Machine Learning for Beginners. Your roadmap to success.

by | Feb 26, 2024 | Machine Learning

Are you eager to dive into the world of machine learning but unsure where to start? This blog is your go-to manual, designed for beginners seeking to master machine learning skills.

The article will take you through a detailed roadmap with some of the best resources available on the internet. We include machine learning courses, articles, tutorials, and books, all from scratch, that’ll help you begin your journey into machine learning and data science.

This step-by-step guide will take you from the very basics of machine learning, diving deep into the algorithms, all the way to the best model-building techniques and more advanced topics like deep learning and artificial intelligence.

Every part has a dedicated resources section for beginners to explore various courses and articles available on the internet for related topics.

Buckle up for the journey!

 

Roadmap to Learning

  1. Introduction to Machine Learning
  2. Prerequisites for Machine Learning
  3. Machine Learning Fundamentals
  4. Machine Learning Algorithms
  5. Courses to Grasp Fundamentals
  6. Data Processing and Feature Engineering
  7. Model Building Techniques
  8. Model Evaluation Techniques
  9. IDEs and Online Platforms
  10. Advanced ML and Deep Learning

 

1. Introduction to Machine Learning

  • Machine learning is a branch of computer science and a subset of artificial intelligence where we train a machine/computer to learn patterns from data and then make predictions based on those patterns.
  • From recommending movies on Netflix to predicting the next word you’ll type, machine learning is behind many of the technologies we use every day. That includes industries like healthcare, finance, and transportation, among others.
  • Learning machine learning is incredibly exciting and valuable. It opens up doors to endless possibilities, allowing you to solve real-world problems, automate tasks, and make better decisions based on data.

Check the links below to learn more about Machine Learning and Data Science:

Machine learning for beginners, roadmap

2. Prerequisites for Machine Learning

Basics of Statistics

By understanding statistical concepts, you can make informed decisions about which machine learning algorithms to use, how to evaluate their performance, and how to interpret the results.

Some important statistical concepts for machine learning are:

  • Descriptive Statistics:
    • Mean, median, mode
    • Variance and standard deviation
    • Percentiles and quartiles
    • Skewness and Kurtosis
  • Probability:
    • Probability distributions (e.g., Gaussian/Normal, Poisson, Binomial)
    • Conditional probability
    • Bayes‘ theorem
    • Random variables and expected values
  • Inferential Statistics:
    • Hypothesis testing (e.g., t-tests, chi-squared tests)
    • Confidence intervals
    • Type I and Type II errors
    • p-values
  • Data Sampling
    • Random sampling techniques
    • Cross-validation (k-fold, leave-one-out)
    • Bootstrap resampling

Resources:

 

Basics of Programming

  • In the realm of machine learning, programming skills are essential for bringing algorithms to life and manipulating and transforming data into insights. So you’ll have to wear the hat of a programmer diving further!
  • Programming languages popular for Machine Learning and analytics are Python, R, Matlab, and Java.
  • Python is the most commonly used programming language in machine learning due to its simplicity, versatility, and extensive libraries and data analytics tools like NumPy, Pandas, and Scikit-learn.
  • Many machine learning frameworks like Scikit-learn, PyTorch, TensorFlow, and Keras, are built for Python which allows beginners to quickly prototype machine learning models and offers a rich ecosystem for data manipulation, visualization, and model building.

Resources:

 

3. Types of Machine Learning

There are fundamentally 3 types of machine learning strategies:

  • Supervised Learning:
    • In supervised learning, the algorithm is trained on a labeled dataset, where each data point is associated with a corresponding target variable. The goal is to learn a mapping from input features to output labels based on the provided examples.
    • Supervised Learning can be further divided into Regression and Classification
  • Resources:
  • Unsupervised Learning:
    • In unsupervised learning, the algorithm is presented with an unlabeled dataset, and its task is to find patterns, structures, or relationships within the data without explicit guidance. This type of learning is used where algorithms autonomously identify hidden patterns and insights.
    • Resources: What is Unsupervised Learning – IBM
  • Reinforcement Learning:
    • Reinforcement learning is a type of machine learning where an agent learns to interact with an environment by performing actions and receiving rewards or penalties in return. The goal is to learn a policy that maximizes cumulative rewards over time, enabling the agent to make informed decisions and adapt its behavior based on feedback from the environment.
    • Resources: What is Reinforcement Learning – IBM

Additional Resources:

 

4. Machine Learning Algorithms

Machine learning algorithms serve as the cornerstone of predictive modeling and decision-making, empowering computers to autonomously learn from data and make predictions or decisions.

