Artificial Intelligence Course in Vadodara
Artificial Intelligence or more commonly known as AI deals with the machine making process. These machines are programmed to perform a variety of different tasks which use the help of artificial intelligence. Visual perception, speech and face recognition, decision making and natural language processing are among the elements that are involved for creating AI bots. Candidates who take up this course will become proficient in creating expert systems, human intelligence, and analytics path.
Artificial Intelligence and Machine Learning Course Content
Artificial Intelligence Course Overview
- Introduction to Data Science and AI: Understand the fundamental concepts of data science and AI, their significance, and their applications across various industries.
- Python Basics: Learn the essentials of Python programming, its evolution, key differences between Python versions, and how to set up a Python development environment.
- Data Handling and Processing: Explore techniques for managing and processing data in Python, including data extraction, manipulation, and statistical analysis.
- Exploratory Data Analysis (EDA): Gain knowledge in performing EDA using libraries like Numpy, Scipy, and Pandas to extract valuable insights from data.
- Machine Learning Basics: Understand the basics of machine learning, from selecting appropriate models to training, evaluation, and enhancing model performance.
- Supervised Learning: Dive into supervised learning, covering classification techniques such as logistic regression, naive Bayes, k-nearest neighbors, support vector machines, decision trees, boosted trees, and random forests.
- Unsupervised Learning: Explore unsupervised learning methods, including clustering with K-means and hierarchical clustering, as well as dimensionality reduction techniques like PCA and LDA.
- Hypothesis Testing in ML: Learn about hypothesis testing in machine learning, including normalization, null and alternative hypotheses, and statistical tests like T-test and ANOVA.
- Reinforcement Learning: Gain an understanding of reinforcement learning, its components, and the exploration vs. exploitation trade-off.
- Deep Learning with Tensor Flow: Learn the fundamentals of deep learning using Tensor Flow, covering neural network architecture, forward and back propagation, popular deep learning models, and hands-on projects like Chatbot creation and Facial Recognition systems.
This Course provides a comprehensive understanding of AI and its practical applications, equipping learners with the skills needed to excel in the field.
Python for Machine Learning - Introduction to Data Science:
- Understanding Data Science: Overview of the field of data science and its importance in modern technology.
- The Data Science Life Cycle: Explanation of the stages involved in the data science process, from data collection to model deployment.
- Understanding Artificial Intelligence (AI): Introduction to AI and its applications across various industries.
- Overview of Implementation of Artificial Intelligence: Explanation of machine learning, deep learning, artificial neural networks (ANN), and natural language processing (NLP).
- Python's Role in Machine Learning: Explanation of how Python is used in machine learning for data manipulation, analysis, and model building.
- Python as a Tool for Machine Learning Implementation: Overview of Python's libraries and frameworks used in machine learning, such as NumPy, pandas, scikit-learn, and TensorFlow.
This course provides a comprehensive introduction to data science and machine learning using Python, covering key concepts and their practical implementation.
Introduction to Python:
1. What is Python and Its History
- An overview of Python as a programming language and a brief history of its development.
2. Differences Between Python 2 and Python 3
- Explanation of the key differences between Python 2 and Python 3, including syntax and features.
3. Installing Python and Setting Up the Environment
- Steps to install Python on different operating systems and setting up the development environment.
4. Python Identifiers, Keywords, and Indentation
- Understanding Python's rules for naming identifiers, reserved keywords, and the significance of indentation in Python code.
5. Comments and Documentation in Python
- How to add comments to Python code for better understanding and documenting code using docstrings.
6. Command-line Arguments and User Input
- How to parse command-line arguments and get user input in Python programs.
7. Basic Data Types and Variables in Python
- Introduction to basic data types in Python, such as integers, floats, strings, and booleans, and how to work with variables in Python.
Python Programming Fundamentals:
1. Introduction to Python:
- Overview of Python and its history.
2. Python Versions:
- Differences between Python 2 and Python 3.
3. Environment Setup:
- Installing Python and setting up the development environment.
4. Basic Concepts:
- Python identifiers, keywords, and indentation.
- Comments and documentation in Python.
5. Input and Output:
- Reading and writing text files.
- Appending to files.
- Working with binary files and using the Pickle module.
6. Data Structures:
- Understanding lists, iterators, generators, comprehensions, tuples, and ranges.
7. Dictionaries and Sets:
- Introduction to dictionaries, sets, and their usage in Python.
8. Functions:
- User-defined functions, anonymous functions, and loops and statements in Python.
- Working with Python modules and packages.
9. Exceptions Handling:
- Understanding exceptions, handling exceptions, and using try-except-else and try-finally clauses.
- Raising exceptions and defining user-defined exceptions.
10. Regular Expressions:
- Overview of regular expressions, matching, searching, and search and replace operations.
- Exploring extended regular expressions and wildcard characters.
Advanced Python Topics:
1. Collections and Debugging:
- Understanding named tuples, default dictionaries, and debugging techniques using IDEs.
2. Data Manipulation:
- Understanding different types of data, data extraction, and manipulation using Python.
- Managing raw and processed data, and data wrangling.
- Using mean, median, mode, variation, standard deviation, and probability functions.
3. Machine Learning Models:
- Introduction to machine learning models, model selection, training, evaluating, and improving model performance.
- Understanding predictive models, linear regression, polynomial regression, and multi-level models.
4. Supervised and Unsupervised Learning:
- Exploring supervised learning algorithms like logistic regression, naive Bayes, k-nearest neighbors, support vector machines, decision trees, boosted trees, and random forests.
- Understanding unsupervised learning algorithms like clustering and dimensionality reduction.
5. Hypothesis Testing:
- Introduction to hypothesis testing in machine learning, normalization, standard normalization, and hypothesis parameters.
- Understanding null and alternative hypotheses, p-value, and various types of tests like T-test, Z-test, ANOVA, and Chi-square test.
6. Reinforcement Learning:
- Understanding reinforcement learning algorithms, exploration vs. exploitation trade-off, and their components.
7. Deep Learning with TensorFlow
- Introduction to deep learning, artificial intelligence, and neural networks.
- Setting up a deep learning environment, installing TensorFlow and Keras.
- Exploring TensorFlow, building neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and recursive neural networks.
- Understanding different architectures, working with datasets, and implementing examples using TensorFlow and Keras.
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