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    AI&ML (Artificial Intelligence and Machine Learning Professional)

    BY
    Careerera

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    Mode

    Online

    Duration

    80 Hours

    Fees

    £ 10 99

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    £ 10  £99
    certificate availability

    Yes

    certificate providing authority

    Careerera

    The syllabus

    Python for AI & ML

    • Python Functions and Packages
    • Working with Data Structures,
    • Arrays, Vectors & Data Frames
    • Functions & Methods
    • Pandas, NumPy, Matplotlib, Seaborn

    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

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