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Artificial Intelligence Course Syllabus

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Softlogic Systems Artificial Intelligence Course Syllabus is specifically designed for College Students, Freshers, and Job Seekers. Our Artificial Intelligence Syllabus covers the essential topics such as machine learning, deep learning, natural language processing, computer vision, neural networks, and AI model deployment. Our Artificial Intelligence Course Content helps you learn Artificial Intelligence Step by Step with real-time projects and Interview Preparations.

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Syllabus for The Artificial Intelligence Course

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  • Limitations of Machine Learning
  • Need for Data Scientists
  • Foundation of Data Science
  • What is Business Intelligence
  • What is Data Analysis
  • What is Data Mining
  • Value Chain
  • Types of Analytics
  • Lifecycle Probability
  • Analytics Project Lifecycle
  • Advantage of Deep Learning over Machine learning
  • Reasons for Deep Learning
  • Real-Life use cases of Deep Learning
  • Review of Machine Learning
  • Basis of Data Categorization
  • Types of Data
  • Data Collection Types
  • Forms of Data & Sources
  • Data Quality & Changes
  • Data Quality Issues
  • Data Quality Story
  • What is Data Architecture
  • Components of Data Architecture
  • OLTP vs OLAP
  • How is Data Stored?
  • What is Big Data?
  • 5 Vs of Big Data
  • Big Data Architecture
  • Big Data Technologies
  • Big Data Challenge
  • Big Data Requirements
  • Big Data Distributed Computing & Complexity
  • Hadoop
  • Map Reduce Framework
  • Hadoop Ecosystem
  • What Data Science is
  • Why Data Scientists are in demand
  • What is a Data Product
  • The growing need for Data Science
  • Large Scale Analysis Cost vs Storage
  • Data Science Skills
  • Data Science Use Cases
  • Data Science Project Life Cycle & Stages
  • Data Acuqisition
  • Where to source data
  • Techniques
  • Evaluating input data
  • Data formats
  • Data Quantity
  • Data Quality
  • Resolution Techniques
  • Data Transformation
  • File format Conversions
  • Annonymization
  • Python Overview
  • About Interpreted Languages
  • Advantages/Disadvantages of Python pydoc.
  • Starting Python
  • Interpreter PATH
  • Using the Interpreter
  • Running a Python Script
  • Using Variables
  • Keywords
  • Built-in Functions
  • StringsDifferent Literals
  • Math Operators and Expressions
  • Writing to the Screen
  • String Formatting
  • Command Line Parameters and Flow Control.
  • Lists
  • Tuples
  • Indexing and Slicing
  • Iterating through a Sequence
  • Functions for all Sequences
  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets.
  • Learning NumPy
  • Introduction to Pandas
  • Creating Data Frames
  • GroupingSorting
  • Plotting Data
  • Creating Functions
  • Slicing/Dicing Operations.
  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections
  • Classes & OOPs
  • What is Statistics
  • Descriptive Statistics
  • Central Tendency Measures
  • The Story of Average
  • Dispersion Measures
  • Data Distributions
  • Central Limit Theorem
  • What is Sampling
  • Why Sampling
  • Sampling Methods
  • Inferential Statistics
  • What is Hypothesis testing
  • Confidence Level
  • Degrees of freedom
  • what is pValue
  • Chi-Square test
  • What is ANOVA
  • Correlation vs Regression
  • Uses of Correlation & Regression
  • ML Fundamentals
  • ML Common Use Cases
  • Understanding Supervised and Unsupervised Learning Techniques
  • Similarity Metrics
  • Distance Measure Types: Euclidean, Cosine Measures
  • Creating predictive models
  • Understanding K-Means Clustering
  • Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
  • Case study
  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Recommendation Use-case
  • Case study
  • How to build Decision trees
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Decision Tree
  • Confusion Matrix
  • Case study
  • What is Random Forests
  • Features of Random Forest
  • Out of Box Error Estimate and Variable Importance
  • Case study
  • Case study
  • Various approaches to solve a Data Science Problem
  • Pros and Cons of different approaches and algorithms.
  • Case study
  • Introduction to Predictive Modeling
  • Linear Regression Overview
  • Simple Linear Regression
  • Multiple Linear Regression
  • Case study
  • Logistic Regression Overview
  • Data Partitioning
  • Univariate Analysis
  • Bivariate Analysis
  • Multicollinearity Analysis
  • Model Building
  • Model Validation
  • Model Performance Assessment AUC & ROC curves
  • Scorecard

Conclusion

The Artificial Intelligence Course Syllabus above is for college students, people who have just graduated, and those looking for a job. Our Softlogic Systems provides a syllabus about Artificial Intelligence, including essential topics such as machine learning, deep learning, natural language processing, computer vision, neural networks, and AI model deployment. After completing this syllabus, you will do projects, prepare for job interviews, and apply for jobs. By learning step by step, Artificial Intelligence will help students get a job placement. The goal is to make students learn Artificial Intelligence in a way that helps them get a job.

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FAQs

The main aim of Artificial Intelligence (AI) is to make machines smart enough to perform tasks that usually need human intelligence, like understanding speech or recognizing objects. The goal is to create AI systems that can learn, adapt, and solve problems on their own, which would help us in many ways.

An AI engineer should know programming, math, and statistics. They need skills in machine learning, data analysis, and problem-solving, and should be familiar with AI tools and algorithms.

