Overview
The Data Science Algorithms course is a specialized 4-week program designed to help learners master the foundational and advanced algorithms that are the backbone of data science. This course dives deep into the mechanics, applications, and optimization techniques of algorithms, empowering students to analyze complex data and build predictive models effectively. With a hands-on approach, this program ensures participants develop practical expertise and a thorough understanding of data science algorithms.
Introduction to Data Science Algorithms
- What are algorithms?
- Importance of algorithms in data science
- Types of data science algorithms
- Overview of supervised, unsupervised, and reinforcement learning algorithms
Regression Algorithms
- Linear Regression
- Logistic Regression
- Polynomial Regression
- Ridge and Lasso Regression
- Applications in predictive modeling
Classification Algorithms
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- Naïve Bayes Classifier
- K-Nearest Neighbors (KNN)
- Real-world use cases
Clustering Algorithms
- K-Means Clustering
- Hierarchical Clustering
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Applications in market segmentation and customer profiling
Optimization Algorithms
- Gradient Descent
- Stochastic Gradient Descent (SGD)
- Adam Optimizer
- Applications in deep learning
Ensemble Methods
- Bagging and Boosting
- AdaBoost
- Gradient Boosting (XGBoost, LightGBM)
- Applications in competitive machine learning
Dimensionality Reduction Techniques
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- t-SNE (t-Distributed Stochastic Neighbor Embedding)
- Applications in feature selection and visualization
Deep Learning Algorithms
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) networks
- Applications in image recognition and natural language processing
Course DurationThis course is structured as an intensive 4-week program:
- Duration: 4 Weeks (ideal for individuals seeking in-depth knowledge of algorithms in a short time frame)
- Commitment: 8-10 hours per week, including lectures, hands-on projects, and self-study.
- Gain a deep understanding of key data science algorithms and their applications.
- Learn how to implement algorithms using Python libraries such as Scikit-learn, TensorFlow, and PyTorch.
- Develop the ability to select and optimize algorithms for specific use cases.
- Build practical projects to showcase algorithmic expertise to potential employers.
- Prerequisites: Basic knowledge of Python and mathematics is recommended.
- Course Fee: Affordable pricing with flexible payment options.
- Start Dates: New batches commence every month.
- Expert Guidance: Learn from industry experts with years of experience in data science and machine learning.
- Hands-On Approach: Work on real-world datasets and projects to reinforce theoretical concepts.
- Comprehensive Coverage: Understand and implement a wide range of algorithms used in data science.
- Certification: Receive an industry-recognized certification upon successful course completion.
- Career Support: Access to mentorship, resume-building workshops, and job placement assistance.
- Data science enthusiasts aiming to deepen their understanding of algorithms.
- Professionals transitioning to data science roles.
- Students pursuing careers in machine learning and AI.
- Individuals preparing for data science competitions and hackathons.
Apply Now