Full Stack Program
Business Analytics
Learn the most desired and rewarding skill of the century. Full Stack provides a complete learning in terms of understanding the concepts, mastering them thoroughly, and applying them in real life.

12 Weeks Duration

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Full Stack Program Overview

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Real World Projects

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What you will learn
Learn indepth concepts, fundamentals, and application with handson problem solving.
Requirements
No prior coding experience, beginner friendly, students from all academic years and branch of study can join.
What next
UPGRADE to GPP to learn all required skills and specialize in your chosen industry with placement guaranteed.
Full Stack 3 Months Journey
Learn Programming
Learn python and R programming to begin your journey
Learn Advance Algorithms and Modeling
Expertise in creating data analytics and machine learning algorithms. Upskill in AI, computer vision, natural language processing and more
Choose Anyone Specialization
Advance Programming, Business Intelligence & Tableau, Big Data or Cloud Computing
Choose Anyone Domain
CS & IT, Non CS&IT Engineering (Mechanical, Electronics & Electrical, Automobile, Chemical, Civil and other Engineering) Business Management (including Marketing, HR, Finance and . . .), Healthcare, Biotechnology, Economics, Finance and more
Case Studies and Projects
Completion Certificate
Course Outline
Python Programming
Data Types, Python Introduction, Installation and Setup, Python Basics, Conditionals & Loops, Working with Functions, List manipulation, Tuple, Set & Dictionary, Regular Expression, Date Time
Data Manipulation with Pandas
Pandas in Python offers data structures and processes for manipulating numerical tables and time series. Data Manipulation, Missing Values, Data Preprocessing, Grouping, Merge, Broadcasting
NumPy
Numpy has functions for operating in the domain of linear algebra, matrices, and more. Why NumPy is fast, Create NumPy arrays, Slicing & Indexing, Mathematical Operations  1D, Boolean Indexing  1D, Boolean Indexing  2D, NumPy Broadcasting.
Data Visualization with Matplotlib
Bar charts, scatter plots, count plots, line plots, pie charts, donut charts, etc, with Python matplotlib.
Data Visualization with Seaborn
Regression plots, categorical plots, area plots, etc, with Python seaborn.
Data Visualization with Plotly
Creating advance and interactive plots with Plotly
Data Preprocessing
Encoding, Scaling with Normalization and Min Max Scaling, Outlier Correction, Missing Values, Polynomial Variables etc. Unstructured Data, Feature Extraction, Feature Engineering, Bias Variance Tradeoff, Unbalanced Data,
Supervised Machine Learning : Regression Analysis
Introduction to Linear Regression, Optimal Coefficients, Cost function, Coefficient of Determination, Analysis of Linear Regression using dummy Data, Linear Regression Intuition. Multiple regression and use in solving real world problems. RIDGE, LASSO, ELASTICENET AND POLYNOMIAL REGRESSION, L1 and L2 regularization. Regression Analysis, Handling, Residuals analysis, AIC, BIC, Model Fitting, Training and Test Data, RSquare, Dummy variables, Non Linear Regression, Gradient descent algorithm that is an iterative optimization approach to finding local minimum and maximum of a given function, KNN, Regression using Decision Trees and Random Forest. Support Vector Machine, Decision Tree. How to train the model, how to evaluate the model and how to optimize the efficiency of the model.
Supervised Machine Learning : Classification Analysis
Handling Classification Problems, Logistic Regression, Cost Function, Finding Optimal Values, Solving Derivatives, Multiclass Logistic Regression, Finding Complex Boundaries and Regularization, Using Logistic Regression from Sklearn. Bayes Theorem, Independence Assumption in Naïve Bayes, Probability estimation for Discrete Values Features, How to handle zero probabilities, Implementation of Naïve Bayes, Finding the probability for continuous valued features, Text Classification using Naïve Bayes. Introduction to KNN, Feature scaling, Cross Validation, Finding Optimal K, Implement KNN, Curse of Dimensionality, Handling Categorical Data, Pros & Cons of KNN. Intuition behind SVM, SVM Cost Function, Decision Boundary & the C parameter, using SVM from Sklearn, Finding Non Linear Decision Boundary, Choosing Landmark Points, Similarity Functions, How to move to new dimensions, Multiclass Classification, Choosing Parameters using Grid Search, Using Support Vectors to Regression. Decision Trees, Getting Best Decision Tree, Deciding Feature to Split on, Continuous Valued Features, Code using Sklearn decision tree, information gain, Gain Ratio, Gini Index, Decision Trees & Overfitting, Pruning. Introduction to Random Forests, Data Bagging and Feature Selection, Extra Trees, Classification report to evaluate the model on recall, precision, fsupport, support, accuracy etc. Confusion matrix to evaluate the true positive, true negative, false positive and false negative outcomes in the model.
