Download Complete Road Map for ML with Practical Real World Projects

What you would learn in Complete Road Map for ML with Practical Real World Projects course?

Two experienced Data Scientists developed this course to ensure to share their knowledge and assist you in understanding complicated theories, algorithms, and code libraries in a straightforward method.

A road map links several of the most important ideas used in machine learning. It also outlines the best way to master them and the tools you can use to implement these tasks.

Below are a few Applications of Machine Learning in a Practical Real World

1. Machine learning can aid in diagnosing diseases. Many doctors use chatbots that can recognize speech to identify patterns in the symptoms—actual examples of medical diagnosis: Assisting in formulating an assessment or recommending an option for treatment.

2. Google Maps uses machine learning when combined with various sources of data, such as aggregate data on location and historical traffic patterns, local government information, and live user feedback from users to determine the likelihood of traffic.

Python is leading the pack, having 57 percent of data scientists and software developers working with machine learning using it, and 33% of them recommending it to develop. Therefore, in this course, you'll also master the Basics of Python to improve the most current state of the Art methods of Deep Learning Models.

Four sections of this course provide a complete understanding of all aspects of Artificial Intelligence, such as Python, Machine Learning, Deep Learning, and Time Series Analysis. This course is enjoyable and thrilling, yet simultaneously we dive into Machine Learning. The course is organized according to the following format:

PHP-

Data Structures, List, Tuples, Dictionary, Libraries, Functions, Operators, etc

Processing and Cleaning of Data

Machine Learning -

Regression Simple Linear Regression SVR Decision Tree, Random Forest,

Clustering K-Means, Hierarchical & Hierarchical Algorithms

Classification Logistic Regression Kernel SVM Naive Bayes, Decision Tree Classification, Random Forest Classification

Natural Language Processing: Bag-of-words algorithm and model for NLP

DEEPER LEARNING -

Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long short term Memory, Vgg16, Transfer learning, Web Based Flask Application.

Additionally, this course is filled with practical exercises built on real-world examples. Therefore, you will not only be able to understand the concepts. However, you'll also be able to practice making the models of your choice.

Course Content:

• Learn about Python, Machine learning, Deep learning, and Time-series principles. Implement real World Projects with Proof Of Concept
• The course comprises more than 25 hours of video content and downloadable documents for each video.
• Data Scientists must know the basics of ML.
• Five Different Practical Data Science projects with I Python Notebooks

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