Machine Learning and Data Science with Python

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!

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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

8. Why we use python
9. What is data science
10. What is machine learning
11. Machine learning concepts & algorithms
12. What is deep learning
13. Machine learning vs deep learning

3 Python For Data Science

14. What is programming
15. Why python for data concepts
16. What is jupyter
17. What is google colab
18. Python variables, Booleans and none
19. Getting started with google colab
20. Python operators
21. Python numbers & Booleans
22. Python strings
23. Python conditional statements
24. Python for loops and while loops
25. Python lists
26. More about lists
27. Python tuples
28. Python dictionaries
29. Python sets
30. Compound datatypes & when to use each one
31. Python functions
32. Object oriented programming in python

4 Statistics for Data Science

33. Intro to statistics
34. Descriptive statistics
35. Measure of variability
36. Measure of variability continued
37. Measure of variability relationship
38. Inferential statistics
39. Measure of asymmetry
40. Sampling distribution

5 Probability & Hypothesis Testing

41. What is exactly is probability
42. Expected values
43. Relative frequency
44. Hypothesis testing overview

6 NumPy Data Analysis

45. Intro numpy array datatypes ( 1.1 NumPy Basics  PDF )
46. Numpy arrays
47. Numpy arrays basics
48. Numpy array Indexing
49. Numpy array computations
50. Broadcasting

7 Pandas Data Analysis

51. Introduction to pandas (1.1 Pandas & 1.2 Pandas Basics PDF )
52. Introduction to pandas continues

8 Python Data Visualization

53. Data Visualization Overview
54. Different Data Visualization Libraries in Python
55. Python Data Visualization Implementation

9 Machine Learning

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

10 Data Loading & Exploration

57. Exploratory Data Analysis

11 Data Cleaning

58. Feature scaling
59. Data cleaning

12 Feature Selecting and Engineering

60. Feature Engineering

13 Linear and Logistic Regression

61. Linear Regression Intro
62. Gradient Descent
63. Linear Regression + Correlation Methods
64. Linear Regression Implementation
65. Logistic Regression

14 K Nearest Neighbors

66. KNN Overview
67. Parametric vs non-parametric models
68. EDA on Iris Dataset
69. The KNN Intuition
70. Implement the KNN algorithm from scratch
71. Compare the result with the sklearn library
72. Hyperparameter tuning using the cross-validation
73. The decision boundary visualization
74. Manhattan vs Euclidean Distance
75. Feature scaling in KNN
76. Curse of dimensionality
77. KNN use cases
78. KNN pros and cons

15 Decision Trees

79. Decision Trees Section Overview
80. EDA on Adult Dataset
81. What is Entropy and Information Gain
82. The Decision Tree ID3 algorithm from scratch Part 1
83. The Decision Tree ID3 algorithm from scratch Part 2
84. The Decision Tree ID3 algorithm from scratch Part 3
85. ID3 – Putting Everything Together
86. Evaluating our ID3 implementation
87. Compare with Sklearn implementation
88. Visualizing the tree
89. Plot the features importance
90. Decision Trees Hyper-parameters
91. Pruning
92. [Optional] Gain Ration
93. Decision Trees Pros and Cons
94. [Project] Predict whether income exceeds $50Kyr – Overview

16 Ensemble Learning and Random Forests

95. Ensemble Learning Section Overview
96. What is Ensemble Learning
97. What is Bootstrap Sampling
98. What is Bagging
99. Out-of-Bag Error (OOB Error)
100. Implementing Random Forests from scratch Part 1
101. Implementing Random Forests from scratch Part 2
102. Compare with sklearn implementation
103. Random Forests Hyper-Parameters
104. Random Forests Pros and Cons
105. What is Boosting
106. AdaBoost Part 1
107. AdaBoost Part 2

17 Support Vector Machines

108. SVM Outline
109. SVM intuition
110. Hard vs Soft Margins
111. C hyper-parameter
112. Kernel Trick
113. SVM – Kernel Types
114. SVM with Linear Dataset (Iris)
115. SVM with Non-linear Dataset
116. SVM with Regression
117. SMV – Project Overview

18 K-means

118. Unsupervised Machine Learning Intro
119. Unsupervised Machine Learning Continued
120. Representing Clusters

19 PCA

121. PCA Section Overview
122. What is PCA
123. PCA Drawbacks
124. PCA Algorithm Steps (Mathematics)
125. Covariance Matrix vs SVD
126. PCA – Main Applications
127. PCA – Image Compression
128. PCA – Data Preprocessing
129. PCA – Biplot and the Screen Plot
130. PCA – Feature Scaling and Screen Plot
131. PCA – Supervised vs Unsupervised
132. PCA – Visualization

20 Data Science Career

133. Creating A Data Science Resume
134. Data Science Cover Letter
135. How to Contact Recruiters
136. Getting Started with Freelancing
137. Top Freelance Websites
138. Personal Branding
139. Networking Do’s and Don’ts
140. Importance of a Website

21 Additional Content: Grand Finale

141. 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…