPTICS Clustering
17. OPTICS Clustering Implementing using Sklearn
18. Hierarchical clustering (Agglomerative and Divisive clustering)
19. Implementing Agglomerative Clustering using Sklearn
20. Gaussian Mixture Model

Reinforcement Learning

Reinforcement Learning (RL) is a dynamic area of machine learning focused on how agents ought to take actions in an environment to maximize cumulative reward. Unlike supervised learning, where the model learns from a fixed dataset, RL involves learning through trial and error, making it particularly suited for complex decision-making problems. This section will explore the foundational concepts, algorithms, and applications of reinforcement learning.

1. Reinforcement learning
2. Reinforcement Learning: Python Implementation using Q-learning
3. Introduction to Thompson Sampling
4. Genetic Algorithm for Reinforcement Learning
5. SARSA Reinforcement Learning
6. Q-Learning in Python

Dimensionality Reduction

Dimensionality Reduction is a crucial technique in machine learning and data analysis that focuses on reducing the number of features or dimensions in a dataset while preserving essential information. As datasets grow in complexity, high dimensionality can lead to issues such as overfitting, increased computation time, and difficulties in visualization. This section will explore various methods and applications of dimensionality reduction.

1. Introduction to Dimensionality Reduction
2. Introduction to Kernel PCA
3. Principal Component Analysis(PCA)
4. Principal Component Analysis with Python
5. Low-Rank Approximations
6. Overview of Linear Discriminant Analysis (LDA)
7. Generalized Discriminant Analysis (GDA)
8. Independent Component Analysis
9. Feature Mapping
10. Extra Tree Classifier for Feature Selection
11. Chi-Square Test for Feature Selection – Mathematical Explanation
12. T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm
13. How and where to apply Feature Scaling?
14. Parameters for Feature Selection
15. Underfitting and Overfitting in Machine Learning

Natural Language Processing

Natural Language Processing (NLP) is a vital subfield of artificial intelligence and machine learning that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This section will explore the fundamental concepts, techniques, and applications of NLP.

1. Introduction to Natural Language Processing
2. Text Preprocessing in Python | Set – 1
3. Text Preprocessing in Python | Set 2
4. Removing stop words with NLTK in Python
5. Tokenize text using NLTK in python
6. How tokenizing text, sentence, words works
7. Introduction to Stemming
8. Stemming words with NLTK
9. Lemmatization with NLTK
10. Lemmatization with TextBlob
11. How to get synonyms/antonyms from NLTK WordNet in Python?

Neural Networks

Neural networks are a fundamental component of deep learning and a powerful tool for solving complex problems in machine learning. Inspired by the human brain, neural networks consist of interconnected layers of nodes (neurons) that work together to process data, learn patterns, and make predictions. This section will cover the essential concepts, architectures, and applications of neural networks.

1. Introduction to Artificial Neutral Networks | Set 1
2. Introduction to Artificial Neural Network | Set 2
3. Hybrid Systems
4. Network Architectures
5. Activation functions
6. Implementing ANN training process in Python
7. A single neuron neural network in Python
8. Convolutional Neural Networks
   • Introduction to Convolution Neural Network
   • Introduction to Pooling Layer
   • Introduction to Padding
   • Types of padding in convolution layer
   • Applying Convolutional Neural Network on mnist dataset
9. Recurrent Neural Networks
   • Introduction to Recurrent Neural Network
   • Recurrent Neural Networks Explanation
   • seq2seq model
   • Introduction to Long Short Term Memory
   • Long Short Term Memory Networks Explanation
   • Gated Recurrent Unit Networks(GAN)
   • Text Generation using Gated Recurrent Unit Networks
10. GANs – Generative Adversarial Network
    • Introduction to Generative Adversarial Network
    • Generative Adversarial Networks (GANs) - Type, Architecture
    • Use Cases of Generative Adversarial Networks
    • Building a Generative Adversarial Network using Keras
    • Modal Collapse in GANs
11. Introduction to Deep Q-Learning
12. Implementing Deep Q-Learning using Tensorflow

ML Model Deployment

1. Machine learning deployement
2. Deploy your Machine Learning web app (Streamlit) on Heroku
3. Deploy a Machine Learning Model using Streamlit Library
4. Deploy Machine Learning Model using Flask
5. Python – Create UIs for prototyping Machine Learning model with Gradio
6. How to Prepare Data Before Deploying a Machine Learning Model?
7. Deploying ML Models as API using FastAPI
   
8. Deploying Scrapy spider on ScrapingHub

ML Projects

1. Rainfall prediction using Linear regression
2. Identifying handwritten digits using Logistic Regression in PyTorch
3. Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
4. Python | Implementation of Movie Recommender System
5. Support Vector Machine to recognize facial features in C++
6. Decision Trees – Fake (Counterfeit) Coin Puzzle (12 Coin Puzzle)
7. Credit Card Fraud Detection
8. NLP analysis of Restaurant reviews
9. Applying Multinomial Naive Bayes to NLP Problems
10. Image compression using K-means clustering
11. Deep learning | Image Caption Generation using the Avengers EndGames Characters
12. How Does Google Use Machine Learning?
13. How Does NASA Use Machine Learning?
14. 5 Mind-Blowing Ways Facebook Uses Machine Learning
    
15. Targeted Advertising using Machine Learning
16. How Machine Learning Is Used by Famous Companies?

FAQs on Machine Learning Tutorial

Q.1 What is Machine learning and how is it different from Deep learning ?

Answer:

Machine learning develop programs that can access data and learn from it. Deep learning is the sub domain of the machine learning. Deep learning supports automatic extraction of features from the raw data.

Q.2. What are the different type of machine learning algorithms ?

Answer:

• Supervised algorithms: These are the algorithms which learn from the labelled data, e.g. images labelled with dog face or not. Algorithm depends on supervised or labelled data. e.g. regression, object detection, segmentation.
• Non-Supervised algorithms: These are the algorithms which learn from the non labelled data, e.g. bunch of images given to make a similar set of images. e.g. clustering, dimensionality reduction etc.
• Semi-Supervised algorithms: Algorithms that uses both supervised or non-supervised data. Majority portion of data use for these algorithms are not supervised data. e.g. anamoly detection.

Q.3. Why we use machine learning ?

Answer:

Machine learning is used to make decisions based on data. By modelling the algorithms on the bases of historical data, Algorithms find the patterns and relationships that are difficult for humans to detect. These patterns are now further use for the future references to predict solution of unseen problems.

kartik

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