AI Courses
Artificial Intelligence
Launchpad’s Artificial Intelligence Course offers an in-depth exploration of AI concepts, tools, and applications.
Artificial Intelligence
Launchpad’s Artificial Intelligence Course offers an in-depth exploration of AI concepts, tools, and applications. You’ll work with machine learning, natural language processing, computer vision, and AI-driven decision-making systems. Our instructors provide practical insights through hands-on projects that prepare you for real-world AI challenges. The course is available online and in-person and equips you with the knowledge and skills needed for a career in AI, along with certification and job placement support.
Curriculum
- 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
Objectives of Learning Artificial Intelligence
Launchpad’s Artificial Intelligence course helps you:
- Learn AI Concepts: Understand the principles of artificial intelligence and its applications.
- Work with AI Tools: Gain experience with Python, TensorFlow, Keras, and other AI tools.
- Develop AI Models: Learn to create models for machine learning, natural language processing, and computer vision.
- Apply AI to Real Problems: Work on projects that apply AI to solve industry-specific challenges.
- Master AI Techniques: Learn advanced techniques like deep learning, reinforcement learning, and AI-driven decision-making.
- Prepare for Certification: Get ready for AI certification exams.
Reason to Choose Launchpad for Artificial Intelligence Training
Launchpad’s Artificial Intelligence training offers:
- Authorized Certifications: Earn certifications from globally recognized tech companies.
- Industry-Experienced Instructors: Learn from professionals with hands-on AI experience.
- Interactive Learning: Participate in live, instructor-led sessions—no pre-recorded content.
- Real-World Projects: Work on projects that reflect real-world AI tasks.
- Skill Development: Build technical expertise and communication skills, including interview techniques.
- Job Placement Support: Benefit from placement assistance, including resume building and LinkedIn optimization.