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Full Stack Data Science with Artificial Intelligence

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Full Stack Data Science with Artificial Intelligence

Learn data science with AI to unlock cutting edge skills in automation predictive analytics and personalized solutions Embrace the future of technology with hands-on experience and take advantage of our exclusive paid internship opportunities Join us to be at the forefront of innovation and kickstart your career in the tech industry

Course Content

  • Introduction to Data Science
    Data Science with real-world Examples
    Applications using Data Science
    Applications using Machine Learning
    Applications using Deep learning
    Applications using computer vision and NLP

  • Data types

    varibales

    operators

    print

    precision and fied width

    Python Installation and Running

    Installation and Running

    Jupyter Notebook

    .py file from terminal

    Google Colab

    String

    list

    tuple

    sets

    dictionary

  • Forloops

    while loops

    if

    else

    elif

    break

    continue

    functions

    lambda function

    comprehensions

  • Error / Exception Handling

    File Handling

    JSON module

    OS Module

    Pickle Module

    Datetime Module

    Copy Module

    oops concept

    class

    object

    constructor

    inheritance

    polymorphisam

    abstraction

    excapsulation

  • what is process

    what is multiprocessing

    what is multithreading

    start

    join

    kill

    terminate

  • Matrix Algebra

    Vector Matrix

    multiplication Matrix

    Eigen Values and Eigen vectors

    Regression lines etc

  • Numpy

    Pandas

    Data Visualization Library: Matplotlib

    Seaborn

  • Descriptive Statistics

    Measure of central tendency

    Measure of dispersion

    outliers

    covariance

    correlation

    testing

    hypothesis testing

    mean

    median

    mode etc..

  • Conditional Probability

    Bayes Rule

    Probability Distribution: Discrete and Continuous

    Normal Distribution etc..

  • Types of Machine Learning Methods

    Supervised Machine learning

    Unsupervised Machine learning

    Reinforcement Machine learning

  • Linear Regression

    Simple Linear Regression

    Multiple Linear Regression

    Polynomial Linear Regression

    Loss Function

    MSE

    MAE

    RMSE

    Optimizers

    Gradient Descent

    Regularization Parameters

    L1 and L2 Regularization

    Mini Project - 1 Developing Regression Model to find car cost status

    Mini Project -2 finding best Regresson Line using Gradient Descent Optimizers

  • KNN

    Logistic Regression

    Naive Bayes

    Decision Tree

    Ensemble: Bagging Random Forest Classifier

    Ensemble: Boosting Gradient Boosting

    AdaBoost

    XG Boosting

    Support Vector Machine (SVM)

  • Creation of new features from existing data

    Selection of relevant features for the model

    Handling missing values

    Handling categorical data

    Normalization and scaling

    Encoding categorical variables

    Polynomial features

    Log transformations

    Date-time feature extraction

    Domain-specific transformations

    Dimensionality reduction techniques

    Text feature extraction

    Image feature extraction

    Feature crossing

    Feature splitting

    Handling outliers

    Feature discretization

    Sequence feature extraction

    Time-series feature extraction

    Feature standardization

    Feature normalization

    Filter methods

    Wrapper methods

    Embedded methods

    Univariate feature selection

    Recursive feature elimination (RFE)

    Feature importance from models

    L1 regularization (Lasso)

    L2 regularization (Ridge)

    Variance thresholding

    Mutual information

    Chi-square test

    Correlation matrix

    Principal component analysis (PCA)

    Feature selection with cross-validation

    Sequential feature selection

    Information gain

    Gain ratio

    SelectFromModel

    SelectKBest

    SelectPercentile

  • Clustering

    Dimensionality reduction

    Anomaly detection

    Association analysis

    Density estimation

    Feature learning

    Similarity measurement

    Data preprocessing

    Visualization of high-dimensional data

    Manifold learning

    Centroid initialization

    Number of clusters (k)

    Euclidean distance

    Cluster assignment

    Centroid update

    Inertia (within-cluster sum of squares)

    Elbow method

    Silhouette score

    K-means++ initialization

    Convergence criteria

    Scalability

    Handling large datasets

    Assumption of spherical clusters

    Sensitivity to outliers

    Agglomerative clustering

    Divisive clustering

    Dendrogram

    Linkage criteria

    single

    complete

    average

    Distance metrics

    Euclidean

    Manhattan

    Merging clusters

    Determining the number of clusters

    Cutoff threshold

    Hierarchical tree structure

    Visualization of clusters

    Scalability

    Interpretability

    Dimensionality reduction

    Principal components

    Eigenvalues

    Eigenvectors

    Covariance matrix

    Explained variance

    Projection of data

    Orthogonal transformation

    Scree plot

    Cumulative explained variance

    Standardization of data

    Interpretation of principal components

    Data compression

    Noise reduction

    Density-based clustering

    Core points

    Border points

    Noise points (outliers)

    Epsilon (ε) parameter

    MinPts parameter

    Density reachability

    Density connectivity

    Cluster formation

    Handling of noise

    Ability to find arbitrarily shaped clusters

    Scalability

    Sensitivity to parameter selection

    Computational complexity

  • Neurons

    Synapses

    Weights

    Biases

    Activation functions

    Input layer

    Hidden layers

    Output layer

    Feedforward networks

    Backpropagation

    Gradient descent

    Learning rate

    Loss function

    Epochs

    Batch size

    Training data

    Validation data

    Test data

    Overfitting

    Underfitting

    Regularization

    Dropout

    Weight initialization

    Normalization

    Standardization

    Hyperparameters

    Model evaluation

    Cross-validation

    Sigmoid

    Tanh

    ReLU (Rectified Linear Unit)

