Machine Learning and Data Science with Python

Learn how to use Machine Learning, Data Science, Matplotlib , NumPy, Plotly , Scikit-Learn , Tensorflow , Pandas, Seaborn , and more!

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Machine Learning and Data Science with Python Masterclass 2021

Learn how to use Machine Learning, Data Science, Matplotlib , NumPy, Plotly , Scikit-Learn , Tensorflow , Pandas, Seaborn , and more!

This is an Introduction to Machine Learning and Data Science with Python Masterclass

After you finish this Introductory Course, you can enroll in our Comprehensive Machine Learning and Data Science with Python Masterclass Course.

https://firsteacademy.com/home/course/machine-learning-and-data-science-with-python-masterclass/18

You will learn the following:

This Machine Learning with Python course dives into the basics of machine learning using Python. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

In this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner.

Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job.

We’ll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib +

NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library.

Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work.

NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery.

Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

The course covers 5 main areas:

1: PYTHON FOR DS+ML COURSE INTRO

This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles.

Intro to Data Science + Machine Learning with Python

Data Science Industry and Marketplace

Data Science Job Opportunities

How To Get a Data Science Job

Machine Learning Concepts & Algorithms

2: PYTHON DATA ANALYSIS/VISUALIZATION

This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training.

Python Crash Course

NumPy Data Analysis

Pandas Data Analysis

Matplotlib

Seaborn

Plotly

3: MATHEMATICS FOR DATA SCIENCE

This section gives you a full introduction to the mathematics for data science such as statistics and probability.

Descriptive Statistics

Measure of Variability

Inferential Statistics

Probability

Hypothesis Testing

4: MACHINE LEARNING

This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training.

Intro to Machine Learning

Data Preprocessing

Linear Regression

Logistic Regression

K-Nearest Neighbors

Decision Trees

Ensemble Learning

Support Vector Machines

K-Means Clustering

PCA

5: STARTING A DATA SCIENCE CAREER

This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training.

Creating a Resume

Creating a Cover Letter

Personal Branding

Freelancing + Freelance websites

Importance of Having a Website

Networking

By the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up.

Here is some of What you’ll learn:

  • 140+ Python Machine Learning and Data Science videos
  • Use Python for Data Science and Machine Learning
  • Use Spark for Big Data Analysis
  • Implement Machine Learning Algorithms
  • Learn to use NumPy for Numerical Data
  • Learn to use Pandas for Data Analysis
  • Learn to use Matplotlib for Python Plotting
  • Learn to use Seaborn for statistical plots
  • Use Plotly for interactive dynamic visualizations
  • Use SciKit-Learn for Machine Learning Tasks
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forest and Decision Trees
  • Natural Language Processing and Spam Filters
  • Neural Networks
  • Support Vector Machines

Thank you very much for getting started with Master Python for Machine Learning and Data Science.”

We imagine you are going to love what’s next. Go Hack’m

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REMEMBER… You will get lifetime access to over 140 lectures.

So, what are you waiting for? Click the buy button NOW, increase your knowledge, become a Professional Machine Learning Expert and advance your career all in a fun and practical way!

Don’t miss this Limited Time Offer. ACT NOW!

 

You will Learn by Practice:

By the end of this Unique Course, you will go from #Newbie to #Advanced as an #Python_Programmer. Here is what you’ll learn:

1 Introduction

  1. Who is this course for
  2. Data science + machine learning marketplace
  3. Data science job opportunities
  4. Data science job roles
  5. What is data scientist
  6. How to get a data science job
  7. Data science projects overview

2 Data Science and Machine Learning Concepts

  1. Why we use python
  2. What is data science
  3. What is machine learning
  4. Machine learning concepts & algorithms
  5. What is deep learning
  6. Machine learning vs deep learning

3 Python For Data Science

  1. What is programming
  2. Why python for data concepts
  3. What is jupyter
  4. What is google colab
  5. Python variables, Booleans and none
  6. Getting started with google colab
  7. Python operators
  8. Python numbers & Booleans
  9. Python strings
  10. Python conditional statements
  11. Python for loops and while loops
  12. Python lists
  13. More about lists
  14. Python tuples
  15. Python dictionaries
  16. Python sets
  17. Compound datatypes & when to use each one
  18. Python functions
  19. Object oriented programming in python

4 Statistics for Data Science

  1. Intro to statistics
  2. Descriptive statistics
  3. Measure of variability
  4. Measure of variability continued
  5. Measure of variability relationship
  6. Inferential statistics
  7. Measure of asymmetry
  8. Sampling distribution

