Full Stack Program
Data science is a blend of machine learning, mathematics, programming tools and data handling, to find out the hidden insights or patterns from data. With the amount of data that is being generated companies from all domains, be it Manufacturing, Finance, Marketing, Retail, IT or Bank, all are looking for Data Scientists. So learn the most desired and rewarding skill of the century. This course provides complete learning in terms of understanding the concepts, mastering them thoroughly, and applying them in real life.
12 Weeks Duration
Weekly Learning Badge
Live Concept Classes
Live Doubt Support
Full Stack Program Overview
Live Concept Classes
Live Doubt Sessions
Real World Projects
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What you will learn
Learn in-depth concepts, fundamentals, and application with hands-on problem solving.
No prior coding experience, beginner friendly, students from all academic years and branch of study can join.
UPGRADE to GPP to learn all required skills and specialize in your chosen industry with placement guaranteed.
Full Stack 3 Months Journey
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
Sharable on Social Media to boost your resume
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 Pre-processing, Grouping, Merge, Broadcasting
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 Analysis with Excel
Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering, Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc. Charts, Pie charts, Scatter and bubble chartsBar charts, Column charts, Line charts, Maps. Binary Classification Problems, Confusion Matrix, AUC and ROC curve Multiple Classification Problems. Standardization, Normalization, Probability Distributions Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation, Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression, Probability, Entropy, Dependence Mutual Information.
The basics of coding on R studio platform, Inputs and R objects (vector, matrix, dataframes and factors) R datatypes, Using dplyr package, Text manipulations using String, Reading data (csv file), Data Visualization with ggplot, Supervised ad Unsupervised Modelling, H2O, Lubridate, Caret.
Encoding, Scaling with Normalization and Min Max Scaling, Outlier Correction, Missing Values, Polynomial Variables etc. Unstructured Data, Feature Extraction, Feature Engineering, Bias Variance Trade-off, Unbalanced Data,
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.
Linear Programming, Solver, Optimization Concepts.
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, R-Square, 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, Multi-class 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, f-support, support, accuracy etc. Confusion matrix to evaluate the true positive, true negative, false positive and false negative outcomes in the model.
Bagging, Boosting, Random Forest, AdaBoost (Adaptive Boosting), Gradient boosting, Hyperparameter Tunning, Cross Validation, Grid Search and more
Unsupervised Machine Learning
Clustering, K-means, 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.
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
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,
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, N-gram, TF-IDF, 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/ Bi-LSTM/ GRU
20+ Guided projects
Our Learners !
The sessions were very informative and I had a great time taking part in it. Our Mentor was really helpful and supportive.
The Projects gave me hands on experience and increased my understanding of the concepts.
YBI Foundation is really an amazing platform for enhancing technical knowledge and practicing it with Hands-On projetcs.
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, In-Class 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 firstname.lastname@example.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
#1 Guaranteed Placement Program specially designed for Beginners and Freshers
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Solved end-to-end Projects
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