What you would learn in Machine Learning & Data Science in Python For Beginners course?
You will have instant access to a one-page Machine Learning workbook containing all the necessary reference materials.
Nine hours worth of concise and clear step-by-step directions as well as practical instruction and engaging
Introduce yourself to the community of students taking this class, and let us know your plans for the future.
A celebration and encouragement for your achievements 25%, 50, 25%, 75%, and finally, the total amount when you receive your certificate
What will you gain from this class?
This course will allow you to build Machine Learning skills for solving real-world problems in the digital age. Machine Learning combines computer science and statistics to analyze raw data in real-time, discover patterns, and make predictions. Learn about the essential techniques and tools that can be used to create Machine Learning solutions for businesses.
It is not necessary to have any technical expertise to master these abilities.
What you need to know:
What exactly is Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Semi-Supervised Machine Learning
Different types of supervised learning Classification
Regression
The types of learning that are unsupervised Types of Unsupervised Learning: Clustering
Association
Data Collection
Data Preparation
The Model to be selected
Information Training, Evaluation, and Assessment
HPT is a Machine Learning
Prediction in ML
DPP in ML
The need for DPP
Steps to be taken DPP
Python Libraries
Data Loss, Encoding, and Separating Data in ML
Python, Java, R, and C ++
How do I install Python and anaconda?
The interface of the Jupyter Notebook
Mathematics in Python
Euler's Variables and Number
Degrees into Radians, or Radians in Degrees Python
Printer Functions are available in Python
Features Scaling for ML
How do I select Features to be used in ML
Filter Method
LDA in the ML
Chi-Square Method
Forward Selection
Learning and Testing Data set for ML
The Selection of the Model Model
ML Applications
Practice Skills for ML Proficiency
The process of ML
What is Extension in ML?
Tradeoffs between ML
ML Variance Error
Logistic Regression
Data Visualization
Pandas and the Seaborn-Library ML
...and more!
Content of the Course:
- What exactly is Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-Supervised Machine Learning
- Different types of supervised learning Classification
- Regression
- Different types of unsupervised learning The most common is clustering.
- Association
- Data Collection
- Data Preparation
- Selecting a Model
- Information Training, Evaluation, and Assessment
- HPT is a Machine Learning
- Prediction in ML
- DPP in ML
- The need for DPP
- Steps to be taken DPP
- Python Libraries
- Data Loss, Encoding, and Encoding, Missing, and
- Python, Java, R, and C ++
- How to install Python and anaconda?
- The interface of the Jupyter Notebook
- Mathematics in Python
- The Euler Number as well as Variables
- Degrees to Radians as well as Radians to Degrees Python
- Printer Functions are available in Python
- Features Scaling for ML
- How do you select features to be used in ML
- Filter Method
- LDA in the ML
- Chi Square Method
- Forward Selection
- Learning and Testing Data set for ML
- The Selection of the Model Model
- ML Applications
- Practice Skills for ML The ability to master
- The process of ML
- What is Extension in ML?
- Tradeoff ML
- ML Variance Error
- Logistic Regression
- Data Visualization
- Pandas as well as Seaborn Library for ML
Download Machine Learning & Data Science in Python For Beginners from below links NOW!