The goal of Artificial Intelligence (AI) is to give machines the ability to “think like humans” by simulating their cognitive processes. Although artificial intelligence (AI) will never be as smart as a human being, it already has many practical uses.

Many factors have converged to make the present an exciting period for significant AI progress.

  • Over the past 60 years, computing power has increased by a factor of a trillion, making it far more efficient than it was back then.
  • Data processing is becoming more cost-effective.
  • Businesses are collecting more signals from client encounters, which means there is more data to study.
  • The use of AI has already greatly enhanced consumer apps, raising hopes for further greater simplification and hence increasing demand for AI expertise and research.

The goal of Artificial Intelligence (AI) is to give machines the ability to “think like humans” by simulating their cognitive processes. Although artificial intelligence (AI) will never be as smart as a human being, it already has many practical uses.

Many factors have converged to make the present an exciting period for significant AI progress.

  • Over the past 60 years, computing power has increased by a factor of a trillion, making it far more efficient than it was back then.
  • Data processing is becoming more cost-effective.
  • Businesses are collecting more signals from client encounters, which means there is more data to study.
  • The use of AI has already greatly enhanced consumer apps, raising hopes for further greater simplification and hence increasing demand for AI expertise and research.

Absolutely to a greater extent. The vast majority of people who have access to a computer, smartphone, or other smart device are already making use of AI’s practical benefits:

  • Personal voice-processing assistants like Siri and Cortana are available now.
  • Image recognition is highly recommended for use in Facebook photo tagging.
  • Amazon uses machine learning algorithms to provide product recommendations.
  • Waze uses a mix of predictive algorithms, forecasting, and optimisation approaches to recommend the best routes for drivers to take.

Yes, working with Artificial Intelligence generally requires coding. Coding is essential for creating algorithms, training models, and implementing AI systems.

Absolutely to a greater extent. The vast majority of people who have access to a computer, smartphone, or other smart device are already making use of AI’s practical benefits:

  • Personal voice-processing assistants like Siri and Cortana are available now.
  • Image recognition is highly recommended for use in Facebook photo tagging.
  • Amazon uses machine learning algorithms to provide product recommendations.
  • Waze uses a mix of predictive algorithms, forecasting, and optimisation approaches to recommend the best routes for drivers to take.

The most basic kind of Artificial Intelligence will help with automating routine operations. Businesses may save a lot of time and money by using automation to handle data collecting, sorting, entering, and transformation. As it develops, it will be able to forecast your company’s future performance and tell you where you’re succeeding.

Candidates with a minimum educational qualification, such as a bachelor’s degree in any field are eligible for an AI course.

Yes, you can learn Artificial Intelligence on your own. There are many online resources, courses, and tutorials available to help you learn AI at your own pace.

The most basic kind of Artificial Intelligence will help with automating routine operations. Businesses may save a lot of time and money by using automation to handle data collecting, sorting, entering, and transformation. As it develops, it will be able to forecast your company’s future performance and tell you where you’re succeeding.

Yes, Artificial Intelligence is still in high demand. Many industries use AI for automation, data analysis, and improving decision-making, which keeps the need for AI skills strong.

In spite of “millions” of job openings, only roughly 300,000 people are working in artificial intelligence (AI). Programming expertise in Python, R, Java, and C++ are particularly in demand due to the widespread adoption of AI.

The World Economic Forum predicts that 97 million new employment will be produced by 2025 as a direct result of AI innovations, and this trend is expected to continue into the foreseeable future.

In spite of “millions” of job openings, only roughly 300,000 people are working in artificial intelligence (AI). Programming expertise in Python, R, Java, and C++ are particularly in demand due to the widespread adoption of AI.

The World Economic Forum predicts that 97 million new employment will be produced by 2025 as a direct result of AI innovations, and this trend is expected to continue into the foreseeable future.

Yes, math is important for AI. It helps develop algorithms and models that AI systems use. Understanding concepts like algebra, calculus, probability, and statistics is essential for working with AI.

Definitely! We offer ongoing placement assistance to help candidates achieve their career goals. Contact our career advisor to arrange a free demo for the leading Artificial Intelligence course, featuring placement support.

A wide variety of AI and Machine Learning tools and techniques are covered in our Artificial Intelligence training in Chennai. This course, developed by AI experts and aimed at developing professionals, is perfect for you if:

  • Are a recent college graduate.
  • Are contemplating a career change into artificial intelligence development.
  • Have an interest in learning new skills to help you create, oversee, and organise AI-based solutions for your present company.
  • Are looking for a way to continue their education at a higher level to improve their academic skills in this field.

Softlogic offers a variety of training modes that cater to the needs of students, including:

  • Classroom training
  • One-to-One training
  • Live instructor-led online training
  • Customized training options
  • Corporate Training

SLA Institute stands out as an excellent AI study destination, boasting expert trainers, practical learning, industry-aligned curriculum, modern facilities, and a strategic location in OMR Chennai’s IT hub.

At Softlogic Systems, you will receive hands-on experience and rigorous training from industry professionals. The course content covers both introductory and advanced-level material.

Learning from AI training in Chennai, which combines excellence and innovation, will provide you with abilities and expertise that are unique and essential in both your personal and professional lives.

When compared to competitors in the software training market, our placement services are unparalleled.

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