Introduction
Introduction of the Course
Introduction to Machine Learning and Deep Learning
Introduction to Google Colab
Python Crash Course
Data Preprocessing
Supervised Machine Learning
Regression Analysis
Logistic Regression
K-Nearest Neighbor (KNN)
Bayes Theorem and Naive Bayes Classifier
Support Vector Machine (SVM)
Decision Trees
Random Forest
Boosting Methods in Machine Learning
Introduction to Neural Networks and Deep Learning
Activation Functions
Loss Functions
Back Propagation
Neural Networks for Regression Analysis
Neural Networks for Classification
Dropout Regularization and Batch Normalization
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
Autoencoders
Generative Adversarial Network (GAN)
Unsupervised Machine Learning
K-Means Clustering
Hierarchical Clustering
Density Based Spatial Clustering Of Applications With Noise (DBSCAN)
Gaussian Mixture Model (GMM) Clustering
Principal Component Analysis (PCA)
What you’ll learn
Theory, Maths and Implementation of machine learning and deep learning algorithms.
Regression Analysis.
Classification Models used in classical Machine Learning such as Logistic Regression, KNN, Support Vector Machines, Decision Trees, Random Forest, and Boosting Methods in Machine Learning.
Build Artificial Neural Networks and use them for Regression and Classification Problems.
Using GPU with Deep Learning Models.
Convolutional Neural Networks
Transfer Learning
Recurrent Neural Networks
Time series forecasting and classification.
Autoencoders
Generative Adversarial Networks
Python from scratch
Numpy, Matplotlib, seaborn, Pandas, Pytorch, scikit-learn and other python libraries.
More than 80 projects solved with Machine Learning and Deep Learning models.