Ensemble Modelling
Bagging, Boosting, Random Forest, AdaBoost (Adaptive Boosting), Gradient boosting, Hyperparameter Tunning, Cross Validation, Grid Search and more
Unsupervised Machine Learning
Clustering, Kmeans, How to choose Optimal K, Silhouette algorithm to choose K, Introduction to K Medoids, K Medoids Algorithm, Hierarchical Clustering, Bottom up/Divisive Approach. Distance methods  Euclidean, Manhattan, Cosine, Mahalanobis. Principal Component Analysis, Intuition behind PCA, Math behind PCA, Finding Optimal Number of Features. LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
Recommendation System
Purposes of Recommender Systems, Paradigms of Recommender Systems, Collaborative Filtering, Association Rule Mining, Market Basket Analysis, Generation Apriori Algorithm, Apriori Algorithm, User Movie Recommendation Model
TensorFlow and Keras
Introduction to TensorFlow, Introduction to Keras, Creating Models with Keras, Working with Keras APIs
Deep Learning
Implementing Neural Network, How to compose Models in Pytorch, Saving and Loading model, Intuitively building networks, Introduction to Artificial Neural Networks, Hidden layers, Activation function, Loss Functions, Understand Forward & Back propagation, Regularization, Types of Regularization, Normalization, Different Optimization Technique, Gradient Descent, Vanishing Gradient, Batch Norm, Transfer Learning, Q Learning, Encoder Decoder, Reinforcement Learning,
Computer Vision
Pooling Layer, Data Flow in CNN, Architecture of CNN, Initializing weights, Forward Propagation in TensorFlow, Convolution and Maxpool Functions, Regularization using Dropout layer, Adding Dropout Layer to the network, Building CNN Keras, AlexNet, VGGNet, Resnet, ResNext, Face Detection, Face Tracking, Face Recognition, Object Detection,
Natural Language Programming
Regular Expression, Using Words as Features, Basics of word processing, Stemming, Part of Speech, Lemmatization, Building Feature set, Classification using NLTK Naïve Bayes, Count vectorizer, Ngram, TFIDF, Word cloud, Principal Component Analysis, Bigrams & Trigrams, Web Scraping with BeautifulSoup, Text summarization, Lex Rank algorithm, Latent Dirichlet Allocation (LDA) Technique, Word2vec Architecture (Skip Grams vs CBOW), Text classification, Document vectors, Text classification using Doc2vec, Music Analytics, Machine Translation, Text Classification, Text Segmentation, Sentiment Analysis, NLP vs. NLU vs. NLG, Word2vec and Glove, RNN/ LSTM/ BiLSTM/ GRU
Time Series Analysis
Introduction to Time Series, Stationary and Non Stationary, AutoCorrelation, Rolling Forecast, Exponential Forecast, Autoregressive Moving Average (ARMA) Models, Autoregressive Integrated Moving Average (ARIMA) Models, Financial Time Series, Auto Regressive Conditional Heteroscedasticity (ARCH) Models, Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) Models, Vector Auto Regressive (VAR) Models, RNN and LSTM
Guided projects
20+ Guided Projects
Course Outline
Introduction
`What is Data Science? Work of Data Scientist, Data Science and ML, Why Python, Introduction to Machine Learning, Supervised Learning, Unsupervised Machine Learning
Python Programming
Python Anaconda and Jupyter Notebook, Variables in Python, Data Types, Data Structure, Pandas Data frame, Numpy Array. Open and read Text files, Read file line by line, CSV Files, Work with CSV Files, Read Excel. Why NumPy is fast, Create NumPy arrays, Slicing & Indexing, Mathematical Operations  1D, Boolean Indexing  1D, Boolean Indexing  2D, NumPy Broadcasting. Introduction to Pandas, Accessing Data in Pandas, Manipulating Data in Data Frame, Handling NAN, Handling Strings in Data. Different ways for Data Visualization, Types Of Data Visualization, What is Data Visualization?, Importance Of Data Visualization. Plotting Graphs, Customizing Graph, Bubble Chart, Pie Chart, Histogram, Bar Graph, How to decide Graph Type. Categorical Distribution Plots, Categorical Scatter plots, Plotting with Categorical Data, Visualizing Statistical Relationships  Scatter Plot, Seaborn vs Matplotlib, Introduction to Seaborn, Starting with Seaborn, Visualizing Statistical Relationships  Line Plot.
