Data Science Real World Projects in Python
- Description
- Curriculum
- FAQ
- Reviews
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Are you looking to land a top-paying job in Data Science?
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Or are you a seasoned AI practitioner who want to take your career to the next level?
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Or are you an aspiring data scientist who wants to get Hands-on Data Science and Artificial Intelligence?
If the answer is yes to any of these questions, then this course is for you!
Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, Airlines, Logistic and technology.
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 Price of Airlines Industry : Develop an AI model to predict Fare of Airlines at various Routes.
2.Task #2 @Predict the strength of a Password: Predict the category of Password whether it is Strong, Good or Weak.
3.Task #3 @Predict Prices of a Stock: Develop time series forecasting models to predict future Stock prices.
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.
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2
Introduction to Business Problem & Dataset -
3
Understanding Data & data-preprocessing
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4
Extract Derived Features from Data
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5
Perform Data Pre-processing
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6
Handle Categorical Data & Feature Encoding
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7
Perform Label Encoding on data
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8
How to handle Outliers in Data
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9
Select best Features using Feature Selection Technique
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10
Intuition Behind Random Forest Part-1
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11
Intuition Behind Random Forest Part-2
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12
Applying Random Forest on Data & Automate predictions
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13
Intuition Behind Decision Tree- Part 1
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14
Intuition Behind Decision Tree- Part 2
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15
Intuition Behind Decision Tree- Part 3
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16
Intuition Behind Decision Tree- Part 4
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17
Intuition Behind Decision Tree- Part 5
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18
Play with multiple Algorithms & dumping your model
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19
Intuition Behind Cross Validation- Part 1
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20
Intuition Behind Cross Validation- Part 2
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21
How to Cross Validate your model
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22
Introduction to Business Problem & Dataset
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23
Exploring your data
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24
Intuition behind TF-IDF --part 1
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25
Intuition behind TF-IDF --part 2
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26
Apply TF-IDF on data
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27
Intuition behind Logistic Regression --part 1
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28
Intuition behind Logistic Regression --part 2
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29
Apply Logistic Regression on Data
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30
Checking Accuracy of Model
