Get Started In Machine Learning

Hello there! This page is designed to be your comprehensive resource to get started in machine learning. Here, you’ll find a curated list of topics and skills essential for mastering the field. We will be adding new topics progressively as we complete them.

To make your learning experience more flexible, many of the skills and project-related topics are available for free. Core topics on our website are marked as premium, priced reasonably to give you the option to choose specific subjects of interest without committing to a large membership fee. For those eager to explore multiple areas, consider our bundle options for greater savings.

Each topic has been meticulously researched and developed, with an investment of 40 to 100 hours per tutorial to ensure comprehensive coverage. You will gain a solid understanding of basic concepts and their real-world applications. Additionally, paying members have the opportunity to seek clarification on any doubts they may have.

Many learners face the challenge of finding a single, comprehensive resource for machine learning. It often means juggling between different books and resources to piece together the knowledge needed for even basic concepts. That’s exactly why this platform “Neuraldemy” was created. It aims to provide everything you need to learn machine learning from start to finish, covering all the key topics in a structured way.

We’re constantly working on adding new content, so while not everything may be available just yet, we’re committed to expanding our offerings as quickly as possible. Our goal is to make learning machine learning as straightforward and complete as it can be, solving the problem of fragmented resources and helping you get the knowledge you need in one place.

Let’s get started!

How To Enroll In This Course

It may take you anywhere between 6 to 12 months to finish the content of this website and learn everything. To enroll in this course, follow these simple steps:

Step 1

Create an Account

Step 2

Choose a Plan

Select a plan based on your needs and preferences to get access to all premium course materials and features.

Step 3

Navigate Through Tutorials

Explore the tutorials below in a sequential manner and ask doubts in forums

Step 4

Finish Tutorials for Certification

Complete the tutorials to earn your certification.

Essential Skills for Machine Learning

To effectively tackle data science and machine learning challenges, having a solid foundation in certain skills is crucial. Here are the key skills you need to Learn:

Python

Markdown

NumPy

Pandas

Matplotlib

Must Have Skill

Free

Python

Learn Python, the fundamental programming language from the best. Used extensively in data science and machine learning. It provides the tools and libraries needed to handle data, create algorithms, and build models. Taught by Dr. Chuck. We don’t have a python course yet.

Understand Markdown, a lightweight markup language used for formatting text. It is essential for documenting code, creating readme files, and presenting research findings in a clear, readable format.

Explore NumPy, a fundamental library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

Dive into Pandas, a powerful data manipulation and analysis library for Python. It allows for easy handling of structured data and provides data frames and series for efficient data analysis and manipulation.

Master Matplotlib, a comprehensive library for creating static, animated, and interactive visualizations in Python. It is crucial for plotting data, creating graphs, and understanding the results of your data analysis and machine learning models.

Learn about the OS module in Python, which provides a way of using operating system-dependent functionality like reading or writing to the file system, managing processes, and handling directories.

Discover the Pickle module in Python, which is used for serializing and deserializing Python objects. It’s useful for saving objects to files and retrieving them later.

Pickle Module

Essential

Free

How to Install Miniconda

Find out how to install Miniconda, a minimal installer for the Conda package manager, which is useful for managing Python environments and packages efficiently.

Miniconda

Mathematical Concepts

Many people will tell you that you don’t need to know mathematics to get started in machine learning or data science, but that’s not the case. While working with problems, you might not need to know every detail, but if you want to solve and understand a problem in the best way possible, you must know the mathematical concepts behind these algorithms. However, mathematical concepts and real applications are different; the problems you face in learning theory differ entirely from what you face in practice. Without understanding the theory behind these black boxes, you may encounter many challenges. That’s why we have created all the posts in a manner that allows you to grasp both the mathematical and practical concepts simultaneously. No matter what you do, understanding mathematical concepts is crucial to knowing which algorithm to use, when to use it, and on what kind of problems. Your perspective will change after you learn the fundamentals behind algorithms. We have created detailed notes on topics that are essential before starting with machine learning. You can purchase these notes and ask questions in community forums. These topics are important to know.

Linear Algebra

Probability

Statistics

Calculus

Optimization

Essential

Premium

Linear Algebra

Master the concepts of linear algebra essential for understanding machine learning algorithms.

Probability & Statistics

Get in-depth knowledge of probability and statistics to strengthen your foundation in data science.

If you are already familiar with calculus, detailed notes are also available in one of our tutorials on SGD.

Calculus

Classical Machine Learning Topics

Explore the foundational algorithms that were widely used before the advent of neural networks. This section provides a comprehensive overview of each algorithm, progressing towards deep learning. It is crucial to study these algorithms sequentially and thoroughly to achieve the best results. As new topics are added, they will be included here. Additionally, practicing problems related to each model is recommended before moving on to the next.

Pandas

NumPy

Matplotlib

Scikit-Learn

Seaborn

Algorithms

Lesson 1

Free

What Is Machine Learning? Understand The Important Basic Concepts Related To ML

Get an introduction to the fundamental concepts of machine learning, including key terms and basic principles that underpin the field.

Intelligence, Knowledge, Data, Information, AGI, Superintelligence And Responsible AI

Explore advanced topics related to artificial intelligence, including the distinctions between intelligence, knowledge, data, and information, and the implications of AGI, superintelligence, and responsible AI

Extra Topic: AI & Jobs: Understanding How Things May Actually Happen

Examine the potential impact of AI on the job market, including how AI may influence employment trends and job roles in the future.

Singular Value Decomposition: Concepts And Applications

Learn about Singular Value Decomposition (SVD), a mathematical technique used in data analysis and dimensionality reduction, and its practical applications.

SVD

Lesson 5

Free

Mathematics For Machine Learning: Mathematical Intuition Basics

Learn how machine learning works behind the scene. Build intuition for upcoming algorithms

Linear Regression: Concept And Application

Understand the principles of linear regression, a fundamental technique used to model relationships between variables and predict outcomes.

Linear Regression

Lesson 7

Free

A Beginner’s Guide to Data Preprocessing In ML

Discover the basics of data preprocessing, including techniques for cleaning and preparing data to improve the performance of machine learning models.

Principal Components Analysis: Concepts And Application

Explore Principal Components Analysis (PCA), a dimensionality reduction method used to simplify data while preserving its variance.

PCA

Lesson 9

Premium

Linear Discriminant Analysis: Concepts And Application

Learn about Linear Discriminant Analysis (LDA), a technique used for dimensionality reduction and classification by finding linear combinations of features that best separate classes.

LDA

Lesson 10

Premium

Naive Bayes: Concept And Application

Understand the Naive Bayes algorithm, a probabilistic classifier based on Bayes’ theorem with strong independence assumptions, and its applications in classification tasks

Naive Bayes

Lesson 11

Premium

Logistic Regression: Concept And Application

Study logistic regression, a statistical method for binary classification that models the probability of a class or event.

Logistic Regression

Lesson 12

Premium

Support Vector Machine: Concepts And Applications

Explore Support Vector Machines (SVM), a powerful classification technique that finds the optimal hyperplane to separate data into distinct classes.

SVM

Lesson 13

Premium

Decision Trees: Concept And Application

Learn about decision trees, a versatile machine learning model that splits data into subsets based on feature values to make predictions.

Decision Trees

Lesson 14

Premium

Stochastic Gradient Descent: Concept And Application

Discover Stochastic Gradient Descent (SGD), an optimization algorithm used to minimize loss functions and train machine learning models efficiently.

SGD

Lesson 15

Premium

Random Forests & Ensemble Learning: Concept And Application

Explore Random Forests, an ensemble learning method that combines multiple decision trees to improve accuracy and robustness.

Random Forests

Lesson 16

Free

Probability Calibration in Machine Learning

Learn about probability calibration techniques that adjust model predictions to better reflect true probabilities.

Probability Calibration

Lesson 17

Premium

Nearest Neighbors: Concept And Application

Understand the k-Nearest Neighbors (k-NN) algorithm, a simple yet effective method for classification and regression based on proximity to labeled examples.

Nearest Neighbors

Lesson 18

Premium

Clustering and Outlier Detection

Study clustering techniques for grouping similar data points and methods for detecting anomalies or outliers in datasets.

Clustering

Lesson 19

Free

Gaussian Mixture Models

Learn about Gaussian Mixture Models (GMMs), a probabilistic model for representing data with a mixture of multiple Gaussian distributions, used for clustering and density estimation.

GMM

Deep Learning & Modern Machine Learning

Dive into the cutting-edge techniques of deep learning that have revolutionized machine learning. This section covers modern algorithms and frameworks that are at the forefront of AI development. Each topic builds on the foundational knowledge from classical machine learning, providing a pathway to understanding and implementing advanced models. Ensure you are familiar with each concept and practice implementing these models to grasp their applications fully.

ANN

CNN

TensorFlow

Keras

Transformers

NLP

Autoencoders

GANs

Lesson 20

Premium

Optimizers For Deep Learning

Explore various optimization techniques used to improve the performance and efficiency of deep learning models.

Test Of Consciousness For AI

A research work by Amritesh Kumar to test consciousness in AI.

TensorFlow Simplified Guide For Beginners

A beginner-friendly guide to TensorFlow, covering the essentials to help you get started with this powerful deep learning framework.

Deep Learning: Introduction To Artificial Neural Networks (ANNs)

Understand the fundamentals of Artificial Neural Networks (ANNs) and their role in deep learning, including key concepts and applications.

Convolutional Neural Networks (CNNs): Concept And Application

Learn about Convolutional Neural Networks (CNNs), their architecture, and how they are applied in image processing and computer vision tasks.

ResNet And DenseNet Implementation In Depth

Delve into advanced topics with detailed implementations of ResNet and DenseNet architectures, exploring their benefits and applications.

Autoencoders: Fundamentals of Encoders and Decoders Using Neural Nets

Get to grips with Autoencoders, a type of neural network used for unsupervised learning tasks, including data compression and noise reduction.

Natural Language Processing (NLP)

Explore the exciting field of Natural Language Processing (NLP) and learn how to apply deep learning techniques to language-related tasks. This topic is coming soon.

Hands-On Projects

Engage in hands-on projects to solidify your understanding of machine learning and data science concepts. These projects will help you apply what you’ve learned in a practical context, enhancing your problem-solving skills and preparing you for real-world challenges. Each project is designed to be comprehensive and provides a valuable learning experience. Dive in and start building your portfolio with these practical exercises!

Data Analysis

Model Deployment

Predictive Analytics

Natural Language Processing

Computer Vision

Deep Learning Models

Feature Engineering

Portfolio

Project 1

Beginner

Fashion MNIST Classification Using ANN

Explore the classification of fashion items using Artificial Neural Networks (ANNs) with the Fashion MNIST dataset. This project provides an introduction to neural network architectures and their applications in image classification.