Statistics
Introduction of Statistics, Data Types in Statistics, Sample & Population, Simple Random Sampling, Stratified sampling, Cluster sampling, Systematic Sampling, Categories of Statistics. Measures in Descriptive Statistics, Measures of central tendency, Measures of Spread, Range, Variance & Standard Deviation, Measure of Position. Introduction to Inferential Statistics, Why Inferential Statistics?, Probability Distribution, Normal Distribution, Standard Normal Distribution, Sampling Distribution, Central Limit Theorem. What is Hypothesis Testing, Null & Alternative Hypothesis, Significance Level, Test statistic, Test Statistic: Critical value & Rejection Region, Test Statistic: Type of Test, Errors in Hypothesis Testing.
Regression Analysis
Introduction to Linear Regression, Optimal Coefficients, Cost function, Coefficient of Determination, Analysis of Linear Regression using dummy Data, Linear Regression Intuition. Multiple regression and use in solving real world problems. RIDGE, LASSO, ELASTICENET AND POLYNOMIAL REGRESSION, L1 and L2 regularization
Classification Analysis
Handling Classification Problems, Logistic Regression, Cost Function, Finding Optimal Values, Solving Derivatives, Multiclass Logistic Regression, Finding Complex Boundaries and Regularization, Using Logistic Regression from Sklearn. Bayes Theorem, Independence Assumption in Naïve Bayes, Probability estimation for Discrete Values Features, How to handle zero probabilities, Implementation of Naïve Bayes, Finding the probability for continuous valued features, Text Classification using Naïve Bayes. Introduction to KNN, Feature scaling, Cross Validation, Finding Optimal K, Implement KNN, Curse of Dimensionality, Handling Categorical Data, Pros & Cons of KNN. Intuition behind SVM, SVM Cost Function, Decision Boundary & the C parameter, using SVM from Sklearn, Finding Non Linear Decision Boundary, Choosing Landmark Points, Similarity Functions, How to move to new dimensions, Multiclass Classification, Choosing Parameters using Grid Search, Using Support Vectors to Regression. Decision Trees, Getting Best Decision Tree, Deciding Feature to Split on, Continuous Valued Features, Code using Sklearn decision tree, information gain, Gain Ratio, Gini Index, Decision Trees & Overfitting, Pruning. Introduction to Random Forests, Data Bagging and Feature Selection, Extra Trees, Regression using Decision Trees and Random Forest.
Unsupervised Machine Learning
Introduction to Unsupervised Learning, Introduction to Clustering, Using Kmeans for Flat Clustering, KMeans Algorithm, Using KMeans from Sklearn, Implementing Fit & Predict Functions, Implementing KMeans Class, How to choose Optimal K, Silhouette algorithm to choose K, Introduction to K Medoids, K Medoids Algorithm, Introduction to Hierarchical Clustering, Top down/Divisive Approach, Bottom up/Divisive Approach. Intuition behind PCA, Math behind PCA, Eigen Vector, Eigen Values, Finding Optimal Number of Features, PCA on Images, Reproducing Images.
Time Series Analysis
Introduction to Time Series, Stationary and Non Stationary, AutoCorrelation, Rolling Forecast, Exponential Forecast, Autoregressive Moving Average (ARMA) Models, Autoregressive Integrated Moving Average (ARIMA) Models, Financial Time Series, Auto Regressive Conditional Heteroscedasticity (ARCH) Models, Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) Models, Vector Auto Regressive (VAR) Models, RNN and LSTM.
