Machine Learning

About Course

A machine learning course typically covers the fundamental concepts and techniques used to build intelligent systems that can learn from data. Students are introduced to key algorithms, such as supervised and unsupervised learning, along with practical applications in fields like image recognition and natural language processing. The course emphasizes the importance of data preprocessing, feature selection, and model evaluation to ensure accurate predictions and robust performance.

Throughout the course, learners engage with hands-on projects to apply theoretical knowledge to real-world problems. Tools like Python and popular libraries such as TensorFlow and scikit-learn are commonly used to implement machine learning models. By the end of the course, students gain the skills to design, train, and deploy machine learning solutions, making them well-prepared for roles in data science, AI development, and related fields.

Course Content

Module 1: Introduction to Machine Learning

Overview of machine learning and its significance in industry.

00:00
Types of learning: supervised, unsupervised, and reinforcement learning.

00:00
Key concepts: overfitting, underfitting, and model evaluation metrics.

00:00

Module 2: Data Preprocessing and Feature Engineering

Module 3: Regression Techniques

Module 4: Classification Algorithms

Module 5: Clustering and Association Rule Learning

Module 6: Introduction to Deep Learning

Module 7: Project 1: Predictive Modeling with Real-World Data

**Module 8: Project 2: Machine Learning Pipeline Implementation

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About Course

Course Content

Module 1: Introduction to Machine Learning

Overview of machine learning and its significance in industry.

Types of learning: supervised, unsupervised, and reinforcement learning.

Key concepts: overfitting, underfitting, and model evaluation metrics.

Module 2: Data Preprocessing and Feature Engineering

Handling missing data, outlier detection, and data normalization.

Feature selection and dimensionality reduction techniques (PCA, LDA).

Encoding categorical variables and dealing with imbalanced datasets.

Module 3: Regression Techniques

Linear regression, polynomial regression, and regularization techniques (Ridge, Lasso).

Multiple linear regression and interpreting coefficients.

Evaluating regression models using R-squared, MAE, MSE, and RMSE.

Module 4: Classification Algorithms

Logistic regression, decision trees, and support vector machines (SVM).

Performance evaluation using confusion matrix, ROC, AUC, and precision-recall.

Advanced classifiers: k-NN, Naive Bayes, and ensemble methods (Random Forest, Gradient Boosting).

Module 5: Clustering and Association Rule Learning

K-means clustering, hierarchical clustering, and DBSCAN.

Evaluation of clustering performance: silhouette score, Davies-Bouldin Index.

Apriori algorithm for association rule mining and market basket analysis.

Module 6: Introduction to Deep Learning

Fundamentals of neural networks: perceptron, activation functions, and backpropagation.

Building deep neural networks using TensorFlow/PyTorch.

Introduction to CNNs and RNNs, and their applications in image and text processing.

Module 7: Project 1: Predictive Modeling with Real-World Data

Developing a predictive model for a business problem (e.g., customer churn, sales forecasting).

Data preprocessing, model selection, and hyperparameter tuning.

Deployment and monitoring of the machine learning model.

**Module 8: Project 2: Machine Learning Pipeline Implementation

Building a complete ML pipeline from data ingestion to model deployment.

Automating the pipeline using tools like MLflow or Kubeflow.

Documentation and presentation of the project findings.

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About Course

Course Content

Module 1: Introduction to Machine Learning

Overview of machine learning and its significance in industry.

Types of learning: supervised, unsupervised, and reinforcement learning.

Key concepts: overfitting, underfitting, and model evaluation metrics.

Module 2: Data Preprocessing and Feature Engineering

Handling missing data, outlier detection, and data normalization.

Feature selection and dimensionality reduction techniques (PCA, LDA).

Encoding categorical variables and dealing with imbalanced datasets.

Module 3: Regression Techniques

Linear regression, polynomial regression, and regularization techniques (Ridge, Lasso).

Multiple linear regression and interpreting coefficients.

Evaluating regression models using R-squared, MAE, MSE, and RMSE.

Module 4: Classification Algorithms

Logistic regression, decision trees, and support vector machines (SVM).

Performance evaluation using confusion matrix, ROC, AUC, and precision-recall.

Advanced classifiers: k-NN, Naive Bayes, and ensemble methods (Random Forest, Gradient Boosting).

Module 5: Clustering and Association Rule Learning

K-means clustering, hierarchical clustering, and DBSCAN.

Evaluation of clustering performance: silhouette score, Davies-Bouldin Index.

Apriori algorithm for association rule mining and market basket analysis.

Module 6: Introduction to Deep Learning

Fundamentals of neural networks: perceptron, activation functions, and backpropagation.

Building deep neural networks using TensorFlow/PyTorch.

Introduction to CNNs and RNNs, and their applications in image and text processing.

**Module 7: **Project 1: Predictive Modeling with Real-World Data

Developing a predictive model for a business problem (e.g., customer churn, sales forecasting).

Data preprocessing, model selection, and hyperparameter tuning.

Deployment and monitoring of the machine learning model.

**Module 8: Project 2: Machine Learning Pipeline Implementation

Building a complete ML pipeline from data ingestion to model deployment.

Automating the pipeline using tools like MLflow or Kubeflow.

Documentation and presentation of the project findings.

Student Ratings & Reviews

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Have any Question?

Module 7: Project 1: Predictive Modeling with Real-World Data