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Course Curriculum

Job Acceleration Program (JAP)
Knowing JAP 00:00:00
Elaborating JAP 00:00:00
Chalo INDIA Startup Drive
All about Chalo India Startup Drive 00:00:00
Introduction
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
PYTHON- Programming language
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
Data science Library
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
Statistics
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
Machine Learning
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
Machine Learning Part-2
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
Data preprocessing
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
Data preprocessing part-2
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
EDA
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
Statistics part-2
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
Web application
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
FLask(one on one)
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)
flask bulk prediction 00:00:00
Streamlit(bulk prediction)
Introduction to streamlit 00:00:00
Streamlit use case and Syntax 00:00:00
-Deployment to streamlit 00:00:00
Streamlit (one on one)
streamlit one on one predition 00:00:00
Django(one on one)
Intro to django 00:00:00
Django Syntex and advantages 00:00:00
django deployment in heroku 00:00:00
django (bulk prediciton)
django bulk prediction 00:00:00
AWS
Introduction to AWS 00:00:00
Deployment to AWS 00:00:00
Django deployment in Aws 00:00:00
SQl
wha is database, sql vs nosql 00:00:00
flask database 00:00:00
django database 00:00:00
MongoDB
MongoDB 00:00:00
MongoDB in flask 00:00:00
Quiz 2 00:10:00
MongoDB in Django 00:00:00
Deep learning
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
Nlp basics and working principle 00:00:00
NLTK 00:00:00
Time Series
What is Time Series 00:00:00
Time series code practice 00:00:00
Machine Learning Project
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
MLOps
What is Mlops 00:00:00
Use of Mlops 00:00:00
HADOOP
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 ā€“ 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 ā€“ Test your knowledge 00:04:00
Spark
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 ļæ½ļæ½ļæ½ pySpark 00:00:00
Getting into Azure
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
Starting with Azure
Microsoft Azure- Working with Data Storage 00:00:00
Microsoft Azure ā€“ 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 ļæ½ļæ½ļæ½ ADF 00:00:00
Microsoft Azure Creating a Virtual Machine 00:00:00
Microsoft Azure ļæ½ļæ½ļæ½ 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
QUIZ SECTION
Bonus Section
FINAL TEST Unlimited
Congrats ! Submit the course completion form. 00:00:00
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