5 Probability & Hypothesis Testing

  1. What is exactly is probability
  2. Expected values
  3. Relative frequency
  4. Hypothesis testing overview

6 NumPy Data Analysis

  1. Intro numpy array datatypes ( 1.1 NumPy Basics  PDF )
  2. Numpy arrays
  3. Numpy arrays basics
  4. Numpy array Indexing
  5. Numpy array computations
  6. Broadcasting

7 Pandas Data Analysis

  1. Introduction to pandas (1.1 Pandas & 1.2 Pandas Basics PDF )
  2. Introduction to pandas continues

8 Python Data Visualization

  1. Data Visualization Overview
  2. Different Data Visualization Libraries in Python
  3. Python Data Visualization Implementation

9 Machine Learning

  1. Introduction To Machine Learning (1.1 Supervised Learning PDF )

10 Data Loading & Exploration

  1. Exploratory Data Analysis

11 Data Cleaning

  1. Feature scaling
  2. Data cleaning

12 Feature Selecting and Engineering

  1. Feature Engineering

13 Linear and Logistic Regression

  1. Linear Regression Intro
  2. Gradient Descent
  3. Linear Regression + Correlation Methods
  4. Linear Regression Implementation
  5. Logistic Regression

14 K Nearest Neighbors

  1. KNN Overview
  2. Parametric vs non-parametric models
  3. EDA on Iris Dataset
  4. The KNN Intuition
  5. Implement the KNN algorithm from scratch
  6. Compare the result with the sklearn library
  7. Hyperparameter tuning using the cross-validation
  8. The decision boundary visualization
  9. Manhattan vs Euclidean Distance
  10. Feature scaling in KNN
  11. Curse of dimensionality
  12. KNN use cases
  13. KNN pros and cons

15 Decision Trees

  1. Decision Trees Section Overview
  2. EDA on Adult Dataset
  3. What is Entropy and Information Gain
  4. The Decision Tree ID3 algorithm from scratch Part 1
  5. The Decision Tree ID3 algorithm from scratch Part 2
  6. The Decision Tree ID3 algorithm from scratch Part 3
  7. ID3 – Putting Everything Together
  8. Evaluating our ID3 implementation
  9. Compare with Sklearn implementation
  10. Visualizing the tree
  11. Plot the features importance
  12. Decision Trees Hyper-parameters
  13. Pruning
  14. [Optional] Gain Ration
  15. Decision Trees Pros and Cons
  16. [Project] Predict whether income exceeds $50Kyr – Overview

16 Ensemble Learning and Random Forests

  1. Ensemble Learning Section Overview
  2. What is Ensemble Learning
  3. What is Bootstrap Sampling
  4. What is Bagging
  5. Out-of-Bag Error (OOB Error)
  6. Implementing Random Forests from scratch Part 1
  7. Implementing Random Forests from scratch Part 2
  8. Compare with sklearn implementation
  9. Random Forests Hyper-Parameters
  10. Random Forests Pros and Cons
  11. What is Boosting
  12. AdaBoost Part 1
  13. AdaBoost Part 2

17 Support Vector Machines

  1. SVM Outline
  2. SVM intuition
  3. Hard vs Soft Margins
  4. C hyper-parameter
  5. Kernel Trick
  6. SVM – Kernel Types
  7. SVM with Linear Dataset (Iris)
  8. SVM with Non-linear Dataset
  9. SVM with Regression
  10. SMV – Project Overview

18 K-means

  1. Unsupervised Machine Learning Intro
  2. Unsupervised Machine Learning Continued
  3. Representing Clusters

19 PCA

  1. PCA Section Overview
  2. What is PCA
  3. PCA Drawbacks
  4. PCA Algorithm Steps (Mathematics)
  5. Covariance Matrix vs SVD
  6. PCA – Main Applications
  7. PCA – Image Compression
  8. PCA – Data Preprocessing
  9. PCA – Biplot and the Screen Plot
  10. PCA – Feature Scaling and Screen Plot
  11. PCA – Supervised vs Unsupervised
  12. PCA – Visualization

20 Data Science Career

  1. Creating A Data Science Resume
  2. Data Science Cover Letter
  3. How to Contact Recruiters
  4. Getting Started with Freelancing
  5. Top Freelance Websites
  6. Personal Branding
  7. Networking Do’s and Don’ts
  8. Importance of a Website

21 Additional Content: Grand Finale

  1. Bonus Lectures. Enjoy the Benefits

You could also end up using these skills in your work for Your #Clients, and much more.

 

We really hope you find this course valuable, but either way, please leave a review and share your experience…

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