Hello there, welcome to the “Python Programming: Machine Learning, Deep Learning | Python” course. This course is designed to teach you Python, machine learning, python programming, django, ethical hacking, data analysis, python for beginners, machine learning python, python bootcamp, Artificial Intelligence, OOP, and Python Projects. Everything that you need to know about Python programming and machine learning in one place.
What is Python Programming?
Before diving deep into machine learning, let's start with the Python programming language. Python is a popular high-level programming language used by developers all over the world. It was created in the late 1980s by Guido van Rossum, and it is currently one of the most popular programming languages.
Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. Python is often praised for its simplicity and ease of use, especially when compared to other programming languages like C++ and Java.
Python's syntax is simple and easy to read, which makes it the perfect language for beginners. Yet, it's also capable of handling complex projects, which is why it's one of the most widely-used programming languages in the world.
What is Machine Learning?
Machine learning is an application of artificial intelligence that enables systems to learn and improve from data without being explicitly programmed. It is a subset of artificial intelligence that focuses on giving computer systems the ability to automatically learn and improve from experience, without being explicitly programmed.
Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work.
Machine learning involves the use of algorithms that can learn from and make predictions on data. The algorithms iteratively learn from the data, allowing the machine to improve its predictions over time. Machine learning algorithms can identify patterns, classify data, and make predictions, among other things.
What is Deep Learning?
Deep learning is a subset of machine learning that focuses on neural networks with many layers. These networks can learn and make predictions on complex data, such as images and speech. Deep learning models are designed to mimic the way the human brain processes information, allowing them to learn and improve over time.
This course will take you on a complete hands-on deep learning tutorial with Python. You will learn everything about Machine Learning Python, go from zero to hero in Python 3. Our Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels.
What will you learn in this course?
This course has suitable for everybody who interested in Machine Learning and Deep Learning concepts in Data Science.
First of all, in this course, you will learn some fundamental stuff of Python and the Numpy library. These are our first steps in our Deep Learning journey. After that, you will learn about Machine Learning Python history, the machine learning concepts, the machine learning a-z workflow, models and algorithms, and what is neural network concept. You will learn about the Artificial Neural network, which will take you to the Keras world then you will exit to the TensorFlow world. You will also understand the Convolutional Neural Network concept, which is followed by Recurrent Neural Network and LTSM. You will take a look at them and then trip to the Transfer Learning concept. In the end, you will arrive at projects in Python Bootcamp, where you will make some interesting machine learning models with the information you've learned along your journey.
How is Machine Learning used?
Machine learning a-z is being applied to virtually every field today. It is used in medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions.
Do you need to know how to code to learn Machine Learning?
It's possible to use machine learning data science without coding, but building new systems generally requires code. Developing machine learning systems involves writing some Python code to train, tune, and deploy your models. It's hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform “feature engineering” to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from it.
What is the best language for machine learning?
Python is the most used language in machine learning using python. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It's useful to have a development environment such as Python so that you don't need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a complete machine learning framework for C# called ML.NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets.
What are the different types of machine learning?
Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email
