Data Sceince & Meachine Learning
Course Rate: INR 60000.00
Duration: 6 Months
About the Course:
Data Science & Machine Learning Course Syllabus**
(*Python | Data Analysis | ML Algorithms | Deep Learning | AI | Cloud Deployment*)
This **comprehensive syllabus** covers **data analysis, machine learning, deep learning, and AI**, preparing you for **real-world** applications.
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## **1. Introduction to Data Science & Machine Learning**
- What is Data Science?
- Applications of Data Science & ML
- Data Science vs Machine Learning vs AI
- Tools & Technologies (Python, Jupyter Notebook, Git, SQL)
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## **2. Python for Data Science**
- Python Basics (Variables, Data Types, Operators)
- Control Flow (if-else, loops)
- Functions & Lambda Expressions
- Object-Oriented Programming (OOP)
- NumPy for Numerical Computing
- Pandas for Data Manipulation
- Matplotlib & Seaborn for Data Visualization
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## **3. Statistics & Probability for Data Science**
- Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)
- Probability Basics (Bayes’ Theorem, Conditional Probability)
- Hypothesis Testing (p-value, t-test, chi-square test)
- Correlation & Covariance
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## **4. Data Preprocessing & Feature Engineering**
- Handling Missing Data
- Data Cleaning & Transformation
- Encoding Categorical Variables
- Feature Scaling (Normalization & Standardization)
- Feature Selection & Dimensionality Reduction (PCA, LDA)
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## **5. Exploratory Data Analysis (EDA)**
- Understanding Data Distribution
- Outlier Detection & Treatment
- Data Visualization (Histograms, Boxplots, Scatterplots)
- Identifying Trends & Patterns
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## **6. Machine Learning Fundamentals**
- Introduction to Machine Learning
- Supervised vs Unsupervised Learning
- Model Training & Evaluation (Train-Test Split, Cross-Validation)
- Performance Metrics (Accuracy, Precision, Recall, F1-Score, ROC Curve)
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## **7. Supervised Learning Algorithms**
- **Regression Models:**
- Linear Regression
- Polynomial Regression
- Ridge & Lasso Regression
- **Classification Models:**
- Logistic Regression
- Decision Trees & Random Forest
- Support Vector Machine (SVM)
- Naïve Bayes
- K-Nearest Neighbors (KNN)
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## **8. Unsupervised Learning Algorithms**
- Clustering Techniques (K-Means, Hierarchical Clustering, DBSCAN)
- Principal Component Analysis (PCA)
- Association Rule Learning (Apriori, FP-Growth)
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## **9. Advanced Machine Learning Techniques**
- Ensemble Learning (Bagging, Boosting, XGBoost, AdaBoost)
- Hyperparameter Tuning (Grid Search, Random Search)
- Handling Imbalanced Data (SMOTE, Class Weight Adjustment)
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## **10. Deep Learning & Neural Networks**
- Introduction to Deep Learning
- Artificial Neural Networks (ANN)
- Activation Functions
- Optimizers (SGD, Adam, RMSprop)
- Model Evaluation & Regularization (Dropout, Batch Normalization)
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## **11. Convolutional Neural Networks (CNN)**
- Understanding Convolutional Layers
- Pooling & Fully Connected Layers
- CNN Architectures (VGG, ResNet, Inception)
- Image Classification with TensorFlow & Keras
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## **12. Natural Language Processing (NLP)**
- Text Preprocessing (Tokenization, Stemming, Lemmatization)
- Bag of Words (BoW), TF-IDF
- Word Embeddings (Word2Vec, GloVe)
- Sentiment Analysis & Text Classification
- Transformer Models (BERT, GPT)
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## **13. Time Series Analysis & Forecasting**
- Introduction to Time Series Data
- Moving Average & Exponential Smoothing
- ARIMA & SARIMA Models
- LSTM for Time Series Forecasting
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## **14. AI & Reinforcement Learning (Optional)**
- Introduction to Reinforcement Learning
- Markov Decision Processes (MDP)
- Q-Learning & Deep Q Networks (DQN)
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## **15. Model Deployment & MLOps**
- Deploying ML Models with Flask & FastAPI
- Model Deployment on Cloud (AWS, Google Cloud, Azure)
- Containerization with Docker
- CI/CD for ML Models (MLflow, Kubeflow)
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## **16. Real-World Data Science Project**
- End-to-End Data Science Project
- Data Collection & Preprocessing
- Model Selection & Evaluation
- Model Deployment & Monitoring
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