Machine Learning with Python Data Science for Beginners training centre in Bangladesh

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EMI EASY PAY

Machine Learning with Python Data Science for Beginners

Introduction

In Python for Data Analysis course, we assume students are already familiar with Python programming and they will learn advanced Python techniques useful for load, wrangling, cleaning, transformation and visualization of data. You will learn about Machine Learning, Artificial Intelligence, Data Science, and different types of packages in this course.

What is this course about?
This course is tailored to impart knowledge on the fundamentals of Master Machine Learning using Python,Demystify Artificial Intelligence, Machine Learning, Data Science,ML Business Solution Blueprint,Explore Spyder, Pandas and NumPy,Implement Data Engineering and Data Analysis,Introduction to Statistics and Probability Distributions,Understand Supervised and Unsupervised Learning,Implement Simple & Multiple Linear Regression,Regression & Classification Model Evaluation,Cross Validation, Hyperparameter, Ensemble Modeling, Random Forest & XGBoost in this course.

Who will benefit from this course?
The booming demand for skilled data scientists across industries makes this course suited for all individuals at all level of experience. We recommend this data science training specially the following professionals:
1. Software professionals looking for a career switch in the field of analytics
2. Professionals working in field of Data and Business Analytics
3. Graduates looking to build a career in Analytics and Data Science
4. Anyone with a genuine interest in the field of Data Science

After completion of this training course, you will be able to:
This training has a clear focus on the vital concepts of business analytics and Python . By the end of the training, participants will be able to:
1. Work on data exploration, data visualization, and predictive modeling techniques with ease.
2. Gain fundamental knowledge on analytics and how it assists with decision making.
3. Master Machine Learning using Python
4. Demystifying Artificial Intelligence, Machine Learning, Data Science
5. Explore & Define a ML use case
6. ML Business Solution Blueprint
7. Implement Data Engineering
8. Exploratory Data Analysis
9. Introduction to Statistics and Probability Distributions
10. Learn Machine Learning Methodology
11. Understand basic and advanced NumPy (Numerical Python) features
12. Perform data analysis with tools in the Pandas library
13. Manipulate, process, transform, merge and reshape large volumes of data
14. Solve data analysis problems in web analytics, social sciences, finance, and economics
15. Measure data by points in time, specific instances, fixed periods, or intervals

What background do I need?
There is no prerequisite knowledge. But if you have basic math skills and basic to Intermediate Python Skills is preferable.

I am from a non-technical background. Will I benefit from this course?
Yes, the course presents both the business and technical benefits of Big Data analytics and Data Visualization. The data mining and technical discussions are at a level that attendees with a business background can understand and apply. Where technical knowledge is required, sufficient guidance for all backgrounds is provided to enable activities to be completed and the learning objectives achieved.

Methodology

PowerPoint Presentation,Handouts,Hands on Lab Practice, Brainstorming

Contents of Training:

Section: 1
Introduction
1. Introduction
2. Overview of Contents
3. The Bigger Picture
4. The Problem Landscape
5. Defining Data Science
6. Demystifying AI-ML-Data Science
7. Exploring the Data Scientist's Toolbox

Section: 2
Introduction to Data Scientist's Toolbox
8. Introduction to Data Scientist
9. Overview of Contents
10. Quick recap of Python
11. Python 2.7 vs Python 3.5
12. Installation & Setup
13. Datatypes Overview
14. Spyder tour
15. Datatypes demo
16. Datatypes- Numpy
16.1: Intro to numpy
16.2: Creating arrays
16.3: Using arrays and scalars
16.4: Indexing Arrays
16.5: Array Transposition
16.6: Universal Array Function
16.7: Array Processing
16.8: Array Input and Output

17. Datatypes-Pandas
17.1: Series
17.2: DataFrames
17.3: Index objects
17.4: Reindex
17.5: Drop Entry
17.6: Selecting Entries
17.7: Data Alignment
17.8: Rank and Sort
17.9: Summary Statistics
17.10: Missing Data
17.11: Index Hierarchy

18. Data Engineering
18.1: Reading and Writing Text Files
18.2: JSON with Python
18.3: HTML with Python
18.4: pip install beautifulsoup4
18.5: pip install lxml
18.6: Microsoft Excel files with Python

19. Data Engineering 2
19.1: Merge
19.2: Merge on Index
19.3: Concatenate
19.4: Combining DataFrames
19.5: Reshaping
19.6: Pivoting
19.7: Duplicates in DataFrames
19.8: Mapping
19.9: Replace
19.10: Rename Index
19.11: Binning
19.12: Outliers
19.13: Permutation

20. Data Engineering 3
20.1: GroupBy on DataFrames
20.2: GroupBy on Dict and Series
20.3: Aggregation
20.4: Splitting Applying and Combining
20.5: Cross Tabulation

21. Functions
22. Data Visualization
22.1: Installing Seaborn
22.2: Histograms
22.3: Kernel Density Estimate Plots
22.4: Combining Plot Styles
22.5: Box and Violin Plots
22.6: Regression Plots
22.7: Heatmaps and Clustered Matrices

Section: 3
Exploratory Data Analysis, Feature Engineering and Hypothesis Testing
23. Introduction to Exploratory Data Analysis
24. Overview of Contents
25. Machine Learning Methodology
26. Exploratory Data Analysis
27. Univariate Analysis
28. Univariate Analysis 2
29. Bivariate Analysis
30. Feature Engineering
31. Introduction to Statistics
32. Probability Distributions

Section: 4
Machine Learning
33. Introduction to Machine Learning
34. Overview of Contents
35. Introduction to Machine Learning
36. Supervised Learning
37. Simple & Multiple Linear Regression
38. Regression Demo
39. Classification - Logistic Regression
40. Classification Logistic Regression Demo
41. Decision Trees
42. Decision Trees Demo
43. Unsupervised Learning - Clustering
44. Unsupervised Learning Clustering Demo
45. Unsupervised Learning -Association Rules
46. Model Evaluation - Regression
47. Model Evaluation - Regression Demo
48. Model Evaluation - Classification
49. Model Evaluation - Classification Demo
50. Regularization & Hyperparameter tuning
51. Bias Variance Tradeoff
52. Cross Validation
53. Hyperparameter Tuning
54. Cross Validation , Hyperparameter Demo
55. Ensemble Modeling
56. Random Forest [Bagging]
57. XGBoost [Boosting]
58. RF & XGB Demo

Section: 5
Capstone Project
59. Project Overview
60. Overview of Contents
61. Project Use Case Overview
62. Defining the Problem Statement
63. Business Solution Blueprint
64. Explore & Define a ML use case
65. EDA and Feature Engineering
66. Approach for Model Development, Evaluation, Optimization
67. Storyboarding

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