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- Python Functions and Packages
- Working with Data Structures,
- Arrays, Vectors & Data Frames
- Functions & Methods
- Pandas, NumPy, Matplotlib, Seaborn
AI&ML (Artificial Intelligence and Machine Learning Professional)
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study, Virtual Classroom
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
£ 10 £99
certificate availability
Yes
certificate providing authority
Careerera
The syllabus
Python for AI & ML
Applied Statistics
- Descriptive Statistics
- Conditional Probability
- Bell curve
- Gaussian Distribution
- Normal Distribution
- Pearson Correlation
- Hypothesis Testing
- Inferential Statistics
- Probability Distributions
Machine Learning-Supervised learning
- Linear Regression
- Multiple Variable Linear Regression
- Logistic Regression
- Decision Tree Algorithm
- Naive Bayes Classifiers
- K-NN Classification
- Support Vector Machines
- Model Hyperparameter Tuning
- Case Study
Unsupervised learning
- K-means Clustering
- Hierarchical Clustering
- DBSCAN
- Dimension Reduction-Principal Component Analysi (PCA)
- Case Study
Recommendation Systems
- Introduction to Recommendation Systems
- Popularity based model
- Content based
- RecommendationSystem
- Collaborative Filtering (User similarity & Item similarity)
- Hybrid Models
Ensemble Techniques
- Bagging
- Boosting
Introduction to Neural Networks and Deep Learning
- Introduction to Perceptron & Neural Networks
- Activation and Loss functions
- Gradient Descent
- Hyper Parameter Tuning
- Tensor Flow & Keras for Neural Networks
- Introduction to Deep Learning
- Shallow Neural Networks Deep Neural Networks
- Introduction to RNN
- Introduction to CNN
- Introduction to ANN
NLP Basics(Natural Language Processing)
- Introduction to NLP
- Stop Words
- Tokenization
- Stemming and lemmatization
- Bag of Words Model
- Word Vectorizer
- TF-IDF
- POS Tagging
- Named Entity Recognition
- Sequential Models and NLP
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
Computer Vision
- Introduction to Convolutional Neural Networks
- Convolution, Pooling, Padding & its mechanisms
- Forward Propagation
- Backpropagation for CNNs
- CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
- Transfer Learning
- How to Build and Train Deep Neural networks, and apply it to Computer Vision.
Introduction to GANs (Generative adversarial networks)
- Introduction to GANs
- Generative Networks
- Adversarial Networks
- How GANs work?
- DCGANs - Deep Convolution GANs
- Applications of GANs
Introduction to ReinforcementLearning (RL)
- RL Framework
- Component of RL Framework
- Examples of RL Systems
- Types of RL Systems
- Q-learning
Articles
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