Overview

Embark on a comprehensive 14-hour journey through data science and machine learning using Python and R. Explore essential concepts like supervised learning, linear regression, principal component analysis, and computer vision. Master practical skills in model selection, evaluation metrics, and hyperparameter tuning. Dive into clustering techniques, deep learning applications in finance and medical science, and learn to handle real-world challenges like imbalanced datasets and missing data. Gain insights into the complete lifecycle of data science projects, career transition strategies, and interview preparation. Apply your knowledge to diverse domains, from stock sentiment analysis to credit card fraud detection and disease prediction. Develop proficiency in tools like PySpark, sklearn, and Flask for model deployment. By the end of this extensive tutorial series, you'll be equipped with the skills to tackle complex data science problems and advance your career in this rapidly evolving field.

Syllabus

What is Machine Learning in Data Science- Machine Learning Tutorial with Python and R-Part 1.
What is Supervised Machine Learning- Machine Learning Tutorial with Python and R-Part 2.
Anaconda installation with Packages- Machine Learning Tutorial with Python and R-Part 3.
Important libraries used in python Data Science- Machine Learning Tutorial with Python and R-Part 4.
PySpark Tutorial for Beginners | Apache Spark with Python -Linear Regression Algorithm.
Principle Component Analysis (PCA) using sklearn and python.
Computer Vision using Microsoft Cognitive Services for Images.
How to select the best model using cross validation in python.
TPR,FPR,FNR,TNR, Confusion Matrix.
Precision, Recall and F1-Score.
Artificial Neural Network for Customer's Exit Prediction from Bank.
GridSearchCV- Select the best hyperparameter for any Classification Model.
RandomizedSearchCV- Select the best hyperparameter for any Classification Model.
K Means Clustering Intuition.
Hierarchical Clustering intuition.
Complete Life Cycle of a Data Science Project.
How we can apply Machine Learning in Finance.
Deep Learning in Medical Science.
Setting up Raspberry pi 3 B+.
How to switch your career to Data Science..
Linear Regression Mathematical Intuition.
Handle Categorical features using Python.
DBSCAN Clustering Easily Explained with Implementation.
Curse of Dimensionality Easily explained| Machine Learning.
Feature Selection Techniques Easily Explained | Machine Learning.
Cross Validation using sklearn and python | Machine Learning.
Handling Missing Data Easily Explained| Machine Learning.
Deploy Machine Learning Model using Flask.
Deployment of Deep Learning Model using Flask.
How to Visualize Multiple Linear Regression in python.
Predicting Heart Disease using Machine Learning.
Predicting Lungs Disease using Deep Learning.
Stock Sentiment Analysis using News Headlines.
Random Forest(Bootstrap Aggregation) Easily Explained.
Voting Classifier(Hard Voting and Soft Voting Classifier).
Credit Card Fraud Detection using Machine Learning from Kaggle.
Hyperparameter Optimization for Xgboost.
Tutorial 45-Handling imbalanced Dataset using python- Part 1.
Tutorial 46-Handling imbalanced Dataset using python- Part 2.
DNA Sequencing Classifier using Machine Learning.
Credit card Risk Assessment using Machine Learning.
Why, How and When to Scale Features in Machine Learning?.
How to choose number of hidden layers and nodes in Neural Network.
Diabetes Prediction using Machine Learning from Kaggle.
How to Read Dataset in Google Colab from Google Drive.
Malaria Disease Detection using Deep Learning.
Python Application to Track Amazon Product Prices.
What is Cross Validation and its types?.
Train Test Split vs K Fold vs Stratified K fold Cross Validation.
My Path on Becoming a Data Scientist- Motivation.
Complete Life Cycle of a Data Science Project.
Step By Step Transition Towards Data Science.
What should be your Salary Expectation as a Data Scientist?.
How to Crack Data Science Interviews- Motivations.
The Role of Maths in Data Science and How to Learn?.
Tutorial 42 - Ensemble: What is Bagging (Bootstrap Aggregation)?.
Tutorial 43-Random Forest Classifier and Regressor.
Important Tools and Libraries Used By Data Scientist.
How To Apply Data Science In Your Domain?.
Skills Required To Become A Data Analyst and a Data Scientist.
How To Become Expertise in Exploratory Data Analysis.
How to Prepare For Data Science Interviews.
Why and When Should we Perform Feature Normalization?.
Flask Vs Django and When Should You Use What?.
Top 5 Python IDEs For Data Science.
Perform Web Scraping On Wikipedia- Data Science.