Machine Learning & Data Science in Python For Beginners

Learn Supervised & Unsupervised ML, Machine Learning Process, Models, Python, NumPy, Pandas, Seaborn, Data Visualisation

What you'll learn
What is Machine Learning
Supervised Machine Learning
Unsupervised Machine Learning
Semi-Supervised Machine Learning
Types of Supervised Learning: Classification
Regression
Types of Unsupervised Learning: Clustering
Association
Data Collection
Data Preparing
Selection of a Model
Data Training and Evaluation
HPT in Machine Learning
Prediction in ML
DPP in ML
Need of DPP
Steps in DPP
Python Libraries
Missing, Encoding, and Splitting Data in ML
Python, Java, R,and C ++
How to install python and anaconda?
Interface of Jupyter Notebook
Mathematics in Python
Euler's Number and Variables
Degree into Radians and Radians into Degrees in Python
Printing Functions in Python
Feature Scaling for ML
How to Select Features for ML
Filter Method
LDA in ML
Chi Square Method
Forward Selection
Training and Testing Data Set for ML
Selection of Final Model
ML Applications
Practical Skills in ML: Mastery
Process of ML
What is Extension in ML
ML Tradeoff
ML Variance Error
Logistic Regression
Data Visualization
Pandas and Seaborn-Library for ML

Requirements
No requirements, you will learn everything from scratch
Internet connection, laptop, or mobile phone
Passion towards learning data science and Machine learning

Description
Get instant access to a 69-page Machine Learning workbook containing all the reference material

Over 9 hours of clear and concise step-by-step instructions, practical lessons, and engagement

Introduce yourself to our community of students in this course and tell us your goals

Encouragement & celebration of your progress: 25%, 50%, 75%, and then 100% when you get your certificate

What will you get from doing this course?

This course will help you develop Machine Learning skills for solving real-life problems in the new digital world. Machine Learning combines computer science and statistics to analyse raw real-time data, identify trends, and make predictions. You will explore key techniques and tools to build Machine Learning solutions for businesses.

You don’t need to have any technical knowledge to learn these skills.

What will you learn

What is Machine Learning

Supervised Machine Learning

Unsupervised Machine Learning

Semi-Supervised Machine Learning

Types of Supervised Learning: Classification

Regression

Types of Unsupervised Learning: Clustering

Association

Data Collection

Data Preparing

Selection of a Model

Data Training and Evaluation

HPT in Machine Learning

Prediction in ML

DPP in ML

Need of DPP

Steps in DPP

Python Libraries

Missing, Encoding, and Splitting Data in ML

Python, Java, R,and C ++

How to install python and anaconda?

Interface of Jupyter Notebook

Mathematics in Python

Euler's Number and Variables

Degree into Radians and Radians into Degrees in Python

Printing Functions in Python

Feature Scaling for ML

How to Select Features for ML

Filter Method

LDA in ML

Chi-Square Method

Forward Selection

Training and Testing Data Set for ML

Selection of Final Model

ML Applications

Practical Skills in ML: Mastery

Process of ML

What is Extension in ML

ML Tradeoff

ML Variance Error

Logistic Regression

Data Visualization

Pandas and Seaborn-Library for ML

…and more!

Contents and Overview

You'll start with the What is Machine Learning; Supervised Machine Learning; Unsupervised Machine Learning; Semi-Supervised Machine Learning; Example of Supervised Machine Learning; Example of Un-Supervised Machine Learning; Example of Semi-Supervised Machine Learning; Types of Supervised Learning: Classification; Regression; Types of Unsupervised Learning: Clustering; Association.

Then you will learn about Data Collection; Data Preparation; Selection of a Model; Data Training and Evaluation; HPT in Machine Learning; Prediction in ML; DPP in ML; Need of DPP; Steps in DPP; Python Libraries; Missing, Encoding, and Splitting Data in ML.

We will also cover Feature Scaling for ML; How to Select Features for ML; Filter Method; LDA in ML; Chi Square Method; Forward Selection; Training and Testing Data Set for ML; Selection of Final Model; ML Applications; Practical Skills in ML: Mastery; Process of ML; What is Extension in ML; ML Tradeoff; ML Variance Error; What is Regression; Logistic Regression.

This course will also tackle Python, Java, R,and C ++; How to install python and anaconda?; Interface of Jupyter Notebook; Mathematics in Python; Euler's Number and Variables; Degree into Radians and Radians into Degrees in Python; Printing Functions in Python.

This course will also discuss Random Selection; Random Array in Python; Random Array and Scattering; Scattering Plot; Jupyter Notebook Setup and Problem; Random Array in Python; Printing Several Function in Python; Exponential and Logarithmic Function in Python.

Next, you will learn about Simple Line Graph with Matplotlib; Color Scheme with Matplotlib; Dot and Dashed Graph; Scattering 1-Data visualization; Labelling-Data Visualization; Color Processing-Data Visualization; Seaborn Scatter Plot; Import DataFrame by Pandas.

Who are the Instructors?

Allah Dittah from Tech 100 is your lead instructor – a professional making a living from his teaching skills with expertise in Machine Learning. He has joined with content creator Peter Alkema to bring you this amazing new course.

We can't wait to see you on the course!

Enrol now, and master Machine Learning!

Peter and Allah

Who this course is for
For beginners and professional as well
Searching jobs in data science and machine learning
For those who want to practice python, data science, and machine learning at the same time