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Data Science and Artificial Intelligence in Tamil

This course is specially designed for Data Science aspirants to enhance Statistics, Machine Learning & Domain skills.

Course Currilcum

    • Knowing JAP 00:00:00
    • Elaborating JAP 00:00:00
    • All about Chalo India Startup Drive 00:00:00
    • Data and its importance 00:00:00
    • What is data science? 00:00:00
    • Prerequisites of data science 00:00:00
    • Life cycle of data science? 00:00:00
    • Roles and skills needed in data science 00:00:00
    • Why python for data science & introduction to python language? 00:00:00
    • Python Install and setup and introduction to IDE ,Google Colab 00:00:00
    • Overview into Data Structures like list, tuple, set, dict, Series, Data Frame, Array 00:00:00
    • Operator 00:00:00
    • Practice on List, Tuple, Set, Dict, Series, Data Frame & Array 00:00:00
    • Slicing and casting 00:00:00
    • If , Else 00:00:00
    • Functions. Lambda Function 00:00:00
    • For Loop, While Loop, 00:00:00
    • Introduction to OOPS, 00:00:00
    • OOPs vs POP, Types of OOPs, 00:00:00
    • Practical Implementation of OOPS 00:00:00
    • Ai vs Ds vs Ml Vs Dl vs NLP vs CV 00:00:00
    • Use of libraries & libraries in data science 00:00:00
    • Introduction to Pandas and Basic Pandas Commands 00:00:00
    • Pandas Practice 00:00:00
    • Numpy Commands 00:00:00
    • Data Visualization Theory 00:00:00
    • Practice on Data Visualization with Matplotlib 00:00:00
    • Practice on Data Visualization with Seaborn & Plotly. 00:00:00
    • What is statistics? 00:00:00
    • Where to use it in ML? 00:00:00
    • Data and its properties. 00:00:00
    • Different types of analytics 00:00:00
    • Random variables and its types 00:00:00
    • Population vs sample 00:00:00
    • Central limit theorem- Mean median mode 00:00:00
    • Gaussian distribution/Normal distribution 00:00:00
    • Left & Right skewed distribution, kurtosis 00:00:00
    • Use case of Distribution. 00:00:00
    • Hypothesis Testing, 00:00:00
    • Statistical Testing using ANOVA, T-test, 2 Sample, P-Value, Chi-Square Test, F-Statistics, 00:00:00
    • Types of machine learning 00:00:00
    • Linear regression theory 00:00:00
    • Overfitting, Underfitting, Generalized model 00:00:00
    • Ridge & Lasso theory 00:00:00
    • Logistic regression theory 00:00:00
    • -Code practice 00:00:00
    • SVM theory 00:00:00
    • -Code project 00:00:00
    • KNN theory 00:00:00
    • K means 00:00:00
    • Hierarchical 00:00:00
    • DBSCAN 00:00:00
    • Naive Bayes theory 00:00:00
    • Code practice 00:00:00
    • Code Practice 1 00:00:00
    • Decision Tree theory 00:00:00
    • Decision Tree Part 2 00:00:00
    • Random Forest 00:00:00
    • ADABOOST 00:00:00
    • Gradient boosting 00:00:00
    • Code Practice 2 00:00:00
    • classification- recall, precision, F1 score,Confusion matrix, Accuracy 00:00:00
    • regression-MSE,MAE,RMSE,RMLE, R squared,R2 squared 00:00:00
    • XG boost 00:00:00
    • XG boost code practice 00:00:00
    • What is data preprocessing and why it is important 00:00:00
    • Outlier detection , removal and replace. 00:00:00
    • Null and NaN value removal and replace. 00:00:00
    • What is feature Engineering 00:00:00
    • Encoding Techniques part 1 00:00:00
    • Encoding Technique Part 2 00:00:00
    • Quiz -4 00:10:00
    • Feature Scaling 00:00:00
    • Feature Selection 00:00:00
    • Feature selection part -2 00:00:00
    • What is imbalance data, and its impacts,Why data balance is important 00:00:00
    • Handling imbalance data. 00:00:00
    • What and Why tuning is important 00:00:00
    • Hyperparameter steps with Sklearn 00:00:00
    • ML model saving , predticing and retraining 00:00:00
    • What is EDA, EDA before and after 00:00:00
    • Why automation is need in EDA, Use of automation 00:00:00
    • Mito 00:00:00
    • Finding outliers in dataset using Z score & IQR 00:00:00
    • 5 number summary – IQR 00:00:00
    • Pearson correlation coefficient 00:00:00
    • Confidence Interval, Estimates 00:00:00
    • Kernal density estimation 00:00:00
    • Q – Q plot 00:00:00
    • Box cox transform 00:00:00
    • Permutation, combination 00:00:00
    • Quiz 3 00:08:00
    • Introduction to VScode 00:00:00
    • Use of VScode in Data Science 00:00:00
