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Are you interested in Machine Learning, Deep Learning, and Computer Vision? Then, the [2023] Machine Learning and Deep Learning Bootcamp in Python is the course for you! This course offers a thorough understanding of the fundamental concepts of machine learning, deep learning, reinforcement learning, and their practical applications. The course includes hands-on implementation using popular libraries such as SkLearn, Keras, and TensorFlow, among others.
Theoretical Background on Machine Learning
In this course, you will learn the theoretical background on Machine Learning algorithms such as Linear Regression, Logistic Regression, K-Nearest Neighbors Classifier, Naive Bayes Algorithm, Support Vector Machines, Decision Trees, Random Forests, Bagging and Boosting, Clustering Algorithms, and Numerical Optimization. You will understand the concepts of how these algorithms work, their applications in various fields, and the differences between each other.
For instance, Linear Regression is an algorithm that deals with continuous variables and helps in determining the relationship between independent and dependent variables. This algorithm is used to create a model that predicts a target variable's value based on input data features. On the other hand, Clustering Algorithms identify the grouping structures within data, and this helps to discover underlying patterns in data.
You will also learn about deep learning and neural networks, including Feed-Forward Neural Networks, Deep Neural Networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).
Practical Implementation with Sklearn, Keras, and TensorFlow
This course focuses on the practical implementation of various algorithms using well-known Python libraries such as SkLearn, Keras, and TensorFlow. You will learn how to use these libraries to train the machine learning models and work on solving real-world problems. These libraries provide a powerful platform for implementing complex algorithms easily and efficiently.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It simplifies the process of creating deep-learning models. TensorFlow, on the other hand, is an open-source software library used for dataflow and differentiable programming across a range of tasks. SkLearn is a popular machine learning library that provides various tools for data analysis and model selection.
You will learn how to use these libraries to build regression models, classification models, clustering algorithms, and neural networks. You will also learn how to evaluate the performance of your models through metrics such as Mean Squared Error, Receiver Operating Characteristic (ROC) curves, and confusion matrices.
Computer Vision
This course will also cover Computer Vision and its application in Deep Learning. You will learn Image Processing Fundamentals, Self-Driving Cars and Lane Detection, Face Detection with Viola-Jones Algorithm, Histogram of Oriented Gradients (HOG) Algorithm, Convolutional Neural Networks (CNNs) Based Approaches, and Single Shot MultiBox Detector (SSD) Object Detection Algorithm.
Computer Vision is a field of artificial intelligence that enables machines to interpret and understand visual information from the world around them. It has application in various fields such as robotics, healthcare, transportation, manufacturing, and more. Computer vision is already being used in self-driving cars, surveillance, facial recognition, medical diagnosis, and many other applications.
Why take the [2023] Machine Learning and Deep Learning Bootcamp in Python?
The course is designed for learners who are interested in machine learning, deep learning, and computer vision. It is appropriate for people who are new to these concepts as well as those who want to explore them in more detail. The course has a rating aggregate of 4.55508 with 1156 course reviews quantity, indicating that previous learners have found the course helpful and informative.
The course is practical oriented, making it easy to understand and work on real-world problems with the help of SkLearn, Keras, and TensorFlow libraries. You will get lifetime access to 150+ lectures plus slides and source codes for the lectures. If you are not satisfied, the course comes with a 30-day money-back guarantee. You will also receive a certificate of completion at the end of the course, which can be added to your LinkedIn profile or CV to showcase new skills to prospective employers.
Wrapping Up
If you're interested in deepening your understanding of topics such as Machine Learning, Deep Learning, Computer Vision, and Reinforcement Learning and how to use popular libraries such as SkLearn, Keras, and TensorFlow for practical implementation, the [2023] Machine Learning and Deep Learning Bootcamp in Python could be an excellent option for you. This course covers various algorithms and their theoretical backgrounds in a straightforward and practical way, making it easy to grasp new concepts.
