Unlock the power of data with machine learning — in just 8 hours.
This comprehensive, fast-paced course is designed for aspiring data professionals, software engineers, and tech enthusiasts who want to build a strong foundation in Machine Learning with hands-on experience.
Guided by real-world projects and practical exercises, this course takes you from the fundamentals of ML theory to building and evaluating predictive models using Python’s top libraries — including Scikit-Learn, Pandas, TensorFlow, and more.
You’ll start with the basics of supervised and unsupervised learning, explore feature engineering and data preprocessing, and go deep into model tuning and evaluation techniques. Finally, you’ll apply your skills in an end-to-end project and get introduced to deployment tools like Flask and Streamlit.
Whether you aim to enhance your current skillset or kickstart a career in machine learning, this bootcamp equips you with the tools, techniques, and confidence to make data-driven decisions and deploy intelligent models.
What You’ll Learn:
-
Core concepts of Machine Learning and its real-world applications
-
Data preprocessing, feature engineering, and visualization
-
Regression and classification algorithms with real datasets
-
Clustering and anomaly detection using unsupervised learning
-
Model evaluation, tuning, and performance optimization
-
Building and deploying ML projects in a production-ready format
Who This Course is For:
-
Beginners with basic Python knowledge
-
Data analysts and software developers expanding into ML
-
Business professionals exploring AI-powered decision-making
-
Students and learners preparing for careers in data science and AI
- 8 Sections
- 46 Lessons
- 8 Hours
- Module 1: Introduction to Machine Learning (45 min)6
- 1.1What is Machine Learning?
- 1.2Applications of ML in Real-World Scenarios
- 1.3Types of ML: Supervised, Unsupervised, Reinforcement Learning
- 1.4ML vs AI vs Deep Learning
- 1.5Overview of ML Workflow (Data Collection → Preprocessing → Modeling → Evaluation → Deployment)
- 1.6Popular ML Libraries: Scikit-Learn, TensorFlow, PyTorch
- Module 2: Setting Up the Environment & Essential Libraries (30 min)4
- Module 3: Data Preprocessing & Feature Engineering (1 Hour)6
- 3.1Understanding Data: Structured vs Unstructured
- 3.2Handling Missing Values, Duplicates, and Outliers
- 3.3Feature Scaling & Normalization (MinMaxScaler, StandardScaler)
- 3.4Encoding Categorical Variables (Label Encoding, One-Hot Encoding, Ordinal Encoding)
- 3.5Feature Selection: Removing Redundant & Irrelevant Features
- 3.6Hands-on: Preprocessing a Real Dataset
- Module 4: Supervised Learning - Regression (1 Hour 15 min)6
- Module 5: Supervised Learning - Classification (1 Hour 15 min)6
- 5.1What is Classification?
- 5.2Logistic Regression: Understanding the Sigmoid Function
- 5.3Decision Trees & Random Forest: Pros & Cons
- 5.4Support Vector Machines (SVM): Concept & Implementation
- 5.5Performance Metrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC Curve
- 5.6Hands-on: Spam Email Detection using Classification
- Module 6: Unsupervised Learning (1 Hour 15 min)6
- 6.1Introduction to Clustering
- 6.2K-Means Clustering: Algorithm & Practical Implementation
- 6.3Hierarchical Clustering: Concept & Implementation
- 6.4Anomaly Detection: Identifying Outliers in Data
- 6.5Dimensionality Reduction: PCA (Principal Component Analysis)
- 6.6Hands-on: Customer Segmentation Using Clustering
- Module 7: Model Evaluation & Hyperparameter Tuning (1 Hour)6
- Module 8: End-to-End ML Project (1 Hour)6
- 8.1Choosing a Dataset for the Final Project (E.g., House Price Prediction, Customer Churn Analysis)
- 8.2Data Cleaning, Feature Engineering, Model Selection
- 8.3Building & Evaluating the ML Model
- 8.4Introduction to Model Deployment (Flask, Streamlit, FastAPI)
- 8.5Best Practices in Machine Learning Projects
- 8.6Final Thoughts & Next Steps in ML Learning
Unlock the power of data with machine learning — in just 8 hours.
This comprehensive, fast-paced course is designed for aspiring data professionals, software engineers, and tech enthusiasts who want to build a strong foundation in Machine Learning with hands-on experience.
Guided by real-world projects and practical exercises, this course takes you from the fundamentals of ML theory to building and evaluating predictive models using Python’s top libraries — including Scikit-Learn, Pandas, TensorFlow, and more.
You’ll start with the basics of supervised and unsupervised learning, explore feature engineering and data preprocessing, and go deep into model tuning and evaluation techniques. Finally, you’ll apply your skills in an end-to-end project and get introduced to deployment tools like Flask and Streamlit.
Whether you aim to enhance your current skillset or kickstart a career in machine learning, this bootcamp equips you with the tools, techniques, and confidence to make data-driven decisions and deploy intelligent models.
What You’ll Learn:
-
Core concepts of Machine Learning and its real-world applications
-
Data preprocessing, feature engineering, and visualization
-
Regression and classification algorithms with real datasets
-
Clustering and anomaly detection using unsupervised learning
-
Model evaluation, tuning, and performance optimization
-
Building and deploying ML projects in a production-ready format
Who This Course is For:
-
Beginners with basic Python knowledge
-
Data analysts and software developers expanding into ML
-
Business professionals exploring AI-powered decision-making
-
Students and learners preparing for careers in data science and AI
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