Applied Data Science, Machine Learning, Deep Learning and Artificial Intelligence
Description
Objectives of the Program :
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- To enable participants understand how Data Science is used in every aspect of our daily lives and businesses.
- To enable the participants to learn basics of Python.
- To enable the participants to learn Data Science using Python.
- To empower the participants with sufficient knowledge to use latest technologies of Data Science (Hands-On).
- To make the participants understand overview of how Exploratory Data Analysis Works.
- To provide the participants Hands-on experience in Data science concepts.
- To provide the participants Hands-on experience in Machine Learning concepts.
- To provide the participants Hands-on experience in Deep Learning concepts.
- To empower the participants with sufficient knowledge to use latest technologies of Data Science/Machine Learning/Deep Learning/ Artificial Intelligence (Hands-On).
Overview of the Program Content :
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- Introduction to Data Science, Machine Learning, Deep Learning and Artificial Intelligence.
- Basics of Python and Hands-on on Python.
- Introduction to Data Science.
- Data Science using Python.
- Hands on Data Science to Python [Database Connectivity with PYTHON: Performing Database Transactions (Inserting, Deleting, and Updating the Database)].
- Introduction to Machine Learning; Supervised Vs Unsupervised Learning.
- Examples Overview of Machine Learning packages in Python.
- Deep Learning.
- Project in Data Science/Machine Learning /Artificial Intelligence.
Topics for this course
167 Lessons
Chapter 1 : Introduction
1. Introduction to Data science -What is Data science
2. Evolution of Data Science
3. Why Data Science
4. Data Science Use Cases
5. Ways of Doing Data Science
6. Artificial Intelligence(AI)
7. Past AI
8. AI winter
9. Present AI- Machine Learning(ML)
10. Machine Learning(Introduction)
11. Machine Learning Use Cases
12. Deep Learning(Introduction)
13. Deep Learning Use Cases
14. Pre-requisites(Skills/Tools/Math/Stats—Introduction/Names–Briefly)
Chapter 3: Python for Data Science
Module 1: Basic building blocks
Module 2 : Data Types
The data types in Python are divided in two categories:
1. Immutable data types – Values cannot be changed.
2. Mutable data types – Values can be changed
Immutable data types in Python are:
1. Numbers
2. String
3. Tuple
Mutable data types in Python are:
1. List
2. Dictionaries
3. Sets
Module 3: Operators
Module 4: Control Structure
Module 5: Functions
Module 6: File Handling
Module 7: Exception Handling
Module 8: The Object-Oriented Approach: Classes, Methods, Objects
Chapter 4 : Data Science Library and data visualization Using Python
Chapter 5: Maths Behind Data Science: Descriptive Statistics
Chapter 6: Maths Behind Data Science : Inferential Statistics
Chapter 7 : Hypothesis Testing
Chapter 8: Exploratory data analysis /Data Cleaning Techniques/ Data Preparation Techniques
Feature Engineering
Chapter 9: Feature selection Methods
Chapter 10: Applied Machine Learning
Chapter 11: Supervised Learning: Predictive Analysis- Regression
Chapter 12: Supervised Learning: Predictive Analysis- Regression: Polynomial Regression
Chapter 13: Advanced Regression
Chapter 14: Supervised Learning : Classification Methods- Logistic Regression
Chapter 15 : Supervised Learning : Classification Methods Concept of Probability conditional probability, Naive Bayes
Chapter 16: Supervised Learning : Classification Methods – Decision Tree, Random Forest, Ensemble Techniques
Chapter 17: Unsupervised ML
Chapter 18 : Model optimization and evaluation metrics
Chapter 19: Time series forecasting
Chapter 20: Model Selection & Comparison between different ML Models
Chapter 21: Introduction to Model Deployment
About the instructor
3 Courses
5 students
Free