Trycle | Python for Data Science and Machine Learning

Course Overview

Globally, the machine learning market is expected to grow twofold by 2022. Machine learning underlies many of the services we use today, including things like speech recognition and recommendations from online shopping platforms to suggesting music recommendations. This course is designed for any beginner to understand and have a clear understanding of what Machine learning is. Within the course you will also work with many real-world samples. The primary topics this course will cover are:

Python Programming from Basics
Jupyter Notebook, Numpy, Pandas, Matplotlib
Machine learning with Scikit Learn
A Complete Data Science and Machine Learning Project.

Why is Machine learning important and an exciting field to study?

Better Career Opportunities and Growth
Highly lucrative job opportunities
Specialized skills boosting salary hikes

1. What are the requirements?

A Computer or Laptop with 4 GB RAM and an Internet connection.

2. What am I going to get from this course?

Will guide You to become a confident Data Scientist and Machine Learning Engineer with clearcut knowledge in all the fundamentals.
Make You confident to handle Projects in Machine Learnig and Data Science.

3. Who this course is for?

Anyone with average knowledge of using Computer and Internet and having a little Logic and Mathematical skills.

About the Author

ratings

675 students enrolled

reviews 13

courses
Fairoos O K, is a highly experienced Computer Engineer, with most experience in Python, Java, and Android Developments. Also an online tutor, having students from countries like the US, UK, and Germany. Currently, practising as a Data Scientist using Python Programming.

Course Curriculum

Section 1: PYTHON PROGRAMMING

Introduction
Preview

Why We choose Python?
How to Install Python?
Preview
What is a Module?
Preview
py Files
Preview
For Loops
For Loop Samples
Strings, Int and Input
If, Else and Elif
While Loops
While Infinite Loops
While Loop Sample
Functions
Global and Local Variables
Variables
Data Types and Operators
String Operations, Special Operators
List in Python
Tuple and Set
Dictionary
Class and Objects
File Handling
Exception Handling
Working with JSON
Conclusion

Section 2: NUMPY, PANDAS AND MATPLOTLIB

Introduction

Introduction to NUMPY, PANDAS AND MATPLOTLIB
Jupyter Notebook
Numpy Module

Course Materials: All the Jupyter Notebooks of classes and Exercises are uploaded in my github page, All csv and excel sheets of data also included in corresponding folders. In Github By Clicking on the code button and selecting Download ZIP, You can download the entire course materials to your system.

Github Link for Course Material
Numpy Arrays

Course Materials: All the Jupyter Notebooks of classes and Exercises are uploaded in my github page, All csv and excel sheets of data also included in corresponding folders. In Github By Clicking on the code button and selecting Download ZIP, You can download the entire course materials to your system.

Github Link: https://github.com/FairoosOk/Zero2Hero
Pandas Introduction

Course Materials: All the Jupyter Notebooks of classes and Exercises are uploaded in my github page, All csv and excel sheets of data also included in corresponding folders. In Github By Clicking on the code button and selecting Download ZIP, You can download the entire course materials to your system.

Github Link: Https://Github.Com/FairoosOk/Zero2Hero
Data Frame Creation and Views
Data Frame Operations
Creating Data Frames
Read Write CSV Files
Read Write EXCEL Files
Handling Missing Data - Filna
Interpolate and Dropna
Replace Functions
Group By
Concat and Merge
Matplotlib Introduction
Format Strings in Plot Function
Labels, Legend and Grid
Bar Charts
Histograms
Pie Chart and Save Plot Images
Conclusion

Section 3: MACHINE LEARNING AND SCIKIT LEARN

Introduction
Linear Regression

Course Materials: All the Jupyter Notebooks of classes and Exercises are uploaded in my github page, All csv and excel sheets of data also included in corresponding folders. In Github By Clicking on the code button and selecting Download ZIP, You can download the entire course materials to your system. Github Link: Https://Github.Com/FairoosOk/Zero2Hero
Linear Regression Multivariate
How Gradient Descent
Gradient Descent Implementation
Save and Load Model
Dummy Variables
One Hot Encoding
Train Test Split
Logistic Regression with Logit Function
Logistic Regression Binary Classification
Logistic Regression Multiclass
Confusion Matrix
How Decision Tree
Decision Tree Implementation
Random Forest
Support Vector Machine
SVM Classifier (SVC)
K- Fold Cross Validation
K- Fold and Parameter Tuning
K- Means Clustering
K- Means Implementation
Conclusion

Section 4: DATA SCIENCE AND MACHINE LEARNING PROJECT

Introduction
Data Cleaning
Feature Engineering
Outlier Removal
Outlier Removal Contnd
Model Building
Model Export
Pycharm and VSC Editors
Python Flask Server
Flask Server Codes
Flask Server Running
Website UI
Conclusion

Section 5: Course Conclusion

reviews

- chethana chethana
- 1 year ago
- Diyon Mathew
  
  Just started, it explains very well and comparatively easier than expected to understand.
- 1 year ago
- Abu Sheriff
- 1 year ago
- shefeena K.I
  
  very nice
- 1 year ago
- Iqbal Thalayad
  
  I liked the course which included step-by-step exercises and tutorials. Very well organized and conceived. I recommend this course who wish to start the journey with machine learning. Thank you Trycle and Fairoos sir.
- 1 year ago
- Febin Mubarak
- 1 year ago
- Nirmal Krishna
  
  The presentation was good.Explanations were quite lucid for the most part.Recommended for absolute beginners to Python.
- 1 year ago
- ELDHOSE JOHN
  
  Best for a beginner looking for a career in data science.
- 10 months ago
- Ananya B
- 9 months ago
- Athul S
- 9 months ago
- ANJUSHA KRISHNADAS
- 7 months ago
- Nandakumar PR
- 5 months ago
- Sreejesh N G
- 3 months ago