BEST DATA SCIENCE COURSE IN DELHI

The 100 Hours boot camp will guide you starting from how to code to make end to end A.I application

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    Course Curriculum

    Learn Python from Basic to Advance
    Learn Python from Basic to Advance 00:00:00
    Fundamentals of Python ,Numpy and Pandas
    Python & NumPy​ ​Demonstrate introductory programming concepts using Python and NumPy as a tool to navigate data sources and collections 00:00:00
    UNIX​ ​Utilize UNIX commands to navigate file systems and modify files 00:00:00
    Descriptive Statistics​ ​Define and apply descriptive statistical fundamentals to sample data sets Intro to Plotting and Visualization 00:00:00
    Git Hub:git​ ​Learn to keep track of changes and iterations using git version control from your terminal 00:00:00
    Intro to Plotting and Visualization Practice plotting and visualizing data using Python libraries like matplotlib and Seaborn ​ Visualization 00:00:00
    EXPLORATORY DATA ​ ​ ANALYSIS
    Project 2 ​Students will use Pandas to apply advanced NumPy and Python skills in order to clean, analyze, and test data from multiple messy datasets 00:00:00
    Experiment Design​ ​Plan experimental study design with a well thought out problem statement and data framework Pandas & Pivot Tables ​Use Pandas to read, clean, parse, and plot data using functions such as boolean, indexing, math series, joins, and others 00:00:00
    SciPy & Statsmodels​ ​Review statistical testing concepts (p-values, confidence intervals, lambda functions, correlation/causation) with SciPy and Stats models 00:00:00
    Web Scraping​ ​Learn to scrape website data using popular scraping tools 00:00:00
    Bootstrapping​ ​Practice resampling and building inferences about your data 00:00:00
    CLASSICAL STATISTICAL MODELING
    Project 3​ ​Using a provided dataset, students will explore, clean, and model data, outlining their strategy and explaining their results 00:00:00
    Gradient Descent​ ​Dive into the math and theory of how gradient descent helps to optimize loss function for regression models 00:00:00
    Feature Selection​ ​Use feature selection to deepen your knowledge of study design and model evaluation 00:00:00
    Regularization & Optimization Learn to apply regularization and optimization when evaluating model fit 00:00:00
    K-Nearest Neighbors​ ​Begin to look at classification models through an application of the kNN algorithm 00:00:00
    MACHINE LEARNING MODELS
    Project 4​ ​Clustering ​Students will scrape and model their own data using multiple methods, outlining their approach and evaluating any risks or limitations 00:00:00
    Define clustering and it’s advantages and disadvantages from classification models 00:00:00
    Ensemble Models​ ​Build and evaluate ensemble models, using decision trees, random forests, bagging, and boosting 00:00:00
    NLP​ ​Get introduced to natural language processing through sentiment analysis of scraped website data. 00:00:00
    Naive Bayes​ ​Learn how Naive Bayes can simplify the process of analyzing data for supervised learning algorithms 00:00:00
    Time Series Analysis ​Analyze and model time series data using the ARIMA model in Pandas 00:00:00
    PCA and Dimension Reduction with practical example on handwriting digits 00:00:00
    SVM algorithm on regression and Classification problem with hyper parameter tuning 00:00:00
    Understand Eigen Values and Eigen Vectors 00:00:00
    Introduction to Deep Learning
    Artificial Neural Network concept ,design and implementation 00:00:00
    Feed Forward Neural Network and Back Propagation 00:00:00
    Apply Neural Network to solve Employee Retention Dataset 00:00:00
    Deep Dive into Artificial Inteligence
    CNN Introduction,Building Fundamentals of how CNN works 00:00:00
    OpenCV for Face Recognition and Video Analytics 00:00:00
    Recurrent Neural Network and LSTM model with CNN for large text Data Training and learning pattern 00:00:00
    Recommender Systems​ ​Build and apply basic recommender systems in order to predict on sample user data 00:00:00
    Portfolio Development
    ​Work with career coaches to create and polish your portfolio for employers 00:00:00
    Interview Prep​ ​Practice 20+ data science case studies to prep for job interviews 00:00:00

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