Name: Introduction To Post Graduate Program in AI & Machine Learning- Henry Harvin® in Philadelphia | Henry Harvin
Uploaded: 2022-10-21 14:37:04

Module 1: Level 1 Foundation : Introduction to Python
- Python Basics
- Python Functions and Packages
- Working with Data Structures, Arrays, Vectors & Data Frames
- Jupyter Notebook – Installation & Function
- Pandas, NumPy, Matplotlib, Seaborn
- Quiz Module 1
Module 2: Level 1 Foundation : EDA and Data Processing
- Data Types
- Dispersion & Skewness
- Uni & Multivariate Analysis
- Data Imputation
- Identifying and Normalizing Outliers
- Quiz Module 2
Module 3: Level 1 Foundation : Applied Statistics
- Descriptive Statistics
- Probability & Conditional Probability
- Hypothesis Testing
- Inferential Statistics Probability Distributions
- Quiz Module 3
Module 4: Level 2 Machine Learning : Supervised learning
- Linear Regression
- Multiple Variable Linear Regression
- Logistic Regression
- Naive Bayes Classifiers
- k-NN Classification
- Support Vector Machines
- Quiz Module 4
Module 5: Level 2 Machine Learning : Ensemble Techniques
- Decision Trees
- Bagging
- Random Forests
- Boosting
- Quiz Module 5
Module 6: Level 2 Machine Learning : Unsupervised learning
- K-means Clustering
- Hierarchical Clustering
- Dimension Reduction-PCA
- Quiz Module 6
Module 7: Level 2 Machine Learning : Featurisation, Model Selection & Tuning
- Feature engineering
- Model selection and tuning
- Model performance measures
- Regularising Linear models
- ML pipeline
- Bootstrap sampling
- Grid search CV
- Randomized search CV
- K fold cross-validation
- Quiz Module 7
Module 8: Level 2 Machine Learning : Recommendation Systems
- Introduction to Recommendation Systems
- Popularity based model
- Content based Recommendation System
- Collaborative Filtering (User similarity & Item similarity)
- Hybrid Models
- Quiz Module 8
Module 9: Level 3 Artificial Intelligence : Introduction to Neural Networks and Deep Learning
- Introduction to Perceptron & Neural Networks
- Activation and Loss functions
- Gradient Descent • Batch Normalization
- TensorFlow & Keras for Neural Networks
- Hyper Parameter Tuning
- Quiz Module 9
Module 10: Level 3 Artificial Intelligence : Computer Vision
- Introduction to Convolutional Neural Networks
- Introduction to Images
- Convolution, Pooling, Padding & its Mechanisms
- Forward Propagation & Backpropagation for CNNs
- CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
- Transfer Learning
- Object Detection
- YOLO, R-CNN, SSD
- Semantic Segmentation
- U-Net • Face Recognition using Siamese Networks
- Instance Segmentation
- Quiz Module 10
Module 11: Level 3 Artificial Intelligence : NLP (Natural Language Processing)
- Introduction to NLP
- Stop Words
- Stemming and Lemmatization
- Tokenization
- Bag of Words Model
- Word Vectorizer
- TF-IDF
- POS Tagging
- Named Entity Recognition
- Introduction to Sequential data
- RNNs and its Mechanisms
- Vanishing & Exploding gradients in RNNs
- LSTMs - Long short-term memory
- GRUs - Gated Recurrent Unit
- LSTMs Applications
- Time Series Analysis
- LSTMs with Attention Mechanism
- Neural Machine Translation
- Advanced Language Models: Transformers, BERT, XLNet
- Quiz Module 11
Module 12: Level 3 Artificial Intelligence : Introduction to Reinforcement Learning (RL)
- RL Framework
- Component of RL Framework
- Examples of RL Systems
- Types of RL Systems
- Q-learning
- Quiz Module 12
Module 13: Level 3 Artificial Intelligence : Introduction to GANs (Generative Adversarial Networks)
- Introduction to GANs
- Generative Networks
- Adversarial Networks
- How do GANs work?
- DCGANs - Deep Convolution GANs
- Applications of GANs
- Quiz Module 13
Module 14: Level 3 Artificial Intelligence : Additional Module
- PowerBI
- Cloud Computing
- Block Chain
- Quiz Module 14
Module 15: Complimentary Module 1: Soft Skills Development
- Business communication
- Preparation for the Interview
- Presentation Skills
Module 16: Complimentary Module 2: Resume Writing
- Resume Writing

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Post Graduate Program in AI & Machine Learning in Philadelphia

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Post Graduate Program in AI & Machine Learning

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Key Highlights

96 Hours of Instructor-Led Sessions

32 Hours of Live Interactive Doubt Solving Sessions

16 Hours of Live Master Sessions by Industry Experts

192 Hours of Self-Paced Learning

192 Hours Hands on with Cloud Labs

Job-Ready Portfolio of 10+ Capstone Projects

98 Auto-Graded Assessments

20+ Industry Case Studies

144 Guided Hands-On Exercises

10+ Assignments and Mini Projects

1 Mock Interview and 2 Hackathons

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Curriculum For Post Graduate Program in AI & Machine Learning in Philadelphia

Module 1: Level 1 Foundation : Introduction to Python

Module 2: Level 1 Foundation : EDA and Data Processing

Module 3: Level 1 Foundation : Applied Statistics

Module 4: Level 2 Machine Learning : Supervised learning

Module 5: Level 2 Machine Learning : Ensemble Techniques

Module 6: Level 2 Machine Learning : Unsupervised learning

Module 7: Level 2 Machine Learning : Featurisation, Model Selection & Tuning

Module 8: Level 2 Machine Learning : Recommendation Systems

Module 9: Level 3 Artificial Intelligence : Introduction to Neural Networks and Deep Learning

Module 10: Level 3 Artificial Intelligence : Computer Vision

Module 11: Level 3 Artificial Intelligence : NLP (Natural Language Processing)

Module 12: Level 3 Artificial Intelligence : Introduction to Reinforcement Learning (RL)

Module 13: Level 3 Artificial Intelligence : Introduction to GANs (Generative Adversarial Networks)

Module 14: Level 3 Artificial Intelligence : Additional Module

Module 15: Complimentary Module 1: Soft Skills Development

Module 16: Complimentary Module 2: Resume Writing

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What you'll Learn in this course

Basics of Python

Algorithms and Machine Learning Techniques

Deep Neural Networks

Natural Language Processing

Image Processing Techniques

Reinforcement Learning

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