    Leaky ReLU

    ELU (Exponential Linear Unit)

    Softmax

    Swish

    Hard Sigmoid

    Parametric ReLU (PReLU)

    Thresholded ReLU

    Mean Squared Error (MSE)

    Mean Absolute Error (MAE)

    Binary Cross-Entropy

    Categorical Cross-Entropy

    Sparse Categorical Cross-Entropy

    Hinge Loss

    Kullback-Leibler Divergence

    Huber Loss

    Log-Cosh Loss

    Cosine Proximity

    Stochastic Gradient Descent (SGD)

    Mini-batch Gradient Descent

    Momentum

    Nesterov Accelerated Gradient (NAG)

    Adagrad

    Adadelta

    RMSprop

    Adam

    Adamax

    Nadam

    L1 regularization

    L2 regularization

    Dropout

    DropConnect

    Early stopping

    Data augmentation

    Batch normalization

    Layer normalization

    Accuracy

    Precision

    Recall

    F1-score

    ROC-AUC

    Confusion matrix

    Precision-Recall curve

    Mean Average Precision (mAP)

    Log loss

    R-squared.

  • Convolutional layers

    Filters (kernels)

    Feature maps

    Receptive field

    Stride

    Padding

    Pooling layers

    Max pooling

    Average pooling

    Global pooling

    Flattening

    Fully connected layers

    Softmax activation

    Cross-entropy loss

    Backpropagation

    Gradient descent

    Batch normalization

    Layer normalization

    Dropout

    VGG 16

    VGG19

    ResNet

    TensorFlow

    Keras

    PyTorch

  • Recurrent Neural Networks (RNNs)

    Hidden state

    Sequence modeling

    Backpropagation through time (BPTT)

    Vanishing gradient problem

    Exploding gradient problem

    Long Short-Term Memory (LSTM)

    Gated Recurrent Unit (GRU)

  • Image reading and writing

    Image resizing

    Image rotation

    Image translation

    Image filtering

    Edge detection

    Contour detection

    Color space conversion

    Image thresholding

    Geometric transformations

    Image blending

    Feature detection

    Object tracking

    Face detection

    Video processing

  • Viola Jones

    HOG

    Yolo v1

    Yolo v2

    Yolo v3

    Yolo v4

    Yolo v7

    Yolo v8

    Unet Segmentation

    v7 Segmentation

    POS Estimation

  • Tokenization

    Lowercasing

    Stop word removal

    Stemming

    Lemmatization

    Text normalization

    Removing punctuation

    Removing special characters

    Handling contractions

    Removing numbers

    Spell checking and correction

    Handling negations

    N-gram generation

    Part-of-speech tagging

    Named entity recognition (NER)

    NLTK

    Spacy

  • SELECT statement

    FROM clause

    WHERE clause

    JOINs INNER JOIN

    LEFT JOIN

    RIGHT JOIN

    FULL JOIN

    GROUP BY clause

    HAVING clause

    ORDER BY clause

    Aggregate functions (SUM AVG COUNT MIN MAX)

    Subqueries (Nested queries)

    UNION and UNION ALL

    Views (CREATE VIEW ALTER VIEW DROP VIEW)

    Indexes (CREATE INDEX DROP INDEX)

    Transactions BEGIN TRANSACTION

    COMMIT

    ROLLBACK

    Constraints PRIMARY KEY

    FOREIGN KEY

    UNIQUE

    CHECK

    Data manipulation INSERT

    UPDATE

    DELETE

    DMl operations

    DQL Operations

    DDl Operations

    Alter

    drop

    sprename

    truncate

  • Formulas and Functions

    math formulas

    LOOKUP

    INDEX-MATCH

    PivotTables (Creating Filtering Grouping)

    Charts and Graphs

    Column chart

    Line chart

    Pie chart etc

    Conditional Formatting

    Data Validation

    Named Ranges

    Excel Tables

    Functions for Text Manipulation

    LEFT

    RIGHT

    CONCATENATE etc

    Functions for Date and Time

    DATE

    TODAY

    MONTH etc

    Data Import and Export

  • Data Visualization

    Power Query

    DAX (Data Analysis Expressions)

    Power BI Desktop

    Power BI Service

    Data Modeling

    DirectQuery and Import Mode

    Advanced Visualization Techniques

    Power BI Mobile App

    Data Connectivity

    Collaboration and Sharing

  • Data Visualization

    Tableau Desktop

    Tableau Server

    Tableau Online

    Data Connectors

    Calculated Fields (Tableau Calculations)

    Parameters and Sets

    Dashboards

    Maps and Spatial Analysis

    Filters and Actions

    Hierarchies

    Tableau Public

    Ad-Hoc Analysis

    Collaboration and Sharing

  • Deployment on AWS

    Azure

    Google Cloud

    Serverless computing

    Managed services Like AWS SageMaker

Instructor

Kamal
Data Science and Generative AI Trainer

Computer Vision Engineer at E&Y Experience in Neural Networks Currently working on Computer Vision Use Cases Artificial Intelligence Trainer and Mentor

Full Stack Data Science with Artificial Intelligence
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  • Lectures50+
  • Skill LevelBasic-Advanced
  • LanguageEnglish
  • QuizzesYes
  • CertificateYes
  • Pass Percentage95%
  • Resume BuildingYes
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