**Machine Learning and Data Science with Python: A Complete Beginners Guide**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 10h 19m | 5.28 GB

Machine Learning and Data Science for programming beginners using Python with Scikit-learn, SciPy, Matplotlib and Pandas

Artificial intelligence, machine learning, and deep learning neural networks are the most used terms nowadays in the technology world. They’re also the most misunderstood and confused terms too. Artificial intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine learning and neural networks are two subsets that comes under this vast machine learning platform. But in this course, we will focus mainly on machine learning. Throughout this course, we will prepare our machine to make it ready for a prediction test.

We will use Python as our programming language. Python is a great tool for the development of programs that perform data analysis and prediction. It has tons of classes and features that perform complex mathematical analyses and give solutions in simple one or two lines of code, so we don’t have to be a statistic genius or mathematical nerd to learn data science and machine learning.

Machine learning and data science jobs are the most lucrative in the technology arena nowadays. Learning this course will make you equipped to compete in this area.

Exhaustive and packed with step-by-step instructions, working examples, and helpful advice, this course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.

Learn

- Machine learning
- Data science using Python

1 Course Overview & Table of Contents

2 Code Password

3 System and Environment preparation

4 Learn Basics of python

5 Learn Basics of NumPy

6 Learn Basics of Matplotlib

7 Learn Basics of Pandas

8 Understanding the CSV data file

9 Load and Read CSV data file

10 Dataset Summary

11 Dataset Visualization

12 Data Preparation

13 Feature Selection

14 Refresher Session – The Mechanism of Re-sampling, Training and Testing

15 Algorithm Evaluation Techniques

16 Algorithm Evaluation Techniques

17 Classification Algorithm Spot Check – Logistic Regression

18 Classification Algorithm Spot Check – Linear Discriminant Analysis

19 Classification Algorithm Spot Check – K-Nearest Neighbors

20 Classification Algorithm Spot Check – Naive Bayes

21 Classification Algorithm Spot Check – CART

22 Classification Algorithm Spot Check – Support Vector Machines

23 Regression Algorithm Spot Check – Linear Regression

24 Regression Algorithm Spot Check – Ridge Regression

25 Regression Algorithm Spot Check – LASSO Linear Regression

26 Regression Algorithm Spot Check – Elastic Net Regression

27 Regression Algorithm Spot Check – K-Nearest Neighbors

28 Regression Algorithm Spot Check – CART

29 Regression Algorithm Spot Check – Support Vector Machines (SVM)

30 Compare Algorithms – Part 1 Choosing the best Machine Learning Model

31 Compare Algorithms – Part 2 Choosing the best Machine Learning Model

32 Pipelines Data Preparation and Data Modelling

33 Pipelines Feature Selection and Data Modelling

34 Performance Improvement Ensembles – Voting

35 Performance Improvement Ensembles – Bagging

36 Performance Improvement Ensembles – Boosting

37 Performance Improvement Parameter Tuning using Grid Search

38 Performance Improvement Parameter Tuning using Random Search

39 Export, Save and Load Machine Learning Models Pickle

40 Export, Save and Load Machine Learning Models Joblib

41 Finalizing a Model – Introduction and Steps

42 Finalizing a Classification Model – The Pima Indian Diabetes Dataset

43 Quick Session Imbalanced Data Set – Issue Overview and Steps

44 Iris Dataset Finalizing Multi-Class Dataset

45 Finalizing a Regression Model – The Boston Housing Price Dataset

46 Real-time Predictions Using the Pima Indian Diabetes Classification Model

47 Real-time Predictions Using Iris Flowers Multi-Class Classification Dataset

48 Real-time Predictions Using the Boston Housing Regression Model

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