Artificial Intelligence is a computer science family in which systems are built to perform functions that frequently demand human intelligence. Tasks involve learning through experience, recognising patterns, interpreting natural language, and exercising judgments. AI encompasses numerous technologies, including machine learning. In this, algorithms learn to improve themselves by experience with the input at hand, while deep learning mimics the networks of the human brain, possibly for processing complex patterns.<\/p>\r\n\r\n
You will learn the basic concepts of Data Science: its lifecycle and integration with AI, how AI is used, and how Python fits into machine learning. At FITA Academy, a learning relationship exists between Python and AI, which is the prerequisite for learning the Artificial Intelligence Syllabus.<\/p>\r\n\r\n
In this section, you’ll learn about Python’s history and evolution, including the differences between Python 2 and 3. You’ll also get hands-on experience installing Python, setting up your environment, and understanding Python identifiers, keywords, and indentation. This module covers comments, command line arguments, user input, and basic data types and variables, providing a solid foundation for Python programming.<\/p>\r\n\r\n
In this module, you’ll learn how to understand and use lists, iterators, and ranges in Python. To manage data efficiently, you’ll also explore generators, comprehensions, and lambda expressions.<\/p>\r\n\r\n
You will delve into Python dictionaries, including usage and features, with many examples. You will also learn about sets, including properties and applications. In the AI Syllabus at FITA Academy, this module is part of our curriculum for enhancing the user’s Python skills. The AI Syllabus incorporates these critical components to provide a robust background in AI Python programming applications.<\/p>\r\n\r\n
This module teaches how to handle files efficiently. We’ll cover reading and writing text files, appending data, and managing binary files manually and with the Pickle module.<\/p>\r\n\r\n
In this module, you’ll learn to define and use Python functions, including creating your user-defined functions. Explore Python packages and their functions, understand anonymous functions, and master loops and statements. You will also be introduced to Python modules and packages, making your coding more efficient and organised.<\/p>\r\n\r\n
In the “Python Exceptions Handling” module of our Artificial Intelligence Course Syllabus at FITA Academy, you will discover how to deal with errors in Python elegantly. You’ll explore what exceptions are, how to manage them with try-except blocks, the try-finally clause, standard exceptions, raising exceptions, and creating user-defined exceptions.<\/p>\r\n\r\n
In this section, you’ll learn the basics of regular expressions, including what they are and how to use the match and search functions. You’ll also explore matching vs searching, search and replace techniques, and extended regex with wildcards.<\/p>\r\n\r\n
You learn all the powerful features of Python under the “Useful Additions” module, including debugging techniques, breakpoints, and using an IDE in the best way possible to facilitate the coding process. We will describe these notions simply and in practice at FITA Academy.<\/p>\r\n\r\n
In the “Introduction to Data Understanding” module, you’ll learn the importance of grasping data, its crucial role in decision-making, and how data forms the foundation of practical analysis. These concepts are integral to the Artificial Intelligence Course Syllabus at FITA Academy.<\/p>\r\n\r\n
You’ll learn about Structured Data and Tabular Data, including CSV (Comma-Separated Values) Files and Relational Databases, focusing on effectively managing and analysing these data formats.<\/p>\r\n\r\n
Understand how to process and analyse Textual Data, Images, Audio and Speech Data, Video Data, and key sub-modules.<\/p>\r\n\r\n
In this lesson, you will learn about Semi-Structured Data. Specifically, you’ll start with JSON (JavaScript Object Notation) and XML (eXtensible Markup Language). You’ll explore their format, structure and critical applications.<\/p>\r\n\r\n
In this section, you will learn rows, including columns, data types and formats, and descriptive statistics to understand tabular data appropriately.<\/p>\r\n\r\n
This module covers the basics of a CSV file, its advantages and limitations, and how to read and write one from a programming perspective. These topics appear in the AI and ML syllabus.<\/p>\r\n\r\n
In this module, you will learn essential tools and techniques for understanding data. You will also learn about data visualisation with charts, graphs, heatmaps, and scatter plots so you can dig deeper into the data to get meaningful insights.<\/p>\r\n\r\n
