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Datascience with Python Online Training

Datascience with Python Online Training
Datascience with Python Online Training

HIGHEST RATED 4/5

Short Description :: In technical terms, Python is an object-oriented, high-level programming language with integrated dynamic semantics primarily for web and app development. It is extremely attractive in the field of Rapid Application Development because it offers dynamic typing and dynamic binding options.

1)    Introduction to Data Science 

  • Importance
  • Prospects 
  • Data science tools & technologies 
  • What is Machine Learning? 
  • Why Machine Learning in Data Science 

2)    Understanding Data 

  • Types of data 
  • Raw data handling 
  • Processed or transformed data 
  • Decision