 

Supervised Learning

Regression

Regression algorithms facilitate the prediction of continuous values based on input features. Common regression algorithms include linear regression, polynomial regression, decision tree regression, etc.

Resources: 10 regression algorithms you should know

 

Regression Algorithms —

Classification

Classification enables the prediction of discrete labels or categories from input data. They are integral to tasks such as email spam detection, sentiment analysis, and medical diagnosis. Widely used classification algorithms include logistic regression, decision tree classification, support vector machines (SVM), k-nearest neighbors (KNN), etc.

Resources: Classification Algorithms in Machine Learning

Classification Algorithms —

Unsupervised Learning

Clustering Algorithms

Clustering algorithms are pivotal for grouping similar data points into clusters based on their intrinsic similarities. They find applications in customer segmentation, image segmentation, and anomaly detection.

Key clustering algorithms are:

Dimensionality Reduction

Dimensionality reduction algorithms streamline data by reducing the number of input features while retaining critical information. They are beneficial for tasks like data visualization, feature extraction, and noise reduction.

Key dimensionality reduction algorithms are:

Reinforcement Learning Algorithms

Reinforcement Learning (RL) is about an agent learning to interact with an environment to maximize rewards.

Resources: Fundamentals of Reinforcement Learning – University of Alberta

Here are some major RL algorithms:

5. Courses to Grasp Fundamentals

This section caters to the machine learning courses available on the internet which cover everything from the basics of algorithms to practicing exciting machine learning projects and modeling with hands-on experience.

6. Data Preprocessing and Feature Engineering

Python Feature Engineering Cookbook book cover

7. Model Building Techniques

Model-building techniques play a crucial role in building machine learning models, which are suitable to a given task based on factors like performance, interpretability, and computational efficiency.

Some model-building techniques are:

8. Model Evaluation 

Model evaluation is a critical step in assessing the performance and effectiveness of machine learning models using performance metrics like:

Regularization Performance Metrics

  • Mean Squared Error (MSE): The average squared difference between the predicted and actual values.
  • Root Mean Squared Error (RMSE): The square root of the average squared difference between the predicted and actual values.
  • R-Squared: The proportion of variance in the dependent variable that is explained by the independent variables, with values closer to 1 signifying a better fit.

Classification Performance Metrics

  • Confusion Matrix: Summarizes a classification model’s performance by comparing actual and predicted values in a tabular format.
  • Accuracy: Represents the proportion of correct predictions made by the model out of all predictions.
  • Precision: Reflects the ratio of true positive predictions to all positive predictions made by the model, highlighting its ability to avoid false positives.
  • Recall: Indicates the ratio of true positive predictions to all actual positive instances, demonstrating the model’s capability to identify positives correctly.
  • F1 Score: Quantifies the balance between precision and recall, providing a single metric that combines both measures into a harmonic mean.
  • Receiver Operating Characteristic (ROC) Curve: Illustrates the trade-off between true positive rate and false positive rate at various classification thresholds, by calculating the area under the ROC curve (AUC).

Resources:

9. IDEs and Online Platforms

Integrated Development Environments(IDEs) are essential tools for machine learning (ML) practitioners, providing a comprehensive platform for writing, testing, and deploying ML models. Many IDEs are open-source and provide APIs for interacting with machine learning libraries and frameworks like TensorFlow, PyTorch, Scikit-learn, etc.

Some popular IDEs for ML:

Online Platforms for hands-on ML and finding datasets:

10. Advanced ML and Deep Learning

Neural Networks are the building blocks of advanced ML and Deep Learning. They employ interconnected layers of nodes to learn complex patterns and relationships in the data, making it suitable for big data computation and complex tasks.

Neural Network models are state-of-the-art and have use cases in domains like Computer Vision, Natural Language Processing (NLP), Speech Recognition, Image Recognition, Autonomous Vehicles and Self-Driving cars, Robotics, and the popular Generative AI.

machine learning courses at train in data.

Resources:

Additional resources:

 

If you made it to the end of the article, well done! You are way on your way to becoming a machine learning expert. Good luck and happy learning!