Free Download Machine learning Basics and Advanced Topics Using Python
Published 8/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 47m | Size: 295 MB
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)
Who this course is for
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