Interested in the field of Machine Learning, Deep Learning and Artificial Intelligence? Then this course is for you!This course has been designed by a software engineer. I hope with my experience and knowledge I did gain throughout years, I can share my knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.I will walk you step-by-step into the Machine Learning, Artificial Intelligence and Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same time, we dive deep into Machine Learning, Deep Learning and Artificial Intelligence . Throughout the brand new version of the course we cover tons of tools and technologies including:Deep Learning.Google ColabAnacondaJupiter NotebookArtificial Intelligent In Healthcare.Artificial Neural Network.Neuron.Activation Function.Keras.Pandas.Seaborn.Feature scaling.Matplotlib.Generating a DNA Sequence.Data Pre-processing.Sigmoid Function.Tanh Function.ReLU Function.Leaky Relu Function.Exponential Linear Unit Function.Swish function.Markov Models.K-Nearest Neighbors Algorithms (KNN).Support Vector Machines (SVM).Importing library and data.Deep feedforward networks.Analysing Data.Exploratory Analysis.Handling Missing Data And Anomalies in Python.Data standardization.Temporal Features.Geolocation Features.Data Scaling.Data Visualization.
What you'll learn
understand the fundamentals of machine learning, deep learning, and AI
implement artificial neural networks and deep feedforward networks
engage with tools such as Google Colab, Anaconda, and Jupyter Notebook
perform data pre-processing, visualization, and exploratory analysis
apply algorithms like K-Nearest Neighbors and Support Vector Machines in projects related to healthcare