Machine Learning Full Course

About Course

🎯 Course Objective

To provide a complete, hands-on introduction to machine learning using Python and R, covering theoretical concepts, practical implementation, and real-world projects.

🧩 What You Will Learn

Core Concepts of Machine Learning
Data Preprocessing for clean and usable data
Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, etc.
Unsupervised Learning: K-Means Clustering, Hierarchical Clustering
Model Evaluation & Validation: Cross-validation, confusion matrix, bias-variance trade-off
Reinforcement Learning basics
Natural Language Processing (NLP)
Deep Learning (Introductory level using ANN, CNN)
Model Deployment & Real-World Use Cases

🛠️ Technologies & Tools Used

Python & R Programming
Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
TensorFlow/Keras (Intro to deep learning)
Jupyter Notebook & RStudio
Google Colab (for cloud-based hands-on coding)

📁 Course Modules

Introduction to Machine Learning
Data Preprocessing
Regression Models
Classification Models
Clustering Algorithms
Dimensionality Reduction
Association Rule Learning
Reinforcement Learning
NLP with Text Data
Artificial Neural Networks (ANN)
Convolutional Neural Networks (CNN)
Model Selection & Tuning
End-to-End Projects & Deployment

🧪 Real-World Projects

House Price Prediction
Customer Segmentation
Spam Email Classifier
Stock Market Trend Analysis
Image Classification

👨‍🏫 Who This Course Is For

Beginners in data science and machine learning
Students and professionals looking to shift into AI/ML
Data analysts, engineers, or developers upgrading skills
Anyone curious about how machines learn from data.

Course Content

Machine Learning Introduction

INTRODUCTION

10:16:47

Supervised Learning
Learning from labeled data (input-output pairs) Algorithms: Linear Regression Logistic Regression Decision Trees Support Vector Machines (SVM) k-Nearest Neighbors (KNN)

Unsupervised Learning
earning patterns from unlabeled data Algorithms: K-Means Clustering Hierarchical Clustering Principal Component Analysis (PCA) Association Rule Learni

Reinforcement Learning
Learning through trial and error by interacting with the environment Key Concepts: Agent, Environment, Reward Q-Learning Markov Decision Process (MDP) Deep Q-Networks (DQN)

Deep Learning
Subfield of ML based on neural networks Used for image recognition, speech processing, etc. Components: Artificial Neural Networks (ANN) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN)

Model Evaluation & Optimization
Measuring and improving model performance Techniques: Cross-validation Confusion Matrix, Precision, Recall, F1-Score Hyperparameter Tuning (Grid Search, Random Search) Bias-Variance Tradeoff

Student Ratings & Reviews

No Review Yet

Machine Learning Full Course

About Course

🎯 Course Objective

🧩 What You Will Learn

🛠️ Technologies & Tools Used

📁 Course Modules

🧪 Real-World Projects

👨‍🏫 Who This Course Is For

Course Content

Machine Learning Introduction

INTRODUCTION

Supervised Learning
Learning from labeled data (input-output pairs) Algorithms: Linear Regression Logistic Regression Decision Trees Support Vector Machines (SVM) k-Nearest Neighbors (KNN)

Supervised Learning

Unsupervised Learning
earning patterns from unlabeled data Algorithms: K-Means Clustering Hierarchical Clustering Principal Component Analysis (PCA) Association Rule Learni

Unsupervised Learning

Reinforcement Learning
Learning through trial and error by interacting with the environment Key Concepts: Agent, Environment, Reward Q-Learning Markov Decision Process (MDP) Deep Q-Networks (DQN)

Reinforcement Learning

Deep Learning
Subfield of ML based on neural networks Used for image recognition, speech processing, etc. Components: Artificial Neural Networks (ANN) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN)

Deep Learning

Model Evaluation & Optimization
Measuring and improving model performance Techniques: Cross-validation Confusion Matrix, Precision, Recall, F1-Score Hyperparameter Tuning (Grid Search, Random Search) Bias-Variance Tradeoff

Model Evaluation & Optimization

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Resources

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About Course

🎯 Course Objective

🧩 What You Will Learn

🛠️ Technologies & Tools Used

📁 Course Modules

🧪 Real-World Projects

👨‍🏫 Who This Course Is For

Course Content

Machine Learning Introduction

INTRODUCTION

Supervised Learning Learning from labeled data (input-output pairs) Algorithms: Linear Regression Logistic Regression Decision Trees Support Vector Machines (SVM) k-Nearest Neighbors (KNN)

Supervised Learning

Unsupervised Learning earning patterns from unlabeled data Algorithms: K-Means Clustering Hierarchical Clustering Principal Component Analysis (PCA) Association Rule Learni

Unsupervised Learning

Reinforcement Learning Learning through trial and error by interacting with the environment Key Concepts: Agent, Environment, Reward Q-Learning Markov Decision Process (MDP) Deep Q-Networks (DQN)

Reinforcement Learning

Deep Learning Subfield of ML based on neural networks Used for image recognition, speech processing, etc. Components: Artificial Neural Networks (ANN) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN)

Deep Learning

Model Evaluation & Optimization Measuring and improving model performance Techniques: Cross-validation Confusion Matrix, Precision, Recall, F1-Score Hyperparameter Tuning (Grid Search, Random Search) Bias-Variance Tradeoff

Model Evaluation & Optimization

Student Ratings & Reviews

Quick Links

Resources

Legal

Supervised Learning
Learning from labeled data (input-output pairs) Algorithms: Linear Regression Logistic Regression Decision Trees Support Vector Machines (SVM) k-Nearest Neighbors (KNN)

Unsupervised Learning
earning patterns from unlabeled data Algorithms: K-Means Clustering Hierarchical Clustering Principal Component Analysis (PCA) Association Rule Learni

Reinforcement Learning
Learning through trial and error by interacting with the environment Key Concepts: Agent, Environment, Reward Q-Learning Markov Decision Process (MDP) Deep Q-Networks (DQN)

Deep Learning
Subfield of ML based on neural networks Used for image recognition, speech processing, etc. Components: Artificial Neural Networks (ANN) Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN)

Model Evaluation & Optimization
Measuring and improving model performance Techniques: Cross-validation Confusion Matrix, Precision, Recall, F1-Score Hyperparameter Tuning (Grid Search, Random Search) Bias-Variance Tradeoff