TensorFlow and Keras
Introduction to TensorFlow, Constants, Session, Variables, Placeholder, MNIST Data, Initialising Weights and Biases, Forward Propagation, Cost Function, Running the Optimiser, How does the Optimiser work?, Running Multiple Iterations, Batch Gradient Descent. Introduction to Keras, Flow of code in Keras, Kera Models, Layers, Compiling the model, Fitting Training Data in Keras, Evaluations & Predictions
Deep Learning
Why do we need Neural Networks, Example with Linear Decision Boundary, Finding NonLinear Decision Boundary, Neural Network Terminology, No of Parameters in Neural Network, Forward and Backward Propagation, Cost Function, How to handle Multiclass classification,, Error Function in Gradient descent, Derivative of Sigmoid Function, Math behind Backpropagation, Implementing a simple Neural Network, Implementing a general Neural Network. Problem in Handling images, Convolution Neural Networks, Stride and Padding, Channels, Pooling Layer, Data Flow in CNN, Architecture of CNN, Initializing weights, Forward Propagation in TensorFlow, Convolution and Maxpool Functions, Regularization using Dropout layer, Adding Dropout Layer to the network, Building CNN Keras. Building ML Models for sequential Data, Recurrent Neural Networks, How does RNN work, Typical RNN Structures, Airline Data Analysis, Preparing Data for RNN, Setting up the RNN model, Analysing the Output, Vanishing or Exploiting Gradients, Gated Recurrent Units, Variations of the GRU, LSTM.
Natural Language Processing (NLP)
Using Words as Features, Basics of word processing, Stemming, Part of Speech, Lemmatization, Building Feature set, Classification using NLTK Naïve Bayes, Using Sklearn classifiers within NLTK, Count vectorizer, Sklearn Classifiers, Ngram, TFIDF.
Big Data
Python integration with Hadoop MapReduce and Spark, Introduction to Big Data and Hadoop Ecosystem, HDFS and Hadoop Architecture, MapReduce and Sqoop, Basics of Impala and Hive, Working with Hive and Impala, Type of Data Formats, Advanced HIVE concept and Data File Partitioning, Apache Flume and HBase, Apache Pig, Basics of Apache Spark, RDDs in Spark, Implementation of Spark Applications, Spark Parallel Processing, Spark RDD Optimization Techniques, Spark Algorithm, Spark SQL
Cloud Computing
Cloud Computing Fundamentals, Traditional IT Infrastructure, Cloud Infrastructure, Cloud Companies (IBM, Microsoft Azure, GCP, AWS ) & their Cloud Services, Use Cases of Cloud computing, Overview of Cloud Deployment, Models Implementation in Cloud.
Tableau
Introduction to Data Visualization, Introduction to Tableau, Connect to and Transform Data, Basic Charts and Dashboard, Descriptive Statistics, Dimensions and Measures, Explore and Analyse, Create Content, Dashboard Design & Principles, Advanced Design Components, Special Chart Types.
Special Topics
Mock interview/Industry mentor guidance sessions
Workshops for building your resume and LinkedIn / GitHub profiles
Curated interview problems
Our Learners !
The faculty is very supportive and always solve our doubts by 1:1 doubt resolution in a very easy way.
Bhagyashree Bharule
This course helped me rise in my career from Basics to the expertise level.
Sankalp Srivastava
With this program I have gained practical knowledge of how Machine Learning concepts are applied in real time work settings.
Sarika Santosh Jadhav
Frequently Asked Questions
What is Full Stack Program?
Online program with live concept classes and live doubt sessions toto get job ready
Is this program beginner friendly ?
Program is designed for both non coding and experienced students. You will be able to learn from fundamental to intermediate level.
Why should I do Full Stack Program?
These learning skills are in high demand and offers a clear edge over others when applying for top rated job roles. One may find it easy to learn these high level skills by taking up this program and to get the right guidance from industry experts.
What are the strengths of YBIF programs?
Rigorous Program with Live Classes, Study Material, Assignments, Guided Projects, InClass Quiz, Weekly Assessment, Real World Problems, Doubt Sessions, Career Counselling, Interview Preparation. Earn Badges, Certificate and Recommendations.
Who can apply for YBIF Full Stack Programs?
Any student pursuing in any year of B.Tech/BCA/BBA or M.Tech/MCA/MBA or any diploma/degree course can apply anytime for the internship program by visiting our website.
Is this live training or a recorded sessions?
Concept classes are live instructor led that enables you to ask questions and participate in discussion during session. We do provide recordings of sessions for future reference.
What are the system requirements for attending class?
We highly recommend our students to use laptop/desktop for an optimal class experience. However you can attend the class using any device which supports browser.
Who can I contact if I have question about enrolment?
Contact program advisor at support@ybifoundation.org or WhatsApp at (+91) 9667987711
100% Guaranteed Placement Program : Zero to Mastery
Six Months Guaranteed Placement Program with Zero Upfront Fee and Zero Fee After Placement