Active Course
Data Science & Machine Learning
About this course
An end-to-end diploma program in Data Science and Machine Learning covering Python, statistics, exploratory data analysis, feature engineering, supervised/unsupervised ML models, AI integration, and deployment.
Course Curriculum
• Data Science Fundamentals
• What is Data Science?
• Data Science vs Data Analytics vs Artificial Intelligence
• Machine Learning vs Deep Learning
• Data Science Life Cycle
• CRISP-DM Methodology
• Real-World Applications of Data Science
• Understanding Business Problems
• End-to-End Data Science Workflow
• Career Opportunities in Data Science
• Introduction to Research & Experimentation
• Python Fundamentals
• Python Refresher
• Variables & Data Types
• Operators
• Conditional Statements
• Loops
• Functions
• Modules & Packages
• Exception Handling
• File Handling
• Object-Oriented Programming Basics
• Scientific Computing
• NumPy Arrays
• Array Operations
• Matrix Operations
• Broadcasting
• Vectorization
• Mathematical Functions
• Random Module
• Linear Algebra Basics
• Data Manipulation
• Pandas Series & DataFrames
• Data Import & Export
• Data Filtering
• Indexing & Selection
• GroupBy Operations
• Merge & Join
• Pivot Tables
• Missing Value Handling
• Duplicate Removal
• Feature Engineering
• Data Transformation
• Statistical Foundations
• Types of Data
• Descriptive Statistics
• Probability Theory
• Probability Distributions
• Mean, Median & Mode
• Variance & Standard Deviation
• Correlation & Covariance
• Normal Distribution
• Sampling Techniques
• Central Limit Theorem
• Inferential Statistics
• Hypothesis Testing
• Confidence Intervals
• p-Values
• A/B Testing
• ANOVA (Introduction)
• Chi-Square Test
• Mathematics for ML
• Linear Algebra Basics
• Matrices & Vectors
• Matrix Multiplication
• Eigenvalues & Eigenvectors (Introduction)
• Calculus Concepts
• Derivatives
• Partial Derivatives
• Gradient Descent Concept
• Data Visualization
• Data Visualization Principles
• Choosing Appropriate Charts
• Matplotlib
• Seaborn
• Plotly (Introduction)
• Statistical Charts
• Correlation Heatmaps
• Pair Plots
• Distribution Plots
• Interactive Visualizations
• Exploratory Data Analysis
• Data Profiling
• Dataset Understanding
• Missing Data Analysis
• Outlier Detection
• Feature Distribution
• Correlation Analysis
• Trend Analysis
• Data Storytelling
• Business Insights Generation
• Data Preparation
• Data Collection
• Data Cleaning
• Handling Missing Values
• Handling Outliers
• Encoding Categorical Variables
• Feature Scaling
• Standardization
• Normalization
• Train-Test Split
• Data Leakage Prevention
• Feature Engineering
• Feature Selection
• Feature Extraction
• Dimensionality Reduction
• Principal Component Analysis (PCA)
• Polynomial Features
• Creating Custom Features
• Handling Imbalanced Data
• Data Augmentation Concepts
• Introduction to Machine Learning
• Machine Learning Workflow
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning
• Reinforcement Learning (Introduction)
• Bias vs Variance
• Overfitting & Underfitting
• Model Evaluation Basics
• Machine Learning with Scikit-learn
• Introduction to Scikit-learn
• ML Pipelines
• Dataset Management
• Model Training
• Model Prediction
• Performance Evaluation
• Regression Algorithms
• Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• ElasticNet Regression
• Classification Algorithms
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Trees
• Random Forest
• Naïve Bayes
• Support Vector Machines (SVM)
• Gradient Boosting
• XGBoost
• LightGBM (Introduction)
• Clustering
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN
• Dimensionality Reduction
• Principal Component Analysis (PCA)
• t-SNE (Introduction)
• Association Rule Learning
• Apriori Algorithm
• Market Basket Analysis
• Anomaly Detection
• Isolation Forest
• Outlier Detection
• Fraud Detection Concepts
• Model Performance
• Training vs Testing
• Validation Techniques
• Cross Validation
• Confusion Matrix
• Accuracy
• Precision
• Recall
• F1 Score
• ROC Curve
• AUC Score
• Model Optimization
• Hyperparameter Tuning
• Grid Search
• Random Search
• Feature Importance
• Model Comparison
• Ensemble Learning Basics
• Generative AI for Data Scientists
• ChatGPT for Data Science
• Claude for Research
• Gemini for Code Generation
• AI-Assisted Data Cleaning
• AI Feature Engineering
• AI Model Explanation
• AI Report Generation
• AI Visualization Suggestions
• AI Documentation
• AI Experiment Assistance
• Prompt Engineering for Data Science
• Data Science Prompting
• Python Code Generation
• SQL Query Generation
• Model Interpretation Prompts
• EDA Prompting
• Report Writing Prompts
• Deploying ML Models
• Saving Models
• Pickle & Joblib
• Building Prediction APIs
• Flask Basics
• FastAPI Basics
• Model Inference
• Batch Predictions
• Real-Time Predictions
• Introduction to Cloud Deployment
Hands-on Projects
House Price Prediction System
Student Performance Prediction
Customer Churn Prediction
Loan Approval Prediction
Credit Risk Analysis
Employee Attrition Prediction
Sales Forecasting
Customer Segmentation System
Recommendation System
Spam Email Classifier
Disease Prediction System
Movie Recommendation Engine
AI-Powered Predictive Analytics Dashboard
End-to-End Machine Learning Pipeline
Key Skills Covered
- Exploratory Data Analysis (EDA)
- Feature Engineering & Preprocessing
- Supervised Machine Learning Algorithms
- Unsupervised Machine Learning & Clustering
- Model Evaluation & Hyperparameter Tuning
- Deep Learning Fundamentals
- Machine Learning Model Deployment
Technologies Covered
Python
Pandas
NumPy
Scikit-Learn
Matplotlib
Seaborn
TensorFlow
Flask/FastAPI
Course Details
Duration
6 to 8 Months
Certificate
Upon completion
Start Date
Flexible
Instructor
CDC Training Services Pvt.Ltd