    • Why is a Web application needed? 00:00:00
    • Approach in making web application? 00:00:00
    • Introduction to Flask 00:00:00
    • HTML, CSS basics 00:00:00
    • Flask Framework and Syntax 00:00:00
    • Flask Framework and Syntax part-2 00:00:00
    • Introduction to Git 00:00:00
    • Use of Git 00:00:00
    • Introduction to Heroku 00:00:00
    • flask bulk prediction 00:00:00
    • Introduction to streamlit 00:00:00
    • Streamlit use case and Syntax 00:00:00
    • -Deployment to streamlit 00:00:00
    • streamlit one on one predition 00:00:00
    • Intro to django 00:00:00
    • Django Syntex and advantages 00:00:00
    • django deployment in heroku 00:00:00
    • django bulk prediction 00:00:00
    • Introduction to AWS 00:00:00
    • Deployment to AWS 00:00:00
    • Django deployment in Aws 00:00:00
    • wha is database, sql vs nosql 00:00:00
    • flask database 00:00:00
    • django database 00:00:00
    • MongoDB 00:00:00
    • MongoDB in flask 00:00:00
    • Quiz 2 00:10:00
    • MongoDB in Django 00:00:00
    • Deep learning Introduction 00:00:00
    • Difference between Deep learning and machine learning 00:00:00
    • ANN 00:00:00
    • ANN part-2 00:00:00
    • ANN part-3 00:00:00
    • Code Practice 3 00:00:00
    • CNN 00:00:00
    • CNN part -2 00:00:00
    • CNN part -3 00:00:00
    • RNN 00:00:00
    • RNN part -2 00:00:00
    • RNN part -3 00:00:00
    • Nlp basics and working principle 00:00:00
    • NLTK 00:00:00
    • What is Time Series 00:00:00
    • Time series code practice 00:00:00
    • machine learning projects part-1 00:00:00
    • machine learning projects part-2 00:00:00
    • deep learning projects part-1 00:00:00
    • deep learning projects part-2 00:00:00
    • NLP learning projects part-1 00:00:00
    • Time series projects part -1 00:00:00
    • What is Mlops 00:00:00
    • Use of Mlops 00:00:00
    • Introduction to HADOOP 00:00:00
    • HADOOP Distributed File System 00:00:00
    • HADOOP Yarn 00:00:00
    • Hadoop- MapReduce Practical Assignments 00:00:00
    • Hadoop MapReduce 00:00:00
    • Hadoop Installation and Map Reduce Job Execution DEMO 00:00:00
    • Step by Step Guide : Hadoop 3.3.0 installation and MapReduce Job Execution on Windows 00:00:00
    • Step by step Guide Ubuntu (Linux) virtual machine installation on windows and configure Hadoop 00:00:00
    • Map Reduce – Test your Knowledge 00:03:00
    • Hadoop Introduction Quiz 00:02:00
    • SQL-Structured Query Language 00:00:00
    • Apache HIVE – Hadoop Ecosystem Tool 00:00:00
    • Step by Step Guide : Apache HIVE installation using Cygwin on Windows & External Table Creation 00:00:00
    • Apache HIVE-Practical Assignment -Part 1 00:00:00
    • Apache HIVE-Practical Assignment -Part 1-Continued 00:00:00
    • Apache HBase -Hadoop Ecosystem Tool 00:00:00
    • Step by Step Guide : Apache Hbase installation on Windows & Table Creation 00:00:00
    • Apache HBase Practical Assignment 00:00:00
    • Hadoop Eco System- Other Tools 00:00:00
    • Hadoop – Test your knowledge 00:04:00
    • Apache Spark -Session 00:00:00
    • Apache Spark Programming Guide 00:00:00
    • Apache Kafka Stream processing and DEMO 00:00:00
    • Apache Spark Streaming 00:00:00
    • Python for Apache Spark – pySpark 00:00:00
    • Big Data and Cloud Computing 00:00:00
    • Microsoft Azure Fundamentals and Overview 00:00:00
    • How to Create a New Microsoft Azure Account 00:00:00
    • Azure Subscription Policies ,Resource Groups and Storage Account 00:00:00
    • QUIZ – 5 00:10:00
    • Microsoft Azure- Working with Data Storage 00:00:00
    • Microsoft Azure – DataBricks 00:00:00
    • Microsoft Azure- Working with Databricks 00:00:00
    • Microsoft Azure- Cosmos DB 00:00:00
    • Microsoft Azure SQL 00:00:00
    • Microsoft Azure Synapse Analytics and PolyBase 00:00:00
    • Microsoft Azure Stream Analytics and event hubs 00:00:00
    • Microsoft Azure Data Factory Service – ADF 00:00:00
    • Microsoft Azure Creating a Virtual Machine 00:00:00
    • Microsoft Azure – Monitoring and Troubleshooting Data Storage and Processing 00:00:00
    • Microsoft Azure : Data Engineer Associate Certification Path- Part 1 00:00:00
    • Microsoft Azure : Data Engineer Associate Certification Path- Part 2 00:00:00
    • FINAL TEST Unlimited
    • Congrats ! Submit the course completion form. 00:00:00
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