Comprehensive Training Curriculum on Artificial Intelligence Training PPT
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Created by Subject Matter Experts
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Structured Sessions
Comprehensive Curriculum
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Real-Life Case Studies
Assessment Questions
Client Proposal
Complete Curriculum
- Introduction to Artificial Intelligence
- History of AI
- Types of AI
- Based on Functionality
- Based on Capabilities
- Artificial Narrow Intelligence
- Artificial General Intelligence
- Artificial Super Intelligence
- Importance of AI
- AI vs Human Intelligence
- Building Blocks of AI
- AI Trends
- AI Statistics
- Key Takeaways
- Let’s Test What We Have Learnt
- Applications of Artificial Intelligence in
- Marketing
- Finance
- Defense & Military
- Telecommunication
- Sales
- Healthcare
- Automobile Industry
- Gaming
- E-Commerce Industry
- Social Media
- Robots
- Education Sector
- Chatbots
- Agriculture
- Supply Chain
- Navigation
- Lifestyle
- Human Resources
- Key Takeaways
- Let’s Discuss
- Overview of Machine Learning
- History of Machine Learning
- Machine Learning Algorithms
- Supervised Learning
- Regression Models in ML
- Introduction to Regression Models
- Types of Regression Models
- Linear Regression
- Polynomial Regression
- Ridge Regression
- Lasso Regression
- Bayesian Regression
- Overview of Decision Trees
- Overview of Random Forest Algorithm
- Classification Models in ML
- Logistic Regression
- KNN Algorithm
- Naive Bayes Algorithm
- SVM Algorithm
- Unsupervised Learning
-
- Clustering in ML
- Types of Clustering in ML
- Partitioning Clustering
- Density-Based Clustering
- Distribution Model-Based Clustering
- Hierarchical Clustering
- Fuzzy Clustering
- Overview of Clustering Algorithms in ML
- Types of Clustering Algorithms
- K-means Algorithm
- Mean-Shift Algorithm
- DBSCAN Algorithm
- Expectation-Maximization Clustering using GMM
- Agglomerative Hierarchical Algorithm
- Affinity Propagation
- Application of Clustering
- Association Rule Learning
- Apriori Algorithm
- Eclat Algorithm
- F-P Growth Algorithm
- Applications of Association Rule Learning
- Hidden Markov Model
-
- Reinforcement Learning
- Supervised Learning
- Importance of Machine Learning
- Steps in Machine Learning
- Data Collection
- Data Preparation
- Choosing a Model
- Training the Model
- Evaluating the Model
- Parameter Tuning
- Making Predictions
- Advantages of Machine Learning
- Disadvantages of Machine Learning
- Future of Machine Learning
- Key Takeaways
- Let’s Test What We Have Learnt
- Introduction to Automatic Language Translation using ML
- Google Translate
- Microsoft Translate
- Facebook Translator
- Limitations of Automatic Language Translator
- Introduction to Medical Diagnosis Using ML
- Objectives of ML-powered Medical Diagnosis
- Benefits of ML-powered Medical Diagnosis
- Applications of ML-powered Medical Diagnosis
- Organizations using ML for Medical Diagnosis
- Introduction to Image Recognition using ML
- Working of Image Recognition
- ML Image Recognition Models
- Image Recognition Application for Face Analysis
- Image recognition Application for Animal Monitoring
- Introduction to Speech Recognition using ML
- Speech Recognition System
- Key Features of Speech Recognition
- Speech Recognition Algorithms
- Speech Recognition with Machine Learning Use Case: IBM
- Key Takeaways
- Let’s Test What We Have Learnt
- Introduction to Deep Learning
- Importance of Deep Learning
- Working of Deep Learning
- Machine Learning vs Deep Learning
- Functions of Deep Learning
- Sigmoid Activation Function
- Hyperbolic Tangent Function
- ReLU
- Loss Functions
- Mean Absolute Error
- Mean Squared Error
- Hinge Loss
- Cross-Entropy
- Optimizer Functions
- Stochastic Gradient Descent
- Adagrad
- Adadelta
- Adaptive Moment Estimation
- Deep Learning Process
- Working of Deep Learning
- Deep Neural Network
- Deep Learning Technique
- How to Create Deep Learning models?
- Two Phases of Learning
- Advantages of Deep Learning
- Applications of Deep Learning
- Detecting Developmental Delay in Children
- Colorization of Black and White Images
- Adding sound to Silent Movies
- Pixel Restoration
- Sequence Generation
- Toxicity testing for chemical structures
- Radiology/Detection of mitosis
- Market Prediction
- Fraud Detection
- Earthquake Prediction
- Deep Fakes
- Limitations of Deep Learning
- Key Takeaways
- Let’s Discuss
- Introduction to Natural Language Processing (NLP)
- Understanding NLP
- NLP Techniques
- Working of NLP
- Importance of NLP
- Steps of NLP
- Lexical Analysis
- Syntactic Analysis
- Semantic Analysis
- Discourse Integration
- Pragmatic Analysis
- Applications of NLP
- Introduction to Natural Language Generation (NLG)
- Working of NLG
- Applications of NLG
- Advantages of NLG
- Introduction to Natural Language Understanding (NLU)
- NLP vs NLU
- NLU Use Cases
- Automatic Ticket Routing
- Automated Reasoning
- Machine Translation
- Question Answering
- Importance of NLU
- Factors to Consider while selecting NLU solutions
- Evaluating the accuracy of NLU solutions
- Leading NLU Companies
- NLP vs NLG vs NLU
- AI vs ML
- ML vs DL
- AI vs ML vs DL
- Key Takeaways
- Overview of Hybrid Model
- Pros and Cons of Hybrid Model
- Introduction to ANN (Artificial Neural Network)
- Layers in a Neural Network
- Neurons in a Neural Network
- Activation Function in a Neural Network
- Threshold Function in a Neural Network
- Sigmoid Function in a Neural Network
- Rectifier Function in a Neural Network
- Hyperbolic Tangent Function in a Neural Network
- Working of a Neural Network
- Introduction to Gradient Descent
- Types of Gradient Descent
- Introduction to Backpropagation
- Advantages of Backpropagation
- Disadvantages of Backpropagation
- Advantages of ANN
- Disadvantages of ANN
- Introduction to CNN (Convolutional Neural Network)
- Working of CNN
- Convolutional layer in CNN
- Hyperparameters of Convolutional Layer in CNN
- Pooling Layer in CNN
- Fully-Connected Layer in CNN
- Working of CNN
- Introduction to Autoencoders
- Components of Autoencoders
- Types of Autoencoders
- Applications of Autoencoders
- Introduction to Variational Autoencoders
- Introduction to Feedforward Neural Networks
- Introduction to Recurrent Neural Networks (RNN)
- Recurrent Neural Network vs Feedforward Neural Network
- Why RNN?
- Problems with RNN
- Types of RNN
- Variants of RNN Architectures
- Bidirectional RNN
- Long Short-Term Memory
- Gated Recurrent Units
- Advantages of RNN
- Disadvantages of RNN
- Applications of RNN
- Introduction to Mixture Density Network
- Components of Mixture Density Networks
- How does a Mixture Density Network look like?
- Key Takeaways
- Let’s Discuss
- AI Strategies for Business Outcomes
- Evaluating Current Capacities of AI
- Building an AI Strategy
- Roadmap For Building a Viable AI Strategy
- Strategy for AI Business Models
- Five-Step Implementation Plan for AI
- AI Assessment Roadmap
- Top AI Job Roles
- Myths and Facts around AI
- ABCDE Framework for AI Enterprise Strategy
- Key Takeaways
- Let’s Discuss
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