AI with Data Science

AI with Data Science Course Overview

This comprehensive AI with Data Science course is designed to equip you with cutting-edge skills in artificial intelligence and data analysis. Learn to build intelligent systems and analyze vast datasets using essential tools like Python, R, Numpy, Pandas, and TensorFlow. The course covers advanced topics, including supervised and unsupervised learning, neural networks, and deep learning, through real-world, industry-relevant projects.

In addition, you'll enhance your data visualization abilities through dedicated modules in Tableau and SQL, allowing you to translate complex data into actionable insights.

Training Options for AI with Data Science Course

We offer flexible learning formats, including live instructor-led sessions or self-paced learning with recorded content. Personalized, one-on-one sessions are also available, offering tailored guidance, interview preparation, and career support.

Advantages

The demand for AI and Data Science professionals is skyrocketing, with applications across industries such as healthcare, finance, and technology. AI engineers and data scientists command top-tier salaries and exciting opportunities at leading companies like Google, IBM, and Facebook.

Why Choose This Course?

AI and Data Science are transforming industries, enabling smarter decision-making and automation. This course provides you with the necessary skills to build predictive models, automate processes, and gain insights from large datasets, positioning you at the forefront of AI-driven innovation.

Eligibility

This course is ideal for data analysts, software engineers, business professionals, and anyone with basic knowledge of programming, statistics, or math looking to upskill in AI and Data Science.

Our curriculum covers Python, R, data preprocessing, machine learning, and deep learning, ensuring you're well-equipped to tackle real-world challenges and excel in your AI and Data Science career.

Welcome to the AI with Data Science Course! Master the tools and techniques to analyze data and build intelligent systems, unlocking new career opportunities in this rapidly growing field.

Artificial Intelligence & Data Science Syllabus

Module 1: Introduction to Data Science

Objectives:

• Understand the key concepts and applications of data science.
• Learn the importance of data-driven decision-making.

Topics:

• Data Science Life Cycle
• Data Science vs. Data Analytics vs. Machine Learning
• Applications of Data Science in Business and Industry

Module 2: Data Collection and Data Wrangling

Objectives:

• Learn techniques for collecting and cleaning data.

Topics:

• Data Collection Techniques: APIs, Web Scraping
• Cleaning and Preparing Data with Pandas
• Handling Missing Values and Duplicates
• Data Transformation and Feature Engineering

Module 3: Data Visualization and Exploratory Data Analysis (EDA)

Objectives:

• Understand how to visualize data to uncover insights.

Topics:

• Introduction to Data Visualization Tools: Matplotlib, Seaborn
• Descriptive Statistics and EDA
• Interactive Data Visualization using Plotly

Module 4: Statistical Analysis for Data Science

Objectives:

• Learn the core statistical concepts needed for data science.

Topics:

• Probability Distributions
• Hypothesis Testing
• ANOVA and Regression Analysis

Module 5: Machine Learning for Data Science

Objectives:

• Understand how machine learning is applied to data science projects.

Topics:

• Introduction to Supervised Learning: Linear and Logistic Regression
• Unsupervised Learning: Clustering and Dimensionality Reduction
• Model Evaluation and Tuning

Module 6: Big Data and Data Science Tools

Objectives:

• Learn about the tools and platforms used for handling big data in data science.

Topics:

• Introduction to Hadoop and Spark
• Big Data Frameworks and Ecosystems
• Cloud Computing for Data Science (AWS, GCP)

Module 1: Introduction to Artificial Intelligence

Objectives:

• Understand what artificial intelligence is and its real-world applications.

Topics:

• Definition and History of AI
• AI vs. Machine Learning vs. Deep Learning
• Applications of AI in Industry

Module 2: Problem Solving and Search in AI

Objectives:

• Learn the techniques used for problem solving and search in AI systems.

Topics:

• Informed and Uninformed Search Algorithms
• Optimization Problems
• Game Playing and Adversarial Search

Module 3: Machine Learning in AI

Objectives:

• Understand the role of machine learning in AI and its techniques.

Topics:

• Supervised Learning: Regression, Decision Trees
• Unsupervised Learning: Clustering, Association Rule Mining
• Reinforcement Learning

Module 4: Deep Learning and Neural Networks

Objectives:

• Understand the fundamentals of deep learning and its applications in AI.

Topics:

• Neural Networks: Introduction to Perceptrons and Activation Functions
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN) and LSTMs
• Training Neural Networks: Backpropagation and Optimization

Module 5: Natural Language Processing (NLP) in AI

Objectives:

• Understand how AI techniques are applied to language processing.

Topics:

• Text Processing: Tokenization, Lemmatization
• Sentiment Analysis and Text Classification
• Chatbots and Conversational AI

Module 6: AI Ethics and Society

Objectives:

• Learn the ethical considerations and societal impacts of AI technologies.

Topics:

• Ethics in AI: Bias, Fairness, and Transparency
• Regulations and Laws Surrounding AI
• Impact of AI on Jobs and Society