Data Science, Machine Learning, And AI Course
July 21, 2023 2023-07-21 15:37Data Science, Machine Learning, And AI Course
Data Science, Machine Learning, And AI Course
Course Overview
The data science course provides a comprehensive overview of essential topics including Mathematics, SQL, Python, and Machine Learning. Students will learn mathematical concepts such as probability, statistics, and linear algebra, which are critical in data analysis. In addition, they will develop skills in SQL, a programming language used for managing and manipulating databases, which is vital for data retrieval and storage. Students will also learn Python, a versatile programming language used extensively in data science, to write code and develop analytical models. Lastly, students will gain expertise in Machine Learning, a branch of Artificial Intelligence (AI) that enables systems to learn from data and improve over time, which is crucial in predictive modeling and pattern recognition. Overall, the data science course aims to equip students with the knowledge and skills required to succeed in the rapidly growing field of data science.
Suitable For
This course is suitable for
- Individuals interested in learning about data analysis and machine learning.
- Beginners who are looking to gain a strong foundation in essential topics such as mathematics, programming, and machine learning.
- Those with some prior experience in the field who want to enhance their skills and knowledge.
- Students who want to develop analytical models and make data-driven decisions.
Certification
- Certification in Data Science, Machine Learning, and AI
Skills You Will Gain
The students will acquire following set of skills after completing this Training Course.
- Ability to use Mathematical Concepts
- Proficiency in SQL
- Become an expert in Python
- Expertise in Machine Learning
- Ability to Analyze and Visualize Data
- Data Cleaning and Preprocessing Techniques
- Machine Learning Algorithms
- Artificial Intelligence Algorithms
- Data ethics and Data management and Analysis
Career Path
- Data Analyst
- Machine Learning Engineer
- Data Scientist
- Python Developer
- Database Administrator
Machine Learning Projects
- Weather Prediction (Time Series Data)
- Predicting cancer malignant or benign based on Data
- Predicting Stock Prices
- Hand Written Digit Recognition
- Wine Quality Prediction
- Marketing Data Analysis
- Taxi Fare Prediction
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Mathematics & Statistics
Mathematics and statistics are crucial in machine learning, providing tools for creating models that learn from data and make accurate predictions. Linear algebra, calculus, and probability theory are used in algorithms, while statistics analyzes data, identifies patterns, and predicts outcomes. Strong knowledge in these areas is necessary for success in machine learning.
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SQL (Database)
SQL is critical in machine learning, allowing efficient data retrieval and analysis from relational databases. It ensures accurate analysis of large data sets, with solid understanding essential for success.
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Python
Python is a widely-used programming language in machine learning. It offers powerful libraries, tools, and frameworks for data analysis, modeling, and visualization. Python's simplicity and flexibility make it a popular choice for building machine learning models and deploying them in real-world applications.
- 1. Python Setup and What is Python?
- 2. Data Types and Syntax
- 3. Comparison Operators
- 4. Python Loop
- 5. Python Statements
- 6. Logical Operators
- 7. Methods and Functions
- 8. Error and Exception Handling
- 9. Modules Packages and libraries
- 10. Debugging
- 11. Advanced python Modules (DateTime)
- 12. File Management
- 13. Multiple Activities to Perform
- 14. Multiple Projects to Build
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Data Science/ Machine Learning
Machine learning is a subset of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It involves the use of algorithms, statistical models, and data to build predictive models and uncover insights from large datasets.
- 1. Data Preprocessing
- 2. Supervised Learning
- 2.1. Regression Models
- 2.1.1. Simple Linear Regression
- 2.1.1. Multiple Linear Regression
- 2.1.2. Polynomial Regression
- 2.1.2. Random Forest Regression
- 2.1.3. Topics such as
- 2.1.3.1. Assessing a Regression Model
- 2.1.3.2. Bias vs Variance
- 2.1.3.3. Regularisation
- 2.1.3.4. Gradient Descent
- 2.2. Classification Models
- 2.2.1. Decision Tree Classification
- 2.2.2. K-Nearest Neighbor
- 2.2.3. Logistic Regression
- 2.2.4. Naïve Bayes
- 2.2.5. Random Forest Classification
- 2.2.6. Support Vector Machines
- 2.2.7. Additional Topics
- 2.2.7.1. Assessing a Classification Model
- 2.2.7.2. Adaboost
- 2.2.7.3. Gradient Boosting
- 2.2.7.4. XGBoost
- 2.2.7.5. Grid Search CV
- 3. Unsupervised Learning
- 3.1. Clustering Models
- 3.1.1. Hierarchical
- 3.1.2. K-Means Clustering
- 3.2. Association
- 3.2.1. Apriori
- 3.2.2. Eclat
- 4. Build Dashboards for Data Visualisation
- 5. Solved Sample Code Files for easy practice
- 6. Access to Multiple Datasets
- 7. 7+ Real world data projects
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A.I.
- 1. Reinforcement Learning
- 1.1. Thompson Sampling
- 1.2. Upper Confidence Bound (UCB)
- 1.3. Q-Learning
- 2. Natural Language Processing (NLP)
- 3. Deep Learning
- 3.1. Artificial Neural Networks (ANN)
- 3.2. Convolutional Neural Networks (CNN)
- 3.3. Recurrent Neural Networks (RNN)
- 3.4. Additional Topic
- 3.4.1. LSTM
- 4. Solved Sample Code Files for easy practice
- 5. Access to Multiple Datasets
- 6. 2+ Master A.I. Projects
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GIT
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Python GUI And SQLite