Machine Learning: Basics And Advanced Topics Using Python

Machine learning

What you'll learn

Introduction, Machine Learning (ML) Definition, Types of learning Techniques: Supervised Learning, Un-supervised Learning, Reinforcement Learning

Dataset Analysis, Preprocessing Techniques, Framework of ML Development for a Project in Business

Explaining supervised ML algorithms such as Linear Regression, Logisitc Regression, Support vector Machines, Decision Trees, Naive bayes, KNN, Random Forrest

Explaining unsupervised ML algorithms such as Hierarchical Clustering, DBSCAN, PCA

Explaining Reinforcement Learning algorithms such as Q-learning, Deep Q-Network (DQN)

Implementing ML algorithms using Python

Requirements

Python

Description

Introduction to Machine Learning •Overview•What is Machine Learning (ML)?•Workflow of Machine Learning Model•How to Obtain Best Results with a ML Model?•Types of Tasks Using Machine Learning Models•Terminologies•Responsibilities of Job Positions in Machine Learning•Some Applications of Machine Learning•Some Forecasting Applications Used in Business•Prediction of Time Series Data•Nature/behavior of Time series data may be include:•Other Applications Used in Business Using Machine Learning•Challenges of Machine Learning•Some Issues in Machine Learning•Hugging Face•Python Tools & Python LibrariesLearning Techniques •What is Difference between Traditional Programming & Machine Learning?•Machine learning in Practice•Machine learning FrameworksTypes of Learning•Supervised Learning•Unsupervised Learning•Reinforcement LearningML Tasks & Applications•Regression•Classification•Clustering•Dimensionality ReductionExample on Supervised Learning in Learning PhaseExample on Supervised Learning in Prediction PhaseML Learning Algorithms/TechniquesAdvs. & Disadvs. of ML AlgorithmsMachine learning (ML) for ClassificationMachine Learning (ML) for RegressionMachine Learning ProcessOverall Process of Building a ML ModelDataset Analysis • Data Overview• Dataset Workloads• Typical dataset composition• Sources of Dataset• Data Types• Framework for a Business Problem• Data Collection & labeling dataData Evaluation•Format of Data•Examine Data Types•Describe Dataset with its Statistics•VisualizationData Processing•Data cleansing•Feature EngineeringData Conversion•Data Encoding•Data scalingData ImbalancedSMOTESupervised Learning AlgorithmsLinear Regression (LR)Logistics RegressionSupport Vector Machine (SVM)Decision Tree (DT)Naïve Bayes (NB)K-Nearest Neighbor (KNN)Ensemble Learning: Bagging Techniques e.g. Random Forest (RF)Ensemble Learning: Boosting Techniques e.g. Gradient Boosting Decision Trees (GBDT)Unsupervised Learning AlgorithmsK-meansHierarchical ClusteringDBSCANPrinciple Component Analysis (PCA)Reinforcement LearningQ-LearningDeep Q-Network (DQN)

Overview

Section 1: Introduction

Lecture 1 1- Aims and Contents of Course

Lecture 2 2- Overview

Lecture 3 3- Difinition and Workflow of Machine Learning

Lecture 4 4- Responsibilities of Job Positions in Machine Learning

Lecture 5 5- Applications of Machine Learning

Lecture 6 6- Challenges and Issues in Machine Learning

for all