Machine Learning & Deep Learning Masterclass in One Semester

Practical Oriented Explanations by solving more than 80 projects with NumPy, Scikit-learn, Pandas, Matplotlib, PyTorch.

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.