Mastering Machine Learning: A [Step-by-Step] Guide for Beginners

Machine Learning Expert

Friday, January 5th, 2024.

Have you ever noticed how the internet seems to know what you like?

Well, it’s because of something called machine learning. 

It’s like a smart computer program that learns from data and helps make decisions.

Machine learning is used in many things we see online, like the posts we see on social media and the ads that pop up.

It’s pretty cool.

Here’s a fun fact: Companies all over the world use machine learning, and it’s predicted that by 2030, the global machine learning market will be worth a lot of money—USD 419.94 billion, to be exact.

That’s a huge amount of money!

As you already know, many people are using machine learning nowadays, and it’s becoming increasingly popular in various industries. However, did you know that 8 out of 10 people don’t know how to begin for machine learning courses.

And If you’re curious about machine learning and want to learn more, YHills will take it step by step and explain everything in a simple way so that no doubt remains: we’ll leave you confident that this new technology is within your grasp.

Let’s get started on a journey to becoming machine learning experts.

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Machine Learning Course Overview

Before understanding machine learning, it’s essential to know what it is not.

Machine learning is not AI itself, but it’s part of AI.

AI wants to create smart systems like humans, with reasoning, problem-solving, learning, and more. It uses various methods like machine learning, expert systems, and robotics.

Machine learning, on the other hand, relies on algorithms to analyze lots of data and make predictions based on that data.

Introduction to Machine Learning

Machine learning is a field of artificial intelligence (AI) that involves training computers to learn from data and make predictions or decisions without being explicitly programmed for every specific task. 

It finds applications in various fields, such as image and speech recognition, medical diagnosis, recommendation systems, fraud detection, and more.

It’s like teaching a computer to recognize patterns and make sense of information on its own—and it’s a key part of the future of AI.  

Here’s a quick example:

Let’s say you want to build a spam email filter. Instead of manually writing rules to identify spam, you can use machine learning.

You provide the computer with a large dataset of labeled emails, where each email is labeled as either spam or not spam. The computer analyzes the characteristics of these emails, such as keywords, email addresses, or formatting patterns. It learns to recognize patterns that differentiate spam from legitimate emails.

Once the machine learning model is trained on this data, it can be used to classify new, unseen emails as either spam or not spam. It applies the patterns it has learned to make predictions based on the features of the incoming emails.

Over time, as the model receives feedback on its predictions, it can further refine and improve its accuracy.

Key Concepts and Terminology

The following are some key concepts and terminology used in machine learning:

  • Data: Data is the most important aspect of machine learning. It can be any kind of information, such as text, images, audio, or video.
  • Features: Features are specific characteristics of the data that you want to analyze using machine learning algorithms. For example, while analyzing an image using a neural network to classify it as either cat or dog, the color and shape of an animal would be featured.
  • Model: A model is a representation of something in terms of its essential characteristics. This could be anything from building a mathematical formula based on historical data points to create predictions about future events (e.g., stock prices).
  • Parameters: Parameters are the variables that define and shape a model. For example, when training a neural network to classify images as either cat or dog, the parameters would be the number of hidden layers in the neural net and how many neurons per layer.
  • Algorithm: An algorithm is a set of steps that can be followed to perform a task. For example, in order to train a neural network to classify images as cat or dog, we need an algorithm that allows us to provide the appropriate data and then adjust the parameters until we get the desired result.

The steps of the algorithm might be something like:

  1. Collect a training set of cat and dog images.
  2. Convert each image into a vector of numbers that represent its brightness, color, shape, etc.
  3. Train a neural network using backpropagation.

Types of Machine Learning Algorithms

Machine learning algorithms are the building blocks of the field, enabling computers to learn from data and make predictions or take actions. 

There are several types of machine learning algorithms, each designed to solve different types of problems. 

Let’s explore some of the most common ones:

  1. Supervised Learning Algorithms

Supervised learning algorithms learn from labeled data, where each example in the data set has input features and corresponding output labels. 

These algorithms aim to learn a mapping between the input features and the output labels, allowing them to predict labels for new, unseen examples. 

Some popular supervised learning algorithms include:

  • Linear Regression: Used for predicting continuous values based on input features.
  • Logistic Regression: Used for binary classification problems, where the output is either one of two classes.
  • Decision Trees: Employ a hierarchical structure to make decisions based on input features.
  • Random Forest: Ensemble of decision trees that combine their predictions for improved accuracy.
  • Support Vector Machines (SVM): Find a hyperplane that separates different classes of data points.
  1. Unsupervised Learning Algorithms

Unsupervised learning algorithms deal with unlabeled data, where there are no output labels provided. 

These algorithms aim to discover patterns, structures, or relationships in the data without any explicit guidance. Some common unsupervised learning algorithms include:

  • Clustering Algorithms: Group similar data points together based on their characteristics.
  • K-Means Clustering: Divides data into k clusters based on feature similarity.
  • Hierarchical Clustering: Builds a hierarchy of clusters based on the distance between data points.
  • Dimensionality Reduction Algorithms: Reduce the number of input features while preserving important information.
  • Principal Component Analysis (PCA): Finds the most important features that explain the most variance in the data.
  • t-SNE (t-Distributed Stochastic Neighbor Embedding): Visualizes high-dimensional data in a lower-dimensional space.
  1. Semi-Supervised Learning Algorithms

Semi-supervised learning algorithms combine both labeled and unlabeled data for training. They leverage the small amount of labeled data and the larger amount of unlabeled data to make predictions or decisions. 

These algorithms are useful when acquiring labeled data is expensive or time-consuming.

  1. Reinforcement Learning Algorithms

Reinforcement learning algorithms learn through interactions with an environment to maximize a reward signal. They aim to find an optimal policy or decision-making strategy by trial and error. 

Reinforcement learning is often used in scenarios where an agent learns to play games, control robots, or make autonomous decisions.

