Data Science dan Machine Learning dengan Python

Data Science

Data Science dan Machine Learning dengan Python

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admin

Last Update:

November 19, 2024

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About Course

Kursus Data Science dan Machine Learning dengan Python ini akan membimbing Anda dari dasar-dasar Data Science hingga ke teknik Machine Learning dan Deep Learning yang lebih canggih. Pelajari Python, visualisasi data, dan bangun model prediktif menggunakan pustaka populer seperti NumPy, Pandas, dan Keras. Cocok untuk pemula dan mereka yang ingin memperdalam keterampilan Data Science.

About Course

Kursus ini dirancang untuk memberikan pemahaman mendalam tentang Data Science dan Machine Learning menggunakan Python. Peserta akan memulai dengan konsep dasar Data Science, kemudian mempelajari pemrograman Python, termasuk pustaka-pustaka penting seperti NumPy dan Pandas. Kursus ini juga mencakup teknik visualisasi data, serta berbagai algoritma Machine Learning mulai dari Regresi Linear, Regresi Logistik, Decision Tree, Random Forest, hingga Support Vector Machines. Selain itu, peserta akan diperkenalkan pada Deep Learning dan mengimplementasikan model menggunakan Keras. Kursus diakhiri dengan proyek Data Science, yang mencakup seluruh tahapan mulai dari persiapan data, analisis, pemasangan model, hingga evaluasi model akhir. Kursus ini cocok bagi pemula yang ingin memulai perjalanan di dunia Data Science maupun bagi mereka yang ingin memperdalam keterampilan Machine Learning mereka.

Dasar-dasar Data Science dan Python untuk Data Science
Pustaka Python seperti NumPy dan Pandas untuk analisis data
Teknik visualisasi data menggunakan Matplotlib dan Seaborn
Algoritma Machine Learning seperti Regresi Linear, Regresi Logistik, Decision Tree, Random Forest, SVM, dan KNN
Teknik clustering dengan K-Means
Pengenalan Deep Learning dan implementasi menggunakan Keras
Pembuatan model Neural Network, termasuk LSTM
Penyempurnaan Hyperparameter untuk meningkatkan performa model
Proyek Data Science dari tahap persiapan data hingga evaluasi model akhir

Course Content

1. Dasar-Dasar Data Science

1.1 Data Science Basics Theory

10:57
1.2 Python Basics Data Types

11:17
1.3 Python Basics Operator

18:44
1.4 Numpy Arrays

13:53
1.5 Numpy Operations

05:01
1.6 Intro to Pandas

19:14
1.7 Missing Data, Group By, Operations

16:10
1.8 Operations, Data Input Output, Exercises

03:29
1.9 Matplotlib Basics

12:31

2. Pengenalan Kursus

2.1 Introduction to the Course

01:51
2.2 Overview of Data Science & Python for Data Science

07:25
2.3 Getting Started with Google Colab

04:47
2.4 Variables and Data Types in Python

09:45
2.5 Operators in Python

05:53
2.6 Control Structures in Python

06:57
2.7 Functions in Python

07:50
2.8 NumPy Basics

04:34
2.9 Arrays and Array Operations in NumPy

05:02
2.10 Introduction to Pandas

01:18
2.11 DataFrames in Pandas

01:36
2.12 Data Cleaning & Preprocessing with Pandas

01:42
2.13 Data Manipulation with Pandas

02:46

3. Visualisasi Data

4. Dasar-Dasar Machine Learning

5. Regresi Linear

6. Regresi Logistik

7. Decision Trees

8. Random Forests

9. Support Vector Machines (SVM)

10. K-Nearest Neighbors (KNN)

11. Clustering Algorithms

12. Pengenalan Deep Learning

13. Data Science Project

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Instructor
admin
Language
English

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Dasar-Dasar Data Science
Dasar-Dasar Pemrograman Python
Pengenalan Pandas
Visualisasi Data
Dasar-Dasar Machine Learning
Regresi Linear
Regresi Logistik
Decision Tree
Random Forest
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Algoritma Clustering
Pengenalan Deep Learning
Proyek Data Science

Data Science dan Machine Learning dengan Python

Data Science dan Machine Learning dengan Python

Teacher

Last Update:

Review:

About Course

About Course

What Will You Learn?

Course Content

1. Dasar-Dasar Data Science

1.1 Data Science Basics Theory

1.2 Python Basics Data Types

1.3 Python Basics Operator

1.4 Numpy Arrays

1.5 Numpy Operations

1.6 Intro to Pandas

1.7 Missing Data, Group By, Operations

1.8 Operations, Data Input Output, Exercises

1.9 Matplotlib Basics

2. Pengenalan Kursus

2.1 Introduction to the Course

2.2 Overview of Data Science & Python for Data Science

2.3 Getting Started with Google Colab

2.4 Variables and Data Types in Python

2.5 Operators in Python

2.6 Control Structures in Python

2.7 Functions in Python

2.8 NumPy Basics

2.9 Arrays and Array Operations in NumPy

2.10 Introduction to Pandas

2.11 DataFrames in Pandas

2.12 Data Cleaning & Preprocessing with Pandas

2.13 Data Manipulation with Pandas

3. Visualisasi Data

3.1 Introduction to Data Visualization

3.2 Line Plots and Scatter Plots

3.3 Bar Plots and Histograms

3.4 Pie Charts and Box Plots

3.5 Heatmaps and Pair Plots

3.6 Boxplots and Violinplots with Seaborn

4. Dasar-Dasar Machine Learning

4.1 Introduction to Machine Learning

4.2 Supervised Learning vs Unsupervised Learning

5. Regresi Linear

5.1 Linear Regression – Introduction and Data Preparation

5.2 Linear Regression – Model Fitting and Evaluation

5.3 Linear Regression – Feature Selection and Regularization

5.4 Linear Regression – Cross Validation and Performance Tuning

6. Regresi Logistik

6.1 Logistic Regression – Introduction and Data Preparation

6.2 Logistic Regression – Model Fitting and Evaluation

6.3 Logistic Regression – Feature Selection and Regularization

6.4 Logistic Regression – Cross Validation and Performance Tuning

7. Decision Trees

7.1 Decision Trees – Introduction and Data Preparation

7.2 Decision Trees – Model Fitting and Evaluation

7.3 Decision Trees – Feature Selection and Regularization

7.4 Decision Trees – Cross Validation and Performance Tuning

8. Random Forests

8.1 Random Forests – Introduction and Data Preparation

8.2 Random Forests – Introduction and Data Preparation 2

8.3 Random Forests – Feature Selection and Regularization

8.4 Random Forests – Cross Validation and Performance Tuning

9. Support Vector Machines (SVM)

9.1 Support Vector Machines – Introduction and Data Preparation

9.2 Support Vector Machines – Model Fitting and Evaluation

9.3 Support Vector Machines – Feature Selection and Regularization

9.4 Support Vector Machines – Cross Validation and Performance Tuning

10. K-Nearest Neighbors (KNN)

10.1 K-Nearest Neighbors – Introduction and Data Preparation

10.2 K-Nearest Neighbors – Model Fitting and Evaluation

10.3 K-Nearest Neighbors – Feature Selection and Regularization

10.4 K-Nearest Neighbors – Cross Validation and Performance Tuning

11. Clustering Algorithms

11.1 Clustering Algorithms – Introduction and Data Preparation

11.2 K-Means Clustering – Model Fitting and Evaluation

11.3 K-Means Clustering – Performance Evaluation and Tuning

12. Pengenalan Deep Learning

12.1 Introduction to Deep Learning (NN Basics, Activation Functions)

12.2 Introduction to Keras