In this module you will learn how to handle missing, noisy, and inconsistent data. You will also see practical techniques for data cleaning and refinement, thereby making better analyses and decisions.<\/p>\r\n\r\n
You would learn about manipulating data using Python discussing, extraction, cleaning, transformation, integration, and documentation. This module ensures that the concepts related to management and analysis techniques are well understood. It is crucial for the AI Course Syllabus and gives an excellent basis for further applications of AI.<\/p>\r\n\r\n
In the “Central Tendency” module, you’ll learn about Mean, Mode, and Median, including their definitions, calculations, and practical applications to analyse data effectively.<\/p>\r\n\r\n
This module covers data dispersion, including essential subtopics for deeper understanding.<\/p>\r\n\r\n
In this section, you’ll learn about Probability Density and Mass Functions, Statistics, Data Pre-Processing, Conditional Probability, EDA, and working with tools like Numpy, Scipy, Pandas, and Scikit-learn at FITA Academy.<\/p>\r\n\r\n
In this module, you’ll learn about Mean, Mode, Median, Standard Deviation, Variation, Range, and Frequency Tables. These concepts are vital to the Syllabus Artificial Intelligence at FITA Academy.<\/p>\r\n\r\n
You’ll learn about vital statistical methods, such as the Probability Theorem, Distributions, P-value, T-test, Chi-square, ANOVA, and Null Hypothesis, which are essential for concluding data.<\/p>\r\n\r\n
In this module, you will learn how to prepare data for algorithms and comprehend it mathematically. You will also explore techniques for cleaning and organising data for practical analysis and modelling.<\/p>\r\n\r\n
In this section, you’ll learn how to use essential tools like Statsmodels, Numpy, Scipy, Pandas, Matplotlib, Seaborn, and Beautiful Soup to enhance your data analysis skills.<\/p>\r\n\r\n
In this section, you will learn exploratory data analysis techniques, such as variables, univariate and bivariate analysis, addressing missing values and outliers, and the necessary data transformations and creations.<\/p>\r\n\r\n
The “Manipulation” module teaches the fundamental techniques for filtering, cleaning, and reshaping data. At FITA Academy, you can learn to aggregate, index, slice, and pivot the data frames for effective data handling.<\/p>\r\n\r\n
In the “Understanding Machine Learning Models” module, you’ll learn a machine learning model, explore various types, choose the right one, and discover how to train, evaluate, and improve model performance.<\/p>\r\n\r\n
This module will discuss predictive modelling, including linear and polynomial regression, leading to multi-level models.<\/p>\r\n\r\n
In this module, you’ll learn about Machine Learning algorithms and their importance and explore different types, such as Supervised, Unsupervised, and Reinforcement Learning. Discover key concepts in the Artificial Intelligence Subject Syllabus.<\/p>\r\n\r\n
This module will teach you some essential supervised learning algorithms that any data analyst must know. Learn about Logistic Regression, naive Bayes, SVM, Decision Trees, and more to enhance your skills and become a better data analyst.<\/p>\r\n\r\n
Learn about TSA, its terms, advantages, key players, AR and MA models, stationarity, and how to carry out forecasting effectively with TSA techniques.<\/p>\r\n\r\n
You’ll learn about unsupervised learning, including clustering techniques like K-means and Hierarchical Clustering, and dimensionality reduction methods.<\/p>\r\n\r\n
In this section, you’ll learn about hierarchical clustering techniques, including understanding their principles and implementing them through practical exercises to analyse and group data effectively.<\/p>\r\n\r\n
In this module, you’ll learn about the importance of dimensions, the purpose of advantage dimensionality reduction, and critical techniques like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).<\/p>\r\n\r\n
you’ll learn about hypothesis testing in machine learning, its advantages, basics, normalisation, and standard normalisation. Gain insights into key concepts with the Artificial Intelligence Syllabus at FITA Academy.<\/p>\r\n\r\n
In the “Parameters of Hypothesis Testing” module, you’ll learn about the Null Hypothesis, Alternative Hypothesis, and their key parameters. Also, you’ll learn about key concepts like P-Value, T-Test, Z Test, ANOVA Test, and Chi-Square Test, including their types and applications, at FITA Academy. For more insights, check out Artificial Intelligence Interview Questions and Answers<\/a> to boost your preparation!<\/p>\r\n\r\n In this module, you will be introduced to basic reinforcement learning, its benefits, crucial elements, and the tradeoff between exploration and exploitation-what you would want to know to master that AI technique.<\/p>\r\n\r\nOverview Reinforcement Learning Algorithm<\/strong><\/h2>\r\n
Hands on Projects\u00a0<\/strong><\/h2>\r\n