Data Science with Machine Learning
Data Science with Machine Learning
- Understanding the basics of data science, including its history, applications, and relevance to business.
- Understanding the data science process, including problem identification, data acquisition, data preparation, data analysis, and communication of results
- Identifying and accessing existing datasets, data archives, and public records
- Understand the importance of data cleaning in data science and its impact on business decisions.
- Learn various techniques for data pre-processing, data quality assessment, data profiling, and data transformation.
- Understand the challenges involved in data cleaning, such as missing data, data duplication, and data inconsistency.
- Learn how to apply data-cleaning techniques to real-world datasets.
- Writing research reports, presenting findings, and communicating results effectively.
- How to identify and handle missing data, outliers, and other common data quality issues.
- Provide participants with an understanding of different types of data and how to preprocess them effectively, including numerical, categorical, time series, text, and image/audio data.
- Teach participants how to generate and select features that are relevant and informative for machine learning and data analysis tasks.
- Introduce participants to advanced feature engineering techniques, such as feature selection and dimensionality reduction.
- Provide students with hands-on experience working with real-world datasets and implementing feature engineering
- techniques using popular tools and libraries such as Python and sci-kit-learn..
- Understand the fundamental concepts and techniques of machine learning, including supervised and unsupervised learning, deep learning, and model evaluation.
- Preprocess data to make it suitable for use in machine learning algorithms, including data collection, cleaning, transformation, and reduction.
- Apply supervised learning techniques such as regression, decision trees, and ensemble methods to predict output
variables based on input variables. - Apply unsupervised learning techniques such as clustering, dimensionality reduction, and association rule mining to find patterns and relationships in data without labeled output variables.
- Select the best machine learning model for a given task and evaluate its performance using metrics such as accuracy, precision, and recall.
- Understand ethical considerations in machine learning, such as bias in data and models, algorithmic fairness, and privacy and security.
- Advanced machine learning algorithms can learn complex patterns in data and provide more accurate predictions than
traditional methods. - Machine learning algorithms can process large amounts of data quickly and make real-time decisions, allowing businesses to respond faster to changes in their environment.
- Machine learning algorithms can personalize products and services to individual users, providing a better customer
experience. - Machine learning can automate routine tasks, freeing up time for employees to focus on higher-level tasks.
- Machine learning algorithms to assess and mitigate risks in various industries, such as finance and healthcare.
- Machine learning algorithms analyze customer behavior and predict which customers are at risk of leaving, allowing
businesses to take proactive measures to retain them
No prior knowledge is required. Learning will start from scratch.
| Course Title | Data Science with Machine Learning |
| Days per week | Sundays only |
| Number of hours per week | 3 hours per day |
| Total study time | 12 classes – 36 credit hours |
| Requirements / pre-requisites | – |
- Virtual Internship Experience: Gain practical experience in a virtual internship setting, applying data science concepts to real-world projects.
- Data Cleaning Proficiency: Develop skills in identifying and addressing data issues such as missing values, duplicates, and outliers, ensuring dataset quality for analysis.
- Machine Learning Mastery: Acquire expertise in various machine learning techniques, including supervised and unsupervised learning, regression, classification, and deep learning.
Communication Skills Enhancement: Improve communication skills through the creation of reports, presentations, and interactive visualizations to effectively convey insights and findings.
- What is Data Science?
- The Data Science Process
- Tools and Technologies Used in Data Science
- Introduction to Python
- Python Library for Data Science
- Applications of Data Science
- Understanding data cleaning
- Importance of data cleaning in data science
- Data quality dimensions
- Types of data errors
- Data cleaning vs. data pre-processing
- Data normalization, standardization, and transformation
- Outlier detection and treatment
- Data Quality Assessment
- Types of missing data
- Techniques for handling missing data
- Introduction to EDA
- Overview of the data analysis process
- Importance of EDA in data analysis
- Common tools and techniques used in EDA
- Basic plotting techniques (histograms, scatter plots, box plots)
- Advanced plotting techniques (heatmaps, density plots, violin plots)
- Choosing appropriate plots for different types of data
- Descriptive statistics
- Measures of dispersion (range, standard deviation, interquartile range)
- Skewness and kurtosis
- Correlation and covariance
- Exploring relationships between variables
- Scatter plots and correlation
- Categorical variables and contingency tables
- Multivariate analysis
- Case studies and real-world applications
- Introduction to Machine Learning
- Types of Machine Learning: Supervised, Unsupervised, Semi-Supervised, Reinforcement
- The role of Data Science in Machine Learning
- Data Transformation and Reduction
- Feature Scaling
- Supervised Learning
- Regression
- Classification
- Decision Trees
- Ensemble Methods
- Model Evaluation Metrics
- Unsupervised Learning
- Clustering
- Dimensionality Reduction
- Association Rule Mining
- Accuracy, Precision and Recall
- Introduction to Neural Networks
- Single-layer Neural Networks
- Perceptron and its architecture
- Training and optimization algorithms
- Applications of single-layer neural networks
- Multi-layer Neural Networks
- Multi-layer perceptron (MLP) and its architecture
- Backpropagation algorithm and its variants
- Regularization techniques for deep learning
- Convolutional Neural Networks (CNNs)
- Introduction to CNNs and their architecture
- Training and optimization of CNNs
- Applications of CNNs in Image and video analysis
- Recurrent Neural Networks (RNNs)
- Introduction to RNNs and their Architecture
- Backpropagation through time (BPTT) algorithm
- Applications of RNNs in natural language processing
- Single-layer Neural Networks
- Model evaluation
- Results analysis and interpretation
- Presentation and documentation
Overview
Course Modality
- On-site
- Online
Course Duration
- 12 Hours
Course
- Data Science with Machine Learning
Course Support
- 24/7 Support and Recording Available
Course Language
- English
Trainer Info
Mehwish Alam – Trainer Nodebook Private Limited
Experienced data scientist, Microsoft Ambassador, Stanford Section Leader. Skilled in Azure, ML, Python, R. Expertise in data modeling, ML algorithms. B.S. Computer Science from NED University.
