Machine Learning Roadmap: Getting Started With Machine Learning

Welcome to the Machine Learning Roadmap! Whether you’re a curious beginner or an experienced professional, this guide is tailored to introduce you to the exciting world of machine learning (ML).

Let’s embark on a journey to explore the fundamental concepts and advanced techniques that power the domains of artificial intelligence.

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

This machine learning tutorial helps you gain a solid introduction to the fundamentals of machine learning and explore a wide range of techniques, including supervised, unsupervised, and reinforcement learning.

Prerequisites to learn machine learning

  • Knowledge of Linear equations, graphs of functions, statistics, Linear Algebra, Probability, Calculus etc.
  • Any programming language knowledge like Python, C++, R are recommended.

Here is the Full Machine Learning Roadmap from beginners to Advanced Level.

1. Understanding the Features of Machine learning

  • Machine learning is a technology driven by Data. Large amount of data generated by organizations on daily basis. So, by notable relationships in data, organizations makes better decisions.
  • Machine can learn itself from past data and automatically improve.
  • From the given dataset it detects various patterns on data.
  • For the big organizations branding is important and it will become more easy to target relatable customer base.
  • It is similar to data mining because it is also deals with the huge amount of data.

2. What is Machine Learning?

The first Step is understanding what machine Learning is;

Machine Learning (ML) empowers computers to learn without explicit programming. It’s akin to giving machines a human-like ability: the power to learn from data.

ML is ubiquitous today, permeating various aspects of our lives more than we might realize.

3. Machine Learning Mathematics

Next you need to have a better understanding of Machine Learning Mathematics.

Machine Learning Mathematics lays the foundation for understanding the algorithms and models driving ML systems. It’s essential to grasp concepts like linear algebra, statistics, probability, and calculus for a deeper understanding of ML algorithms.

4. Data Pre-processing

Understanding data is pivotal in ML. Data Pre-processing techniques refine raw data, preparing it for analysis. Techniques like data cleaning, feature scaling, and encoding enhance data quality, paving the way for accurate model training.

5. Introduction to Machine Learning

  • Introduction to Data in Machine Learning
  • Demystifying Machine Learning
  • ML – Applications
  • Best Python libraries for Machine Learning
  • Artificial Intelligence | An Introduction
  • Machine Learning and Artificial Intelligence
  • Difference between Machine learning and Artificial Intelligence
  • Agents in Artificial Intelligence
  • 10 Basic Machine Learning Interview Questions

6. Machine Learning Data and It’s Processing:

  • Introduction to Data in Machine Learning
  • Understanding Data Processing
  • Python | Create Test DataSets using Sklearn
  • Python | Generate test datasets for Machine learning
  • Python | Data Preprocessing in Python
  • Data Cleaning
  • Feature Scaling in Machine Learning
  • Python | Label Encoding of datasets
  • Python | One Hot Encoding of datasets
  • Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python
  • Dummy variable trap in Regression Models

7. Supervised learning :

¡》Getting started with Classification

¡¡》Basic Concept of Classification

¡¡¡》Types of Regression Techniques

¡v》Classification vs Regression

V》ML | Types of Learning – Supervised Learning

》Multiclass classification using scikit-learn

》Gradient Descent :

  • Gradient Descent algorithm and its variants
  • Stochastic Gradient Descent (SGD)
  • Mini-Batch Gradient Descent with Python
  • Optimization techniques for Gradient Descent
  • Introduction to Momentum-based Gradient Optimizer

V¡¡》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)
  • Simple Linear-Regression using R
  • Univariate Linear Regression in Python
  • Multiple Linear Regression using Python
  • Multiple Linear Regression using R
  • Locally weighted Linear Regression
  • Generalized Linear Models
  • Python | Linear Regression using sklearn
  • Linear Regression Using Tensorflow
  • A Practical approach to Simple Linear Regression using R
  • Linear Regression using PyTorch
  • Pyspark | Linear regression using Apache MLlib
  • ML | Boston Housing Kaggle Challenge with Linear Regression

V¡¡¡》Python | Implementation of Polynomial Regression

X》Softmax Regression using TensorFlow

》Logistic Regression :

  • Understanding Logistic Regression
  • Why Logistic Regression in Classification ?
  • Logistic Regression using Python
  • Cost function in Logistic Regression
  • Logistic Regression using Tensorflow

X¡¡》Naive Bayes Classifiers

X¡¡¡》Support Vector:

  • Support Vector Machines(SVMs) in Python
  • SVM Hyperparameter Tuning using GridSearchCV
  • Support Vector Machines(SVMs) in R
  • Using SVM to perform classification on a non-linear dataset

X¡V》Decision Tree:

  • Decision Tree
  • Decision Tree Regression using sklearn
  • Decision Tree Introduction with example
  • Decision tree implementation using Python
  • Decision Tree in Software Engineering

XV》Random Forest:

  • Random Forest Regression in Python
  • Ensemble Classifier
  • Voting Classifier using Sklearn
  • Bagging classifier

8. Unsupervised learning :

  • ML | Types of Learning – Unsupervised Learning
  • Supervised and Unsupervised learning
  • Clustering in Machine Learning
  • Different Types of Clustering Algorithm
  • K means Clustering – Introduction
  • Elbow Method for optimal value of k in KMeans
  • Random Initialization Trap in K-Means
  • ML | K-means++ Algorithm
  • Analysis of test data using K-Means Clustering in Python
  • Mini Batch K-means clustering algorithm
  • Mean-Shift Clustering
  • DBSCAN – Density based clustering
  • Implementing DBSCAN algorithm using Sklearn
  • Fuzzy Clustering
  • Spectral Clustering
  • OPTICS Clustering
  • OPTICS Clustering Implementing using Sklearn
  • Hierarchical clustering (Agglomerative and Divisive clustering)
  • Implementing Agglomerative Clustering using Sklearn
  • Gaussian Mixture Model

9. Reinforcement Learning:

  • Reinforcement learning
  • Reinforcement Learning Algorithm : Python Implementation using Q-learning
  • Introduction to Thompson Sampling
  • Genetic Algorithm for Reinforcement Learning
  • SARSA Reinforcement Learning
  • Q-Learning in Python

10. Dimensionality Reduction :

  • Introduction to Dimensionality Reduction
  • Introduction to Kernel PCA
  • Principal Component Analysis(PCA)
  • Principal Component Analysis with Python
  • Low-Rank Approximations
  • Overview of Linear Discriminant Analysis (LDA)
  • Mathematical Explanation of Linear Discriminant Analysis (LDA)
  • Generalized Discriminant Analysis (GDA)
  • Independent Component Analysis
  • Feature Mapping
  • Extra Tree Classifier for Feature Selection
  • Chi-Square Test for Feature Selection – Mathematical Explanation
  • ML | T-distributed Stochastic Neighbor Embedding (t-SNE) Algorithm
  • Python | How and where to apply Feature Scaling?
  • Parameters for Feature Selection
  • Underfitting and Overfitting in Machine Learning

11. Natural Language Processing :

  • Introduction to Natural Language Processing
  • Text Preprocessing in Python | Set – 1
  • Text Preprocessing in Python | Set 2
  • Removing stop words with NLTK in Python
  • Tokenize text using NLTK in python
  • How tokenizing text, sentence, words works
  • Introduction to Stemming
  • Stemming words with NLTK
  • Lemmatization with NLTK
  • Lemmatization with TextBlob
  • How to get synonyms/antonyms from NLTK WordNet in Python?

12. Neural Networks :

¡》Introduction to Artificial Neutral Networks

¡¡》Introduction to ANN (Artificial Neural Networks) Hybrid Systems

¡¡¡》Introduction to ANN | Set 4 (Network Architectures)

¡V》Activation functions

V》Implementing Artificial Neural Network training process in Python

VI》A single neuron neural network in Python

V¡¡》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

V¡¡¡》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

X》GANs – Generative Adversarial Network

  • Introduction to Generative Adversarial Network
  • Generative Adversarial Networks (GANs)
  • Use Cases of Generative Adversarial Networks
  • Building a Generative Adversarial Network using Keras
  • Modal Collapse in GANs

X¡》Introduction to Deep Q-Learning

X¡¡》Implementing Deep Q-Learning using Tensorflow

13. ML – Deployment :

  • Deploy your Machine Learning web app (Streamlit) on Heroku
  • Deploy a Machine Learning Model using Streamlit Library
  • Deploy Machine Learning Model using Flask
  • Python – Create UIs for prototyping Machine Learning model with Gradio
  • How to Prepare Data Before Deploying a Machine Learning Model?
  • Deploying ML Models as API using FastAPI
  • Deploying Scrapy spider on ScrapingHub

13  ML – Applications :

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

14. Misc :

  • Pattern Recognition | Introduction
  • Calculate Efficiency Of Binary Classifier
  • Logistic Regression v/s Decision Tree Classification
  • R vs Python in Datascience
  • Explanation of Fundamental Functions involved in A3C algorithm
  • Differential Privacy and Deep Learning
  • Artificial intelligence vs Machine Learning vs Deep Learning
  • Introduction to Multi-Task Learning(MTL) for Deep Learning
  • Top 10 Algorithms every Machine Learning Engineer should know
  • Azure Virtual Machine for Machine Learning
  • 30 minutes to machine learning
  • What is AutoML in Machine Learning?
  • Confusion Matrix in Machine Learning

15. Advanced Machine Learning Topics

Delve into advanced ML topics like AutoML, Differential Privacy, and Multi-Task Learning.

Stay ahead in the ever-evolving landscape of machine intelligence.

Conclusion

Whether you’re preparing for your first job interview or aiming to upskill in this ever-evolving tech landscape, Ternet Digitals machine Learning Course is your key to success. We provide top-quality content at  for FREE.

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Congratulations on completing the Machine Learning Roadmap! Armed with a solid foundation in ML concepts and techniques, you’re ready to embark on your journey to explore the limitless possibilities of artificial intelligence. Keep learning, exploring, and innovating in this fascinating field!

Happy Machine Learning! 🚀

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