Best Machine Learning Courses for Beginners
Top 3 courses (Free, no registration)
Whether you're a student or a working professional, this guide will help you find the perfect course to jumpstart your career in Machine Learning. From the fundamentals to advanced techniques, these courses cover everything you need to know to succeed in this exciting field. So don't wait any longer, start your journey today and become an expert in Machine Learning!
- "Zero to Hero" by Laurence Moroney
- "NLP" by Laurence Moroney
- "Neural Networks" and DL by Geoffrey Hinton
1. "Zero to Hero" by Laurence Moroney
"Machine Learning Zero to Hero" by Laurence Moroney is a course designed for developers who are new to machine learning and want to become experts in the field. It designed for individuals with little to no prior experience in Python programming.
In this course, Laurence teaches you about machine learning and computer vision in a quick and easy way. He covers topics that might take a whole month to understand, but he explains them in just 5 minutes. You will learn how machine learning programming is different from traditional programming. Also, you will learn how to make a basic neural network and build an image classifier using TensorFlow.
Part 1
Traditional programming is the traditional way of writing code to perform specific tasks. It involves writing code with a set of rules and instructions that the computer follows to complete a task.
Machine learning, on the other hand, is a type of artificial intelligence that allows the computer to learn from data and make predictions or decisions without being explicitly programmed to do so. Instead of writing code for each individual task, the computer is fed with data and uses algorithms to identify patterns and make decisions.
The fit method is a method used in machine learning to train a model on a given dataset. The method takes in the data, fits the model to the data, and returns a model that is trained and can be used to make predictions. The fit method helps the model learn the underlying relationships in the data and improves its accuracy in making predictions.
Part 1 - Intro to Machine Learning
Part 2
Data is the information used by machine learning algorithms to learn patterns and make predictions.
The Fashion MNIST dataset is a collection of 70,000 28x28 grayscale images of clothing items, such as shirts, pants, shoes, and accessories. The dataset is commonly used for training and testing machine learning models in the field of computer vision.
The keras library provides a convenient way to load the MNIST data and use it in our models.
The neural network design refers to the architecture of the network, including the number of layers, the type of layers, and the number of neurons in each layer.
Part 2 - Basic Computer Vision with ML
Part 3
Convolutional Neural Networks (CNNs) are a type of deep learning neural network widely used for computer vision tasks such as image classification, object detection, and image segmentation.
They are called "convolutional" because they perform mathematical operations called "convolutions" on the input data (images), which process the data and extract features from it. These features are then fed into multiple layers of the network, where they are processed and combined to make a final prediction.
Part 3 - Introducing convolutional neural networks
Part 4
An overview of building a simple image classifier with TensorFlow:
- Prepare the data: Get a dataset of labeled images and split it into training, validation, and test sets.
- Preprocess the data: Normalize the pixel values and possibly resize the images to a uniform size.
- Compile the model: Choose a loss function, optimizer, and metrics to compile the model.
- Train the model: Train the model on the training data.
- Evaluate the model: After training, evaluate the model on the test data to measure its performance.
- Make predictions: Use the trained model to make predictions on new, unseen images.
Part 4 - Build an image classifier
2. "NLP" by Laurence Moroney
Natural Language Processing with Laurence Moroney
This course will cover the process of converting text into a numerical representation, known as text vectorization, and using this representation to perform sentiment analysis.
Sentiment analysis is the task of determining the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral.
Natural Language Processing Course - Playlist
You will learn how to represent words in a way that a computer can process them, with the goal of training a neural network to understand their meaning later on.
Part 1 - Natural Language Processing - Tokenization
You will convert sentences into sequences of numbers and use preprocessing techniques to prepare them for training a neural network.
Part 2 - Sequencing - Turning sentences into data
Training a model using a tokenizer involves converting text data into numerical representations that can be used as input for a machine learning model.
A tokenizer is a tool that is used to preprocess text data by dividing it into smaller parts, called tokens, which can then be converted into numerical values.
Part 3 - Training a model to recognize sentiment in text
Recurrent Neural Networks (RNNs) are a type of deep learning neural network that are specifically designed to process sequential data, such as time series data, text, or speech.
Part 4 - ML with Recurrent Neural Networks
Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network that is specifically designed to handle the problem of vanishing gradients in traditional RNNs and is widely used in the field of Natural Language Processing (NLP).
Part 5 - Long Short-Term Memory for NLP
Training an AI to create poetry involves using machine learning techniques, such as deep learning with recurrent neural networks, to train a model on a dataset of poems, with the goal of generating new, original poetry.
Part 6 - Training an AI to create poetry
3. "Neural Networks and DL" by Geoffrey Hinton
Neural Networks and Deep Learning - Geoffrey Hintons - Coursera - 2012 (Full course)
An exciting opportunity to learn from one of the pioneers of the deep learning revolution. Geoffrey Hinton, who won a Turing Award.
This is a unique opportunity to learn from one of the fathers of deep learning and gain insight into the latest advancements in the field.
It's a great course if you're looking to gain a comprehensive understanding of neural networks and deep learning.
It covers the basics, like feedforward networks, activation functions, and backpropagation, and also dives into more advanced topics like deep belief networks, convolutional neural networks, and recurrent neural networks. The course is well-structured and provides hands-on experience, so you'll be able to apply what you learn immediately.
I highly recommend it if you're looking to expand your knowledge in the field of deep learning!
Opportunity to learn about machine learning from one of the fathers of the deep learning revolution who won a Turing Award.
Summary
What sets these machine learning courses apart is the expertise of the instructors.
They are highly knowledgeable about the material and have the ability to explain it in a clear and concise manner, making the learning experience easier and more enjoyable for beginners.