Python
Introduction to Python, Why Python, Variables, Data Types, Type castings, Strings, Indexing15:03
Operators and Conditional Statements, Looping Statements and it’s Control Statement00:00
Lambda Functions, *args, **kwargs, Functions00:00
Data Structures – List, Tuple and List Comprehensions00:00
Data Structures – Set and Dictionaries00:00
Classes, Objects and Constructors, Inheritance00:00
Polymorphism, Abstraction and Encapsulation00:00
Connecting to Databases, Establishing connections to databases, Executing SQL Queries, ORM (Object-Relational Mapping), Working with NoSQL Databases00:00
Introduction of Numpy, and Pandas00:00
Introduction of Seaborn and Matplotlib00:00
Statistics
Introduction to Statistics, Descriptive Statistics, Sample, Population, Major of Central Tendency, Standard Deviation,00:00
Variance, Range, IQR, Outliers, Correlation, Covariance Skewness, Kurtosis, Probability00:00
Probability, Probability distributions, Central Limit Theorem, Binomial and Poisson Distribution00:00
Normal Distribution, Type I & Type II Error00:00
T-test, Z-test, Hypothesis, Testing Interview Questions00:00
Machine Learning
Introduction to ML, Types of variables, Encoding, Normalization, Standardization, Types of ML, Linear Regression00:00
Linear Regression, Logistic Regression, SVM, KNN, Naïve Bayes, Decision Tree, Random Forest00:00
Mean Absolute Error, Mean and Root Mean Square Error, Confusion Matrix, R2 Score, Adjusted R2 Score,F1 Score00:00
Classification Report, AUC ROC, Accuracy, Ensemble Techniques, Random Forest, Xgboost00:00
Unsupervised Machine Learning, PCA, Clustering, k-Means Clustering and Hierarchical clustering00:00
Deep Learning
Introduction to Neural Network, Foreward Propagation, Activation Function00:00
Activation Function(Linear, Sigmoid, Relu, Leaky Relu), Optimizers, Gradient Descent, Stochastics Gradient Descent00:00
Mini batch Gradient Descent, Adagrad, Padding, Pooling, Convolution00:00
Checkpoints and Neural Networks Implementation and Introduction to Time Series Analysis,00:00
Various components of the TSA, Decomposition Method (Additive Method and Multiplicative)00:00
ARMA and ARIMA00:00
R- Programming
Introduction to R, Installing R and RStudio, Basics of RStudio IDE, Writing and executing R scripts, Variables and Data Type in R, Operators00:00
Creating vectors, Vector indexing and slicing, Vectorized operations, Creating matrices, Matrix operations, Matrix indexing, Creating lists, Creating data frames, Indexing and manipulating lists and data frames00:00
Conditional statements, Loops, Applying functions, Flow Control, Functions in R, Object-Oriented Programming in R, S3 and S4 classes, Methods and inheritance, Creating and using objects00:00
Creating and using factors, Working with dates and times, Reading and writing(CSV files and Excel files), Introduction to the readr and readxl packages00:00
Introduction to dplyr, Selecting, filtering, and arranging data, Grouping data, Summarizing data with summarize and mutate00:00
Data Manipulation in R- dplyr, Data Manipulation & Data Visualization in R- tidyr00:00
Introduction to Text Mining, Text Preprocessing, Document-Term Matrix (DTM) and TF-IDF, Exploratory Text Analysis, Sentiment Analysis00:00
Install Necessary Packages, Create a New Package, Package Structure, Writing Functions, Documenting Functions, Testing Your Package, Building and Checking and Sharing Your Package00:00
Introduction to APIs, Using the ‘httr’ Package, GET & POST Request, and Authentication, Introduction to Web Scraping, Using the ‘rvest’ Package, Handling Dynamic Content, Handling Sessions and Cookies00:00
Connecting to Databases in R, Packages Installation, Connect to Database, Execute Queries, Write Data, Disconnect and Error Handling00:00
Project Session00:00
SQL
Basic of Database,Types of Database,Data Types, SQL Operators, Expression, Create, Insert00:00
Drop, Truncate, Delete, Alter, Update, Select, Range, Operater, IN,Wildcard, Like, Clause00:00
Constraint, Aggregation Function, Group by, Order by, Having00:00
Joins, Case, Complex Queries, Doubt Clearing00:00
Tableau
Tableau Desktop, Tableau products00:00
Data import, Measures, Filters00:00
Data transformation, Marks, Dual Axis00:00
Manage worksheets, Data visualization, Dashboarding,Project00:00
Power BI
Power BI Platform, Process Flow00:00
Features, Dataset, Bins00:00
Pivoting, Query Group, DAX Function00:00
Formula, Charts, Reports, Dashboards00:00
Advanced Power BI00:00
Project Session00:00
No-SQL
Introduction, SQL vs NoSQL, Data Model, Data types, Object ID, DAta type, Binary Data, Date, Null, Boolean, Integer, String00:00
Collection method, queries, CRUD Operation, Insert, Find, Update, Delete, Validate, Bulk write, Delete one00:00
Java
Introduction to the Java, Installation, Syntax main()/printIn()/print()/ Variable [String, Int, Boolean, float, char], Datatypes, Operators00:00
Conditions, Loop, Methods, Class, File Handling00:00
Introduction of BigData & Hadoop
Types of Data, Introduction to Bigdata(History, V’s of Bigdata, Advantages & Disadvantages of BigData ), Big Data Applications in Various Sectors, Introduction to Hadoop, Scaling (Horizontal and Vertical), Challenges in Scaling, Parallel Computing, Distributed Computing and Hadoop, Hadoop Tools Overview, Big Data Analytics Lifecycle00:00
On Premises Installation Oracle Virtual Box and setup of VM & Ubuntu, Basic Linux command, Download and Installation of Hadoop, Introduction to Hadoop, Core components of Hadoop, Hadoop working, Principle00:00
VM creation on Cloud (AZURE), Configuration & Insight to Single Node Hadoop Deployment(bsshrc, hadoop-env, core-site, hdfs-site, mapred-site, yarn-site) , Format HDFS Namenode.00:00
HDFS Architecture, Hadoop Commands and Implementation00:00
Mapreduce, Mapreduce Implementation00:00
Introduction to Hive, Hive Installation, Hive Implementaion00:00
Hive Query Language, SQL Opeartions00:00
HIVE_SQL Opeartions00:00
SPARK
Installation of Spark, PySpark, Introduction to Sqoop, Installation of Sqoop,00:00
PySpark Query, Installation of Hbase, Hbase Query00:00
PIG Installation and Query00:00
PIG Query, Oozie00:00
Flume and Doubt Clear00:00
Project Session00:00
Computer Vision
Introduction to Image Processing, Feature Detection, OpenCV00:00
Convolution, Padding, Pooling & its Mechanisms00:00
Forward Propagation & Backward Propagation for CNN00:00
CNN Architectures like AlexNet, VGGNet, InseptionNet, ResNet, Transfer Learning00:00
NLP
Introduction to Text Mining, Text Processing using Python and Introduction to NLTK00:00
Sentiment Analysis, Topic Modeling (LDA) and Name- Entity Recognition00:00
BERT (Bidirectional Encoder Representations from Transformers), Text Segmentation, Text Mining, Text Classification00:00
Automatic Speech Recognition, Introduction to Web Scraping00:00
RL
RL Framework, Component of RL Framework, Exampes of Systems00:00
RL Framework, Component of RL Framework, Exampes of Systems00:00
Types of RL Systems, Q-Learning00:00
Project Session00:00
Introduction to Gen AI & Huggingface transformers platform
Introduction to AI, Hype vs. Reality, Business Applications, Ethical Considerations, Introduction to Generative AI, From Text Generation to Multimodal Models, Potential and Challenges00:00
Introduction to open source Huggingface transformers platform, Review of NLP Basics & Text Pre-processing, Introduction to NLP Concepts: Language Representations, Tokenization, Part-of-Speech Tagging, Text Preprocessing00:00
Feature Engineering: Normalization, Stemming, Lemmatization, Stop Word Removal, Understanding key NLP Applications using Huggingface platform00:00
Sentiment analysis, Sentence classification, Generating text, Extracting an answer from text00:00
Language Models and Transformer Models
Understanding language models, Probability-based language models, Unsupervised learning language representations, Introduction to transformer models, What are transformer models00:00
Types of models: encoder –decoder, decoder only, Attention mechanism, Tasks that transformer models can do: translation, text summarization, Q&A, text generation, Zero shot, few shot text classification00:00
Large Language Models (LLMs)
Introduction to Large Language Models (LLMs), – Structure of popular models – Types of Models: text to text, text to image, text to video, multimodal00:00
Other types of Generative AI algorithms, – GANs ( Generative Adverserial Networks), – Variational Autoencoders (VAEs), Diffusion Models, Mixture of Experts, – Diffferent models available currently for image ( DALLE-2, Midjourney)00:00
Hands on practice of NLP tasks using Huggingface library and opensource language models such as Bloom for finetuning a LLM, zero and few shot classification, -Applications of Generative AI in business00:00
– Customer Insights & Sentiment Analysis – Personalized Marketing & Content Creation – Chatbots: Automating Customer Service and Support – Document Processing Automation00:00
Langchain, AI Application Stack and Ethical Considerations
Langchain, Applied use case for Gen AI – hands on exercise – Designing a custom chatbot – Data analytics using Gen AI model such as OpenAI API00:00
AI Application Stack: Infrastructure & foundation layer – Overview of AI infrastructure: cloud platforms, GPU, and distributed computing, – Setting up an AI environment for generative models – Infrastructure considerations for scalable AI applications – Retrieval augmentation generation or RAG00:00
Hallucination, Data Privacy, Ethics, and Environmental Impact of AI & future of Work – Importance of data privacy in AI applications – Ethical considerations in AI development and Deployment – Environmental Impact and Sustainability in AI – The Future of Work: How AI Will Reshape Roles and Responsibilities00:00
Project Session00:00
Orientation (Introduction to Data Science, Scope of Data Science)
