“Data Science and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. It is widely used in several sectors nowadays such as banking, healthcare technology etc..
As there are tonnes of courses on Machine Learning already available over Internet , this is not One of them..
The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.
1.Task #1 @Predict Ratings of Application : Develop an Machine Learning model to predict Ratings of Play-store applications.
2.Task #2 @Predict Rent of an apartment : Predict the Rent of an apartment using machine learning Regression algorithms..
3.Task #3 @Predict Sales of a Super-market: Develop an Machine Learning model to predict sales of a Super-Market..
Why should you take this Course?
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It explains Projects on real Data and real-world Problems. No toy data! This is the simplest & best way to become a Data Scientist/AI Engineer/ ML Engineer
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It shows and explains the full real-world Data. Starting with importing messy data, cleaning data, merging and concatenating data, grouping and aggregating data, Exploratory Data Analysis through to preparing and processing data for Statistics, Machine Learning , NLP & Time Series and Data Presentation.
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It gives you plenty of opportunities to practice and code on your own. Learning by doing.
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In real-world projects, coding and the business side of things are equally important. This is probably the only course that teaches both: in-depth Python Coding and Big-Picture Thinking like How you can come up with a conclusion
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Guaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee.
Who this course is for:
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Data Scientists who want to apply their knowledge on Real World Case Studies
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Data Analyst who want to get more Practical Assignments..
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Machine Learning Enthusiasts who look to add more projects to their Portfolio
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10
Introduction to Problem Statement -
11
How to access Datasets & Resources
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12
Understand the big Idea- how to collect data !
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13
Perform descriptive analysis on Data !
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14
Perform Exploratory Data Analysis to understand Patterns
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15
How to Automate your code !
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16
Automate your data Visualisation code ..
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17
Understand Hidden patterns from data..
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18
Analyse whether Google is Bias or not !
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19
Analysing distrbution of Ratings
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20
Perform Data Preparation for Analysing App Category
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21
Analysing Android version of data
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22
Lets Perform Data Cleaning..
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23
Lets Clean & ready our Rating & Installs feature
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24
Perform Data-Preparation on Size Feature..
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25
Perform Feature Selection algorithms to select important features
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26
How Feature selection works..
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27
What are outliers & how to find it..
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28
Outliers Detection using IQR..
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29
Outlier Detection in Install feature
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30
How to Impute Outliers
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31
what is Data Transformation
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32
What are Missing Values & how to fill Missing values ?
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33
What is Data Discretization & how to apply it in real-world ?
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34
What is Mean Encoding & how to apply it in real world?
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35
What is Target Guided Mean Encoding ?
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36
What is Label Encoding & how to apply it in real-world
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37
Applying Label Encoding & preparing your data for Data Modelling
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38
Intuition behind Logistic Regression-part 1
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39
Intuition behind Logistic Regression-part 2
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40
Building Logistic Regression Model
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41
Intuition Behind Decision Trees - Part 1
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42
Intuition Behind Decision Trees - Part 2
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43
Intuition Behind Decision Trees - Part 3
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44
Intuition Behind Decision Trees - Part 4
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45
Intuition Behind Decision Trees - Part 5
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46
Intuition Behind Decision Trees - Part 6
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47
Intuition Behind Random Forest - Part 1
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48
Intuition Behind Random Forest - Part 2
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49
Hypertune your Logistic Regression Model
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50
Hypertune your Random Forest Model
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51
Datasets & Resources
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52
How to load data & fill missing values in data !
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53
Fix Missing values of Data !
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54
How to fill Missing values using Random Value Imputation
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55
Perform Wordcloud Analysis
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56
Lets Clean Description Feature
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57
Lets Prepare Description Feature using nltk !
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58
Perform Unigram , bigram & trigram analysis..
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59
Perform GeoSpatial Analysis
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60
Obtaining label distribution of data
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61
how to visualize outliers..
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62
Imputing the outliers..
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63
Perform In-depth analysis on data
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64
Extract important features using Co-relation..
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65
Most suitable Feature encoding technique In real-world ?
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66
Lets pre-process our data for Feature Encoding..
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67
Automate your Data Preparation stuffs !
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68
What is Frequency Encoding & how to apply it in Real-World ?
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69
Lets Build a Decision Tree Model
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70
Playing with Multiple Algorithms..
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71
Lets Hypertune our model..
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72
Datasets & Resources
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73
Lets Prepare our Data..
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74
Finding co-relation values of a matrix..
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75
Define own function to understand our Data !
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76
Perform In-Depth Analysis !
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77
Finding relationship in data !
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78
Lets explore our data !
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79
Data Preparation for Modelling !
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80
Build a Machine learning Model..
Lessons:
