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Data Science and Machine Learning with Python

Course Reviews

Track :

Programming

Lessons no : 66

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What will you learn in this course?
  • Master data manipulation and analysis using Python libraries like Pandas and NumPy for real-world datasets
  • Implement machine learning algorithms such as regression, classification, and clustering with scikit-learn in Python
  • Apply big data tools like Hadoop, Hive, and Pig for scalable data processing and storage solutions
  • Develop predictive models by leveraging statistical techniques and probability concepts in Python and R
  • Utilize data visualization tools like Matplotlib and Seaborn to communicate insights effectively
  • Perform feature engineering and selection to improve machine learning model performance
  • Design and evaluate machine learning workflows for classification, regression, and clustering tasks
  • Integrate Python and R programming skills to automate data analysis and machine learning pipelines

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  • The Certificate is free !
Lessons | 66
What is Machine Learning in Data Science Machine Learning Tutorial with Python and R Part 1
00:10:01

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
00:11:42

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
00:05:18

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
00:08:31

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
00:18:11

PySpark Tutorial for Beginners | Apache Spark with Python Linear Regression Algorithm
Principle Component Analysis PCA using sklearn and python
00:12:30

Principle Component Analysis PCA using sklearn and python
Computer Vision using Microsoft Cognitive Services for Images
00:13:39

Computer Vision using Microsoft Cognitive Services for Images
How to select the best model using cross validation in python
00:15:33

How to select the best model using cross validation in python
TPR FPR FNR TNR Confusion Matrix
00:25:12

TPR FPR FNR TNR Confusion Matrix
Precision Recall and F1 Score
00:09:03

Precision Recall and F1 Score
Artificial Neural Network for Customer s Exit Prediction from Bank
00:23:05

Artificial Neural Network for Customer s Exit Prediction from Bank
GridSearchCV Select the best hyperparameter for any Classification Model
00:18:16

GridSearchCV Select the best hyperparameter for any Classification Model
RandomizedSearchCV Select the best hyperparameter for any Classification Model
00:12:56

RandomizedSearchCV Select the best hyperparameter for any Classification Model
K Means Clustering Intuition
00:14:36

K Means Clustering Intuition
Hierarchical Clustering intuition
00:06:12

Hierarchical Clustering intuition
Complete Life Cycle of a Data Science Project
00:15:44

Complete Life Cycle of a Data Science Project
How we can apply Machine Learning in Finance
00:13:12

How we can apply Machine Learning in Finance
Deep Learning in Medical Science
00:16:50

Deep Learning in Medical Science
Setting up Raspberry pi 3 B
00:11:31

Setting up Raspberry pi 3 B
How to switch your career to Data Science
00:21:59

How to switch your career to Data Science
Linear Regression Mathematical Intuition
00:24:17

Linear Regression Mathematical Intuition
Handle Categorical features using Python
00:18:37

Handle Categorical features using Python
DBSCAN Clustering Easily Explained with Implementation
00:18:32

DBSCAN Clustering Easily Explained with Implementation
Curse of Dimensionality Easily explained| Machine Learning
00:07:37

Curse of Dimensionality Easily explained| Machine Learning
Feature Selection Techniques Easily Explained | Machine Learning
00:23:01

Feature Selection Techniques Easily Explained | Machine Learning
Cross Validation using sklearn and python | Machine Learning
00:09:48

Cross Validation using sklearn and python | Machine Learning
Handling Missing Data Easily Explained| Machine Learning
00:23:22

Handling Missing Data Easily Explained| Machine Learning
Deploy Machine Learning Model using Flask
00:13:20

Deploy Machine Learning Model using Flask
Deployment of Deep Learning Model using Flask
00:07:33

Deployment of Deep Learning Model using Flask
How to Visualize Multiple Linear Regression in python
00:08:25

How to Visualize Multiple Linear Regression in python
Predicting Heart Disease using Machine Learning
00:11:49

Predicting Heart Disease using Machine Learning
Predicting Lungs Disease using Deep Learning
00:13:51

Predicting Lungs Disease using Deep Learning
Stock Sentiment Analysis using News Headlines
00:13:59

Stock Sentiment Analysis using News Headlines
Random Forest Bootstrap Aggregation Easily Explained
00:09:18

Random Forest Bootstrap Aggregation Easily Explained
Voting Classifier Hard Voting and Soft Voting Classifier
00:06:45

Voting Classifier Hard Voting and Soft Voting Classifier
Credit Card Fraud Detection using Machine Learning from Kaggle
00:18:34

Credit Card Fraud Detection using Machine Learning from Kaggle
Hyperparameter Optimization for Xgboost
00:14:55

Hyperparameter Optimization for Xgboost
Tutorial 45 Handling imbalanced Dataset using python Part 1
00:13:01

Tutorial 45 Handling imbalanced Dataset using python Part 1
Tutorial 46 Handling imbalanced Dataset using python Part 2
00:10:59

Tutorial 46 Handling imbalanced Dataset using python Part 2
DNA Sequencing Classifier using Machine Learning
00:17:18

DNA Sequencing Classifier using Machine Learning
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4.4
16 Reviews
Simphiwe Ngcamu 

IT WASINDEED ENLIGHTNING
2025-10-21

Praveena Chinthakunta 

The way of teaching is best
2025-10-08

GEOFFREY OTIENO 

5 stars
2025-10-04

RAKESH KUMAR 

The Data Science course on MindLuster was clear, practical, and beginner-friendly. It covered key concepts with simple explanations and useful examples, making it a great starting point for anyone interested in Data Science.
2025-09-27

Gleb Kamagin 

very good
2025-08-18

Taizyani Cheembela 

it was very hard but rewarding
2025-05-20

Sarthak Rajput 

good
2025-04-30

Haritha T 

thank you
2025-04-17

Shaik Abeed 

Excellent
2025-04-17

Harshal A 

Very helpful
2025-03-16

Charan B S 

it was great learning the course
2025-03-16

CHETHAN GOWDA S 

IT WAS GOOD
2025-03-16

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A data scientist needs to have skills to use big data tools like Hadoop, Hive and Pig, statistics, programming in Python, R, or Scala. Machine Learning Engineer needs to have skills such as computer science fundamentals, programming skills in Python or R, statistics and probability concepts, etc.