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This free course covers the fundamentals of regression analysis, including linear regression, logistic regression, and other advanced techniques. It also provides hands-on coding experience in Python.

This course explores various ensemble techniques, including bagging, boosting, and stacking, to improve the performance of your machine learning models.

Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing. The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using TensorFlow 2 (the world's most popular library for deep learning, and built by Google) and Hugging Face. We shall start by understanding how to build very simple models (like Linear regression models for car price prediction, text classifiers for movie reviews, binary classifiers for malaria prediction) using TensorFlow and Hugging Face transformers, to more advanced models (like object detection models with YOLO, lyrics generator model with GPT2 and Image generation with GANs)After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep-learning solutions that big tech companies encounter.You will learn:The Basics of TensorFlow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision Transformers Evaluation of Cla

The fields of Artificial Intelligence and Machine Learning are considered the most relevant areas in Information Technology. They are responsible for using intelligent algorithms to build software and hardware that simulate human capabilities. The job market for Machine Learning is on the rise in various parts of the world, and the trend is for professionals in this field to be in even higher demand. In fact, some studies suggest that knowledge in this area will soon become a prerequisite for IT professionals.To guide you into this field, this course provides both theoretical and practical insights into the latest Artificial Intelligence techniques. This course is considered comprehensive because it covers everything from the basics to the most advanced techniques. By the end, you will have all the necessary tools to develop Artificial Intelligence solutions applicable to everyday business problems. The content is divided into seven parts: search algorithms, optimization algorithms, fuzzy logic, machine learning, neural networks and deep learning, natural language processing, and computer vision. You will learn the basic intuition of each of these topics and implement practical examples step by step. Below are some of the projects/topics that will be covered:Finding optimal routes on city maps using greedy search and A* (star) search algorithms Selection of the cheapest airline tickets and profit maximization using the following algorithms: hill climb, simulated annealing, and genetic algorithms Prediction of the tip you would give to a restaurant using fuzzy logic Classification using algorithms such as Naïve Bayes, decision trees, rules, k-NN, logistic regression, and neural networks Prediction of house prices using linear regression Clustering bank data using k-means algorithm Generation of association rules with A

You’ve just stumbled upon the most complete, in-depth Neural Networks for Regression course online.Whether you want to:- build the skills you need to get your first data science job- move to a more senior software developer position- become a computer scientist mastering in data science- or just learn Neural Networks to be able to create your own projects quickly....this complete Neural Networks for Regression Masterclass is the course you need to do all of this, and more.This course is designed to give you the Neural Network skills you need to become a data science expert. By the end of the course, you will understand the Multilayer Perceptron Neural Networks for Regression method extremely well and be able to apply them in your own data science projects and be productive as a computer scientist and developer.What makes this course a bestseller?Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Neural Networks for Regression course. It’s designed with simplicity and seamless progression in mind through its content.This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the Multilayer Perceptron (MLPs) technique. It's a one-stop shop to learn Multilayer Networks. If you want to go beyond the core content you can do so at any time.What if I have questions?As if this course wasn’t complete enough, I

Learn how to use Numpy and Pandas for Data Analysis. This will cover all basic concepts of Numpy and Pandas that are useful in data analysis.Learn to create impactful visualizations using Matplotlib and Seaborn. Creating impactful visualizations is a crucial step in developing a better understanding about your data.This course covers all Data Preprocessing steps like working with missing values, Feature Encoding and Feature Scaling.Learn about different Machine Learning Models like Random Forest, Decision Trees, KNN, SVM, Linear Regression, Logistic regression etc... All the video sessions will first discuss the basic theory concept behind these algorithms followed by the practical implementation.Learn to how to choose the best hyper parameters for your Machine Learning Model using Grid Search CV. Choosing the best hyper parameters is an important step in increasing the accuracy of your Machine Learning Model.You will learn to build a complete Machine Learning Pipeline from Data collection to Data Preprocessing to Model Building. ML Pipeline is an important concept that is extensively used while building large scale ML projects.This course has two projects at the end that will be built using all concepts taught in this course. The first project is about Diabetes Prediction using a classification machine learning algorithm and second is about prediciting the insurance premium using a regression machine learning algorithm.

Data science is the field that encompasses the various techniques and methods used to extract insights and knowledge from data. Machine learning (ML) and deep learning (DL) are both subsets of data science, and they are often used together to analyze and understand data.In data science, ML algorithms are often used to build predictive models that can make predictions based on historical data. These models can be used for tasks such as classification, regression, and clustering. ML algorithms include linear regression, decision trees, and k-means.DL, on the other hand, is a subset of ML that is based on artificial neural networks with multiple layers, which allows the system to learn and improve through experience. DL is particularly well-suited for tasks such as image recognition, speech recognition, and natural language processing. DL algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs).In a data science project, DL models are often used in combination with other techniques such as feature engineering, data cleaning, and visualization, to extract insights and knowledge from data. For instance, DL models can be used to automatically extract features from images, and then these features can be used in a traditional ML model.In summary, Data science is the field that encompasses various techniques and methods to extract insights and knowledge from data, ML and DL are subsets of data science that are used to analyze and understand data, ML is used to build predictive models and DL is used to model complex patterns and relationships in data. Both ML and DL are often used together in data science projects to extract insights and knowledge from data.IN THIS COURSE YOU WILL LEARN ABOUT :Life Cycle of a Data Science Project.Python libraries like Pandas and Numpy used extensively in Data Science.Matplotlib and Seaborn for Data Visualizatio