  1. Deep Learning Algorithms

Deep learning algorithms are a subset of machine learning algorithms inspired by the structure and function of the human brain. They are designed to process and learn from large amounts of data using artificial neural networks. 

Deep learning has revolutionized various fields, including computer vision, natural language processing, and speech recognition.

Throughout the course, you will explore each of these types of machine learning algorithms in detail. You will learn how to implement and apply these algorithms using popular libraries and frameworks such as TensorFlow and scikit-learn. 

Understanding the strengths, weaknesses, and use cases of different algorithms, you will gain the necessary knowledge to tackle a wide range of machine learning problems.

Applications of Machine Learning

Machine learning has a wide range of applications across various industries and domains. Let’s take a look at some of the key areas where machine learning is making a significant impact:

 

  1. Image and Speech Recognition

Machine learning algorithms have revolutionized image and speech recognition tasks. They can accurately classify objects within images, identify people’s faces, and transcribe speech into text. These applications are used in various domains, including self-driving cars, security systems, healthcare diagnostics, and voice assistants like Siri and Alexa.

  1. Natural Language Processing (NLP)

NLP focuses on enabling computers to understand, interpret, and generate human language. Machine learning techniques are employed to process and analyze vast amounts of textual data, allowing applications such as sentiment analysis, language translation, chatbots, and question-answering systems. NLP is widely used in customer service, content generation, and information retrieval.

  1. Recommender Systems

Recommender systems use machine learning algorithms to analyze user preferences and provide personalized recommendations. These systems are widely used in e-commerce platforms, streaming services, and social media platforms to suggest products, movies, music, and relevant content to users. By leveraging historical user data, recommender systems can improve user engagement and satisfaction.

  1. Fraud Detection and Cybersecurity

Machine learning plays a crucial role in detecting and preventing fraudulent activities. By analyzing patterns and anomalies in large volumes of data, machine learning algorithms can identify suspicious transactions, detect network intrusions, and classify potentially malicious behavior. These applications are vital in financial institutions, e-commerce, and cybersecurity systems.

  1. Healthcare and Medical Diagnosis

Machine learning is transforming the healthcare industry by enabling more accurate medical diagnosis, personalized treatment plans, and predicting disease outcomes. Algorithms can analyze patient data, medical images, and genetic information to assist in the early detection of diseases, drug discovery, and improving patient care.

  1. Financial Forecasting and Trading

Machine learning algorithms are extensively used in the financial sector for tasks such as stock market prediction, credit risk assessment, fraud detection, and algorithmic trading. By analyzing historical data, market trends, and economic indicators, machine learning models can assist in making informed investment decisions and managing financial risks.

  1. Autonomous Vehicles and Robotics

Machine learning algorithms are at the core of developing autonomous vehicles and robotics systems. These algorithms enable vehicles to perceive the environment, make real-time decisions, and navigate safely. Machine learning also contributes to robotics applications such as object recognition, motion planning, and grasping objects with robotic arms.

  1. Environmental Monitoring and Predictions

Machine learning techniques are utilized in environmental monitoring and predictions, including weather forecasting, climate modeling, and pollution control. By analyzing vast amounts of data from sensors, satellites, and historical records, machine learning algorithms can improve predictions, help in disaster management, and contribute to environmental conservation efforts.

Machine Learning Course Structure

Now, you need to consider how the course could be structured and what you can expect to learn as you progress through it and many more that will help you become a machine learning expert.

Duration and Format

The machine learning course offered is usually available in two formats:

  • Institutional Program
  • Online Course

Institutional Program

Institutional programs offer the machine learning course as a part of a bachelor’s or master’s degree program. The duration and structure of the course may vary depending on the degree program and the institution’s curriculum.

Here is a general overview of the duration and format of the Institutional Program:

  • Duration: Varies depending on the degree program and institution.
  • Format: On-campus or blended learning.

Institutional programs typically provide a comprehensive learning experience that includes lectures, seminars, practical assignments, and assessments.

The course is designed to provide students with a deep understanding of machine learning concepts, algorithms, and their applications. Students enrolled in the Institutional Program have the opportunity to engage with professors and fellow students, fostering collaboration and knowledge sharing.

For specific details regarding the duration and structure of the machine learning course within an institutional program, we recommend referring to the guidelines provided by your institution.

Online Course

Online Course is specifically designed to cater to the needs of working professionals who want to learn the fundamentals of machine learning and apply them in their work just like YHills.

The online format provides flexibility and convenience, allowing learners to study at their own pace.

Here is an overview of the duration and format of the Online Course:

  • Duration: Usually 2-3 Months (Varies based on platform)
  • Format: Online

The Online Course is structured to be completed within a suitable time frame, allowing working professionals to balance their learning goals with their professional commitments.

Learners have access to a dedicated online learning platform, which includes video lectures, interactive exercises, quizzes, and projects assignments.

This format enables learners to gain a solid understanding of machine learning principles and acquire practical skills applicable to real-world scenarios.

Prerequisites

In order to get the most out of this course, you should have a solid foundation in certain subjects.

The following prerequisites will help you excel machine learning:

PrerequisitesDescription
MathematicsA strong understanding of mathematics is crucial for machine learning. Familiarity with concepts like linear algebra, calculus, and probability will be beneficial.
Coding or Scripting ExperiencePrior coding or scripting experience is required for this course. Familiarity with a programming language such as Python will be beneficial as you will be working with coding exercises and implementing machine learning algorithms.
Numerical AbilitiesA minimum level of numerical abilities, typically at least at the high school level, will be necessary to understand and apply mathematical concepts in machine learning. You should feel comfortable with basic mathematical operations, algebra, and statistics.
Algorithms and Data StructuresFamiliarity with algorithms and data structures is important for optimizing and implementing machine learning models efficiently. Understanding concepts like sorting, searching, and graph algorithms will be valuable.
StatisticsKnowledge of statistics is necessary to interpret and analyze data. Concepts such as descriptive statistics, hypothesis testing, and regression should be familiar to you.

Course Modules and Topics Covered

This course gives you a comprehensive education in machine learning, with modules that cover various topics.

Each one is designed to give you a strong foundation of key concepts and techniques.

At the end of this course, you’ll be able to apply the knowledge and skills gained from these modules:

  • Useful Python Resources
  • Python Tools and Utilities
  • Python Features
  • Local Environment Setup
  • Downloads and Installations
  • Setting up Environment Path
  • Interactive Mode
  • Scripting Mode
  • Integrated Development Environment
  • Python Identifiers
  • Reserved Words
  • Lines and Indentation
  • Assigning Values to Variables
  • Multiple Assignment
  • Standard Data Types
  • Data Type Conversion
  • Arithmetic Operators
  • Comparison Operators
  • Assignment Operators
  • Bitwise Operators
  • Logical Operators
  • Membership Operators
  • Identity Operators
  • Operators Precedence
  • IF statements
  • IF…ELIF…ELSE Statements
  • Nested IF statements
  • While loop
  • For loop
  • Number type conversion
  • Mathematical function
  • Random number function
  • Trigonometric function
  • String special operators
  • String formatting operator
  • Built in string methods
  • Basic list operations
  • Indexing and slicing
  • Built in functions and methods
  • Basic tuple operations
  • Indexing and slicing
  • Built in functions
  • Basic Dictionary operations
  • Built in Functions and Methods
  • Use cases
  • Pass by reference and value
  • Function Arguments
  • Scope of variables
  • Default Argument Values
  • Keyword Arguments
  • Arbitrary Argument Lists
  • Unpacking Argument Lists
  • Lambda Expressions
  • Documentation Strings
  • Importing Modules
  • Writing and Reading Binary Data
  • Parsing Text Using Regular Expressions
  • Writing and Parsing XML Files
  • Element Trees
  • DOM (Document Object Model)
  • The except clause with multiple exceptions
  • The try-finally clause
  • Argument of an Exception
  • Raising an exceptions
  • User-Defined Exceptions
  • Creating Classes
  • Creating instance objects
  • Destroying Objects (Garbage Collection)
  • Custom Classes
  • Attributes and Methods
  • Lambda, Filter, Map
  • Functools, Itertools, Generators, Decorators
  • Deque
  • Counter
  • OrderedDict
  • ChainMap
  • os
  • sys
  • Shutil
  • psutil
  • subprocess
  • Requests – get,post
  • BeautifulSoup – findall,find,findparent
  • Characters and Character Classes
  • Quantifiers
  • Grouping and Capturing
  • Assertions and Flags
  • The Regular Expression Module
  • Supervised Learning
  • Unsupervised Learning
  • Linear Regression
  • Metrics in Linear Regression
  • Hands-on in Linear Regression
  • Logistic Regression
  • Metrics in Logistic Regression
  • Hands-on in Logistic Regression
  • Support Vector Machine
  • Hands on in SVM
  • Exploratory Data Analysis
  • Missing Values
  • Outliers
  • Standardization
  • Normalization
  • Feature Scaling and Selection
  • Modelling
  • Linear Regression – Python
  • Logistic Regression – Python
  • Decision Tree, Bagging, Boosting, Random Forest – Python

Hands-on Projects and Assignments

If the skills you’ve been learning in your classes will help you complete projects and assignments, then completing these tasks should be a great way to put those skills into practice.

You’ll also build skills—from the ground up or from scratch.

Plus, you’ll be able to apply your new knowledge in fun and interesting ways with these kind of projects:

In this project, you will tackle the challenge of detecting online payments fraud. You will work with a real dataset that contains various transactional features, such as transaction amount, time, location, and customer information. Your task will be to develop a machine learning model that can accurately identify fraudulent transactions and minimize false positives.
In this project, you will delve into the realm of website traffic forecasting. Using historical website traffic data, you will build a predictive model that can estimate future website traffic. This project will help you understand time series analysis and forecasting techniques, enabling you to make informed decisions about resource allocation, marketing strategies, and website performance optimization.
Credit card data often contains a vast amount of information about customer spending habits. In this project, you will explore credit card clustering, which involves grouping credit card users based on their transaction patterns. By applying clustering algorithms, you will uncover hidden segments within the customer base and gain valuable insights for targeted marketing campaigns and personalized recommendations.
Developing a robust password strength checker is crucial in ensuring the security of user accounts. In this project, you will create a machine learning model that can evaluate the strength of a password based on various criteria such as length, complexity, and uniqueness. This project will enhance your understanding of text classification and feature engineering techniques.
Spam comments can be a nuisance and pose security risks on websites. In this project, you will build a machine learning model to detect and filter out spam comments from a dataset containing user comments. You will explore techniques such as text preprocessing, feature extraction, and classification algorithms to accurately identify and flag spam comments, improving user experience and website security.
Music genre classification is a challenging task due to the subjective nature of musical preferences. In this project, you will work with a dataset of audio features and apply clustering algorithms to group songs into distinct music genres. By analyzing audio characteristics such as tempo, energy, and spectral features, you will gain insights into the similarities and differences between music genres.
Handling consumer complaints efficiently is vital for maintaining customer satisfaction. In this project, you will build a machine learning model that can classify consumer complaints into different categories based on their content. By automating the complaint categorization process, companies can prioritize and address customer concerns more effectively.
Predicting tips in the hospitality industry can help waitstaff optimize their service and maximize their earnings. In this project, you will analyze a dataset containing information about restaurant bills, customer demographics, and waiter tips. Using regression techniques, you will develop a predictive model that can estimate the tip amount based on various factors. This project will sharpen your regression modeling skills and provide insights into customer behavior.
Social media platforms like Twitter offer valuable insights into public sentiment. In this project, you will perform sentiment analysis on Twitter data related to Ukraine and Russia. You will use natural language processing techniques to classify tweets into positive, negative, or neutral sentiments. This project will give you a deeper understanding of text processing, sentiment analysis, and geopolitical data analysis.

Throughout these hands-on projects and assignments, you will receive guidance and feedback from our experienced instructors.

Assessment and Examination in Machine Learning

Your progress in this course will be evaluated through multiple assessments and exams to gauge your knowledge and skills. The final project will encompass all that you’ve learned, so there’s no need to worry if you feel pressed for time to complete it.

Even if you have made significant progress but are unable to finish, rest assured that you will still receive a certificate of completion for the course.

For assessing and testing your machine learning skills, there are several available resources. Here are some options for machine learning assessments and exams:

  1. TestGorilla: TestGorilla offers a machine learning test to assess candidates’ technical knowledge of core concepts like regularization, classification, and unsupervised learning.
  2. Workera: As part of their standardized tests, Workera has developed a machine learning test that evaluates the skills of individuals in roles such as Machine Learning Engineer, Data Scientist, Machine Learning Researcher, or Software Engineer-Machine Learning. This test consists of multiple-choice questions and takes approximately 17 minutes to complete.
  3. Adaface: Often used by recruiters and hiring managers, Adaface provides a machine learning assessment test to evaluate candidates’ machine learning skills before the interview.
  4. Gyfted: Gyfted offers a free machine learning test that allows individuals to practice their programming skills and focuses on key machine learning concepts.
  5. iMocha: iMocha provides various machine learning tests to assess candidates’ skills, including assessments in Azure ML, Blockchain, TensorFlow, NLP, Spark ML, and ANNs.

These exams and assessments evaluate proficiency in machine learning concepts and skills. They cover a range of topics, such as regression methods, classification methods, tree-based algorithms—and more.

Employers can use these assessments to identify candidates with the necessary knowledge and skills in machine learning for specific roles.

Machine Learning Course Content

You’ll find that most courses and tutorials cover similar topics, but with different perspectives.

That’s why it’s important to take a look at some of the most popular machine learning courses before you make your decision.

Here are some key topics covered by these courses:

These algorithms can make predictions based on data that’s been labeled by humans. These include decision trees, logistic regression, support vector machines and neural networks.
These are used to find patterns in unlabeled data. They include clustering and association rule mining and nearest neighbor algorithms.
This type of machine learning trains an agent to make optimal decisions based on a series of interactions with its environment.
These types of algorithms are used for advanced applications that require a high degree of flexibility and complexity. They involve multiple layers of processing and can be trained to recognize patterns in data.
This type of machine learning is used to select and engineer the best features for a given task. There are many different algorithms that can be applied here, including linear regression, Logistic regression, decision trees and support vector machines.
This is the last step in machine learning. It involves measuring how well a model performs on new data, and it’s an important part of ensuring that your model works as intended. There are many different ways to measure performance, including accuracy, precision, recall and F1 score.
Become a Better
ML Engineer
Over 200 students have already  registered for the next batch!

When do you want to start?

Tools and Software

Machine learning is a complex field, and there are many different tools of machine learning available to help you implement it.

Here’s a look at some of the most popular ones:

The most popular programming languages for machine learning are Python and R. Both are open-source, widely used and have large communities of developers. They’re also easy to learn and use, which makes them ideal if you’ve never programmed before. Other languages include Java and Scala, which are popular in enterprise settings because they offer support for object-oriented programming (OOP).
There are many software packages available to help you build and deploy machine learning models. TensorFlow, Caffe, Torch and Theano are some of the more popular options. Each has its own benefits and drawbacks but they all make it easier to implement your models on various devices and platforms. Many of these packages are open source and can be used for free, which makes them ideal if you’re on a budget.
Machine learning libraries are collections of functions that help you implement machine learning algorithms. They usually include functions for data preprocessing, model evaluation and hyperparameter optimization. They can be used together with other programming languages or on their own. Libraries like Scikit-learn are popular because they’re easy to use, efficient and well-documented.
Data manipulation and preprocessing tools play a crucial role in cleaning, filtering, organizing, and transforming raw data, making it suitable for feeding into machine learning models. Among the popular examples are Pandas, Numpy, and Matplotlib, known for their user-friendly interfaces and seamless integration with various programming languages and libraries. These tools are often employed in conjunction with machine learning libraries to streamline the entire data preparation process.
Visualization tools are often used in conjunction with data visualization libraries, such as Seaborn and Tableau, to create compelling and informative visual representations of data. These tools can be used for various purposes, including exploratory analysis and model evaluation. Some popular examples include D3.js (for building interactive visualizations), Bokeh (for creating high-quality graphics), and Plotly (for making publication-ready charts).

Practical Skills

Even if you’ve mastered the theoretical aspects of machine learning, there’s still a lot to learn when it comes to actually performing these tasks. This is where practical skills come in—they help you put theory into practice by implementing real-world solutions.

Few practical skills you should learn include:

Data preparation is the first step in any machine learning project. It involves collecting, cleaning and organizing data so that it’s ready for analysis. This process often involves removing irrelevant information from your dataset, such as instances where a feature has no value or cases where data has been incorrectly labeled (such as not knowing whether a particular patient had diabetes).
Once you have a clean dataset, it’s time to train your model. This process involves finding the best parameters (e.g., weights) that maximize the accuracy of your model on training data while minimizing its complexity. Once you’ve found these parameters, you can evaluate how well they perform on new data by testing them against an independent data set that wasn’t used during training.
Hyperparameters are variables that control the inner workings of your model. They include things like the learning rate, which controls how quickly your model learns; regularization parameters, which determine how much data is used in training; and so on. When you train a model using a lot of different hyperparameters (say, 20), it’s important to find the best combination of values for each parameter that maximize accuracy while minimizing complexity.
Deploying a machine learning model means you’re ready to put your model into production. You can do this by creating an API endpoint or webhook which will accept incoming data and send it through your model. Or you could use something like AWS Lambda, which lets you run code without provisioning servers.

In the real world, data is messy. There are missing values, outlier points that don’t fit the model, and other anomalies that make it difficult for your model to generalize well. This is especially true when working with text data (like tweets or emails), which often contains misspellings or abbreviations that confuse machine learning models. That’s why it’s important to handle these issues early on—before you spend more time and money trying to train your model.

For example, if you’re working with an email dataset, you might want to remove common email abbreviations (like “u” for “you”), deal with typos (“thier” instead of “their”), and handle special cases like emails written in all caps or italics.

Before you start training your model, it’s important to consider the ethical implications of what you’re doing.

For example, let’s say you were working with a data set that contained sensitive information about people—like their race or religion. If you trained an algorithm based on this data without taking measures to ensure its privacy and security, then anyone who has access to the model could learn things about these people that they wouldn’t want others knowing. This can lead to discrimination against certain groups of people and unfair treatment in hiring processes.

Resources and Support

As you begin to learn more about machine learning, there are many resources available to help you.

Here are a few that we recommend:

Machine Learning Books

  1. Machine Learning: 4 Books in 1 by Daniel Covington and Peter Nettesheim: This book is a complete guide for beginners to master the basics of Python programming and understand how to apply machine learning concepts in real-world scenarios.
  2. The Hundred-Page Machine Learning Book by Andriy Burkov: This book is a concise introduction to machine learning that covers a wide range of topics, including supervised and unsupervised learning, neural networks, and deep learning.
  3. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron: This book is a practical guide to machine learning that covers a range of topics, including regression, classification, clustering, and deep learning.
  4. Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong: This book is a comprehensive guide to the mathematical concepts that underpin machine learning, including linear algebra, calculus, and probability theory.
  5. Python Machine Learning by Sebastian Raschka and Vahid Mirjalili: This book is a practical guide to machine learning using Python, covering topics such as data preprocessing, feature selection, and model evaluation.

Online Tutorials and Video Lectures

A number of online resources are available to learn machine learning, including video lectures and tutorials  that can help you understand the concepts behind the algorithms.

Below are some of the best:

  1. Google’s Machine Learning Crash Course: This is a fast-paced, practical introduction to machine learning, featuring a series of lessons with video lectures, real-world case studies, and hands-on exercises. The course covers topics such as how machine learning differs from traditional programming, how to measure loss, how gradient descent works, and how to build a deep neural network.
  2. MyGreatLearning: This website provides both intermediate and basic tutorials of machine learning. It is designed for students and working professionals who are interested in learning machine learning. The website covers topics such as recommendation engines, sorting, tagging and categorizing photos, customer lifetime value, self-driving cars, education, determining creditworthiness, patient sickness predictions, and targeted emails.
  3. GeeksforGeeks: This website provides a comprehensive tutorial on machine learning that covers basic and advanced concepts, specially designed to cater to both students and experienced working professionals. The 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.
  4. Toptal: This website provides a machine learning tutorial with examples that introduces the basics of machine learning theory and its applications. The tutorial lays down the common themes and concepts, making it easy to follow the logic and get comfortable with machine learning basics. The website covers topics such as the supply of expert ML designers, basic theory, practical advice, and material taught by Stanford professor Dr. Andrew Ng in his free and open “Supervised Machine Learning” course.
  5. Machine Learning Mastery: This website provides a comprehensive guide to machine learning that covers topics such as linear algorithms, nonlinear algorithms, ensemble algorithms, how to study/learn ML algorithms, how to learn a machine learning algorithm, how to study machine learning algorithms, how to research a machine learning algorithm, how to investigate machine learning algorithm behavior, and R machine learning posts.

Dedicated Course Website or Learning Platform

You can find a lot of information about machine learning online.

However, if you’re serious about learning the subject, it’s worth purchasing a dedicated course or platform.

This will give you access to interactive content that makes the material easier to understand, as well as exercises and quizzes for practice and reinforcement.

Some of the most popular resources include:

  1. Yhills: Yhills offers a best machine learning certification course that helps learners understand the basics of real-world applications in india. The course is 2-3 months long and covers topics such as building and training models from scratch.
  2. Free Video Lectures: Free Video Lectures provides a range of machine learning courses from top universities such as Harvard. The courses cover various topics, including machine learning algorithms and Python programming.
  3. LearnDataSci: LearnDataSci has compiled a list of the best machine learning courses available online. The courses are subject to strict criteria, including a focus on machine learning and the use of free, open-source programming languages such as Python or R. The list includes courses from platforms such as Coursera, Edx, and Google AI Education.
  4. Google’s Machine Learning Crash Course: Google provides a practical introduction to machine learning through a series of lessons with video lectures, real-world case studies, and hands-on exercises. The course covers topics such as gradient descent, model evaluation, and deep neural networks.
  5. Gyfted: Gyfted offers a free machine learning test that allows learners to practice their programming skills and focuses on key machine learning concepts. The LinkedIn Machine Learning Assessment focuses on topics such as regression methods, classification methods, and tree-based and ensemble methods.

Discussion Forums and Online Communities

Machine learning is an active field of research and development, with many different groups working on the same problems.

There are many online forums where you can ask questions, share your experiences and learn from others.

Here are some of the most popular ones:

Stack Overflow: This is one of the most popular online communities for programmers. You can use it to get help with your own coding problems, whether you’re a novice or an expert.

Kaggle Discussions: Kaggle is a platform for companies and organizations to post data science competitions, in which teams compete to solve real-world problems by applying machine learning algorithms.

Reddit(r/machinelearning): The subreddit r/machinelearning has 2.7 million members—a community where users can ask questions, post interesting links and get feedback from other users about their machine-learning projects.

Quora(Machine Learning): Quora is an online forum where you can get answers from other users and learn about machine learning concepts. It’s a good place to ask questions, especially ones that are specific to your work.

Wolfram Community: Wolfram is an online community for people who are interested in mathematics, science and technology. It provides discussions on machine learning topics and allows users to read academic papers and research papers about the subject.

Instructor and Influencer

To stay on top of the latest machine learning trends, you should learn from the experts. 

You can do this by following influencers or finding a good instructor in the field—reading their articles and taking their courses.

Having an experienced instructor can help you learn the basics of machine learning more quickly and prevent gaps in your knowledge.

Some machine learning experts who are worth following includes:

Andrew Ng: Andrew Ng is a computer scientist, writer, and investor who specializes in machine learning and AI. He is a professor at Stanford University and co-founder of Coursera and deeplearning.ai. He is considered one of the top experts in the field and has authored several books and research papers on machine learning.

Vladimir Vapnik: Vladimir Vapnik is a renowned scientist in the field of machine learning and one of the main developers of the Vapnik–Chervonenkis theory of statistical learning. He is the co-inventor of the support-vector machine method and support-vector clustering algorithm. He has received numerous awards for his contributions to the field of machine learning.

Hilary Mason: Hilary Mason is the general manager for machine learning at Cloudera and founder at Fast Forward Labs. She is a leading expert in the field of machine learning and has authored several books and research papers on the topic. She is also a frequent speaker at industry conferences and events.

Fei-Fei Li: Fei-Fei Li is an associate professor at Stanford University and the co-director of the Stanford Institute for Human-Centered Artificial Intelligence. She is a leading expert in the field of computer vision and has authored several books and research papers on the topic. She is also a frequent speaker at industry conferences and events.

Alex Champandard: Alex Champandard is a machine learning researcher and founder of the AI and Games website. He is known for his work on using machine learning to create intelligent game characters and has authored several books and research papers on the topic.

Expert's Blogs/Articles to learn ML

In order to get started with a new technology, it’s best to read blogs and articles written by more experienced users.

Here are 5 of the best blogs and articles sites about machine learning, written by expert authors.

These resources can help you get started on your AI machine learning journey—and if they don’t pique your interest in AI at all—well, nothing will!

Sebastian Ruder

Sebastian is a Berlin-based research scientist at Google, specializing in natural language processing (NLP) for under-represented languages.

He holds a PhD in Natural Language Processing and Deep Learning from the Insight Research Centre for Data Analytics, and previously worked as a research scientist at DeepMind and AYLIEN.

He created Ruder.io, a blog highlighting important articles in the field of natural language processing and machine learning—and it’s one that should be on your must-read list for anyone interested in those topics.

Pete Warden’s Blog

Meet the accomplished engineer and CTO of Jetpac Inc, known for his remarkable contributions to the tech world. As an author, he has penned down “The Public Data Handbook” and “The Big Data Glossary” for O’Reilly. Not only that, he is the mastermind behind the development of OpenHeatMap and the Data Science Toolkit, among other noteworthy open source projects.

His blog at Petewarden showcases well-structured, informative, and educational articles, although tailored more towards experienced readers than beginners.

Lilian Weng

Lilian Weng is an accomplished applied AI research manager at OpenAI, and she is also the driving force behind a compelling Machine Learning Blog.

Through her platform ieeexplore.ieee.org, Lilian delves into a wide array of subjects, including Reinforcement Learning, Natural Language Processing (NLP), AutoML, Object Detection, Auto Encoders, Transformers, Meta Learning, and many other cutting-edge topics in the field of machine learning.

Her blog serves as an invaluable resource for enthusiasts and professionals seeking to stay informed about the latest developments in the AI landscape.

Google AI Blog

GoogleAI blog is a great resource for those who want to stay up-to-date on all the latest developments in AI research.

The blog at ai.google blog features articles that cover topics such as machine learning, natural language processing (NLP), computer vision, and more.

The site also offers an extensive list of publications on GCP’s official website, which covers everything from AI ethics to best practices when deploying machine learning models in production environments.

Import AI

Jack Clark, a distinguished individual in the world of data, machine learning, and artificial intelligence, is the visionary founder behind Import AI.

Jack’s platform, Import AI, provides in-depth articles on the latest developments in artificial intelligence and machine learning—but it is geared toward those who are already familiar with these concepts.

Career Opportunities

In the AI Boom, some industries and countries are using artificial intelligence to boost their development.

A year ago, The IBM Global AI Adoption Index 2022 found that the global adoption rate of artificial intelligence grew steadily from 35% in a comparison to 2021, while 44% of organizations are working—giving many machine learning beginners the chance to find good jobs during this AI growth market.

Job roles and industries utilizing machine learning

Some of the most common jobs role for a students with a degree in machine learning include:

Machine Learning Engineer: A machine learning engineer builds, tests and optimizes machine learning algorithms. They work closely with data scientists to create a model that can be used for specific tasks. They also help engineers and other employees apply these models to real-world problems.

Data Scientist: A data scientist is responsible for analyzing large amounts of data in order to uncover patterns and make predictions about future events. They may also use their findings to make decisions based on the data they’ve collected.

AI Consultant: An AI consultant is responsible for helping clients develop and implement their machine learning strategies. They may also help clients understand how to use AI in their businesses.

AI Developers: An AI developer is responsible for creating machine learning models that can be applied to real-world problems. They may also be responsible for training these models using large amounts of data from their business partners.

Computer Vision Engineer: A computer vision engineer designs and builds software that analyzes images or video. This can include creating new ways of scanning products in a warehouse, helping self-driving cars recognize objects around them—and anything else the job description may entail.

Now that we understand just how quickly AI is being adopted and utilized by companies around the world, let’s look at some industries which have been using this technology for years.

Amazon: Amazon uses machine learning to improve its recommendation engine, detect fraudulent transactions and optimize its supply chain. Amazon’s algorithms analyze customer data in order to provide personalized recommendations—and flag fraudsters.

Netflix: Netflix uses machine learning to personalize its recommendations for users, predict which shows will be popular and optimize streaming quality. Netflix’s algorithms analyze user data to provide personalized recommendations and improve the streaming experience.

IBM: IBM uses machine learning to improve its Watson platform, which provides AI-powered solutions for various industries. IBM’s machine learning algorithms analyze data to provide insights and predictions for businesses.

American Express: American Express uses machine learning to detect fraud in real-time, saving millions of dollars in possible losses each year. The company’s fraud detection system analyzes millions of transactions every month to identify suspicious activity.

Facebook: Facebook uses machine learning to power its news feed, facial recognition, and language translation features . The company’s deep neural network algorithms scan billions of posts and photos each day to determine which stories will be featured in users’ news feeds.

Resume and Portfolio Building Tips

In today’s competitive job market, it is essential to have a well-crafted resume and strong portfolio of work when pursuing a career in machine learning.

To help you craft a strong resume and portfolio for machine learning, here are some valuable tips to keep in mind:

  1. Tailor Your Resume for the Job: Customize your resume for each application to match the specific job requirements. Highlight relevant machine learning projects, programming languages, and domain knowledge that align with the position you are applying for.
  2. Showcase Your Machine Learning Projects: Include a separate section in your resume to highlight your machine learning projects. Describe the problem statements, the techniques used, and the outcomes achieved. If possible, provide links to GitHub repositories or online demos to demonstrate your work.
  3. Emphasize Your Technical Skills: Clearly list your technical skills related to machine learning, such as programming languages (Python, R, etc.), libraries (TensorFlow, PyTorch, scikit-learn), and data manipulation tools (NumPy, Pandas). Make sure to mention any certifications or online courses you have completed.
  4. Include Relevant Coursework and Certifications: If you’re a recent graduate or have completed relevant coursework and certifications, include them on your resume. This helps demonstrate your commitment to continuous learning and keeping up with the latest developments in the field.
  5. Highlight Your Academic Achievements: If you have a strong academic background in machine learning or related fields, highlight any honors, awards, or research publications on your resume.
  6. Show Your Problem-Solving Skills: Apart from technical skills, emphasize your problem-solving and critical-thinking abilities. Employers value individuals who can approach complex challenges creatively.
  7. Demonstrate Teamwork and Collaboration: Machine learning projects often involve teamwork. Showcase your ability to collaborate effectively, whether through group projects, hackathons, or open-source contributions.
  8. Create an Online Portfolio: Build a personal website or portfolio that showcases your machine learning projects, code samples, and blog posts on relevant topics. A well-organized online presence can leave a lasting impression on potential employers.
  9. Add Practical Experience: If you have gained practical experience through internships or industry projects, highlight them on your resume. Practical exposure is valuable for demonstrating your ability to apply machine learning techniques in real-world scenarios.
  10. Keep it Concise and Professional: Keep your resume and portfolio concise, focusing on the most relevant and impactful information. Use a professional format, clear headings, and bullet points for easy readability.
  11. Proofread and Seek Feedback: Carefully proofread your resume and portfolio for any errors or typos. Additionally, seek feedback from peers, mentors, or career advisors to ensure your documents are polished and effective.

Networking and Professional Development Opportunities

Taking advantage of the many networking and professional development opportunities available to you is an important part of your job search process—if you’re serious about finding a job with a machine learning degree.

To make sure you can handle the challenges of a job interview or offer, here are some ways to build your network and professional development skills.

  1. Attend Machine Learning Meetups and Events: Look for local machine learning meetups, workshops, and conferences in your area. These events provide excellent opportunities to meet like-minded professionals, learn from experts, and engage in meaningful discussions.
  2. Join Online Machine Learning Communities: Participate in online forums, discussion groups, and social media platforms dedicated to machine learning. Platforms like Reddit’s r/MachineLearning and LinkedIn groups offer avenues for networking and knowledge-sharing with professionals worldwide.
  3. Connect with Machine Learning Experts: Reach out to experts, researchers, and industry leaders on platforms like LinkedIn and Twitter. Networking with experienced individuals can provide valuable insights and potential mentorship opportunities.
  4. Contribute to Open-Source Projects: Engage in open-source machine learning projects on platforms like GitHub. Contributing to such projects not only enhances your skills but also exposes you to collaboration with developers across the globe.
  5. Join Professional Associations: Become a member of machine learning or AI-related professional associations. These associations offer access to industry events, webinars, and publications, enriching your understanding of the field.
  6. Volunteer or Speak at Events: Offer to volunteer at machine learning conferences or workshops, which can open doors to networking opportunities. Additionally, consider sharing your knowledge by presenting at relevant events, showcasing your expertise to a broader audience.
  7. Participate in Hackathons and Challenges: Engage in machine learning hackathons and challenges hosted by companies or organizations. These competitions provide a chance to demonstrate your skills, connect with recruiters, and win prizes or job offers.
  8. Online Webinars and Workshops: Attend webinars and online workshops conducted by machine learning experts and institutions. These events allow you to stay updated on the latest advancements and best practices.
  9. Join Machine Learning Competitions: Participate in machine learning competitions like Kaggle competitions or data science challenges. Collaborating with other participants and discussing solutions fosters networking within the community.
  10. Seek Internship Opportunities: Apply for internships in machine learning-related roles. Internships not only offer practical experience but also allow you to build connections with professionals in the industry.
  11. Continuous Learning: Never stop learning. Stay informed about emerging technologies, research papers, and industry trends of machine learning through reading and online courses.

Further Education and Advanced Courses

To continue to educate yourself, there are a number of advanced courses and education available that can help you expand your knowledge of machine learning. These include

The following list provides a summary of some of the most popular courses to date.

  1. Stanford University: Introduction to Artificial Intelligence (Stanford) is a free online course available on edX that provides an introduction to AI through a series of lectures and problem sets. The course covers machine learning, knowledge representation, reasoning under uncertainty, natural language processing, and reinforcement learning.

  2. Johns Hopkins University’s Advanced Machine Learning Course: Johns Hopkins University offers an online course focused on recent advances in machine learning. The course aims to develop skills for performing research and advancing the state of knowledge in machine learning. Topics covered include nonparametric and parametric learning, supervised and unsupervised learning, graphical models, ensemble methods, and reinforcement learning.

  3. Google’s Advanced Courses : Google for Developers offers advanced courses on machine learning that teach tools and techniques for solving a variety of machine learning problems. The courses cover topics such as decision forests, recommendation systems, clustering, and generative adversarial networks.

  4. Towards Data Science – Advanced AI and Machine Learning Online Courses: Towards Data Science provides a curated list of advanced online courses in AI and machine learning. The list includes courses from reputable sources and covers topics such as AI search algorithms, planning, and optimization techniques.

  5. Georgia Tech’s “Machine Learning” Course on Udacity: This comprehensive course covers supervised and unsupervised learning, randomized optimization techniques, and more.

Industry Trends and Future Prospects

The field of machine learning continues to grow and evolve as researchers and practitioners continue to develop new algorithms and methodologies to address different problems.

Here are some key trends that show where the future of machine intelligence might take us:

  1. Increased Automation: Machines are becoming smarter, and they can now do more tasks without human help. This trend may lead to more and more automation in industries like manufacturing, transportation, and customer service.
  2. AI in Everyday Life: Artificial intelligence is already a part of our daily lives through virtual assistants like Siri and smart home devices. In the future, we might see AI becoming even more common in various applications, making our lives easier and more efficient.
  3. AI in Healthcare: Machine learning is transforming healthcare with its potential to diagnose diseases, analyze medical data, and develop personalized treatment plans. This trend could lead to better healthcare outcomes and improved patient care.
  4. AI Ethics and Bias: As AI becomes more prevalent, there is a growing concern about its ethical use and potential biases. The future will likely see more focus on developing fair and unbiased AI systems.
  5. Big Data and AI: The combination of big data and AI can unlock valuable insights and drive innovation across industries. This trend is expected to continue as businesses realize the power of data-driven decision-making.
  6. AI for Environmental Solutions: Machine learning has the potential to help us address environmental challenges by analyzing data and finding sustainable solutions. This trend may play a crucial role in combating climate change and protecting our planet.
  7. AI Security: As AI becomes more integrated into critical systems, the need for robust AI security measures will increase. The future may see advancements in AI cybersecurity to protect against potential threats.
  8. Edge Computing and AI: Edge computing allows AI processing to happen closer to where data is generated, reducing latency and enabling real-time applications. This trend is likely to see significant growth in various IoT (Internet of Things) applications.
  9. AI in Education: Machine learning can personalize educational experiences for students, adapting to their unique needs and learning styles. This trend may revolutionize the way we approach education and training.
  10. AI and Creativity: AI is now being used in creative fields like music, art, and design. The future may witness AI as a valuable tool for artists and creatives to enhance their work and spark new ideas.

Machine Learning Course at YHills

Now, as you’ve read above, you know that machine learning is one of the most important and relevant trends in technology—it’s essential for us all to get better acquainted with it.

Fortunately, YHills offers a Machine Learning course designed specifically for professionals looking to get started with this highly useful and lucrative skill-set —and you can learn more about what the course entails by clicking here .

With this course, you’ll gain access to a range of content that covers the fundamentals of machine learning, as well as how to use it in real-world applications.

You’ll learn about neural networks, decision trees and more; moreover, you can expect to get practical hands-on experience with building your own models using popular tools like TensorFlow and Kaggle.

FAQs for Machine Learning Couse

Making informed choices is vital for your educational journey. In this section, we address the most common queries, ensuring you have all the information you need to embark on your Machine Learning adventure.
Some of the best machine learning courses include Google’s Machine Learning Crash Course, MyGreatLearning’s tutorials, GeeksforGeeks’ comprehensive tutorial, Toptal’s machine learning tutorial, YHills’ Machine Learning Certification Course and Machine Learning Mastery’s comprehensive guide. Choose the one that aligns with your learning style and objectives.
Anyone with a basic understanding of mathematics and programming can learn machine learning. It’s suitable for computer science professionals, data analysts, engineers, and even beginners looking to enter the field. Start with introductory courses if you’re new to the subject.
Beginners can start with these recommended machine learning books: “Machine Learning: 4 Books in 1” by Daniel Covington and Peter Nettesheim, “The Hundred-Page Machine Learning Book” by Andriy Burkov, “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by AurĂ©lien GĂ©ron, and “Python Machine Learning” by Sebastian Raschka and Vahid Mirjalili. These books cover a wide range of machine learning topics, from Python programming to mathematical foundations and practical applications.
If you’re an absolute beginner, start with online courses like “Machine Learning Certification Course” by Yhills or “Machine Learning for Beginners” on platforms like Coursera or Udemy. You can also explore free resources like Google’s “Machine Learning Crash Course” or Khan Academy’s “Introduction to Machine Learning.”
For beginners interested in Python-based machine learning, “Python Machine Learning” by Sebastian Raschka and Vahid Mirjalili is a top choice. It covers essential concepts and practical applications using Python libraries like scikit-learn and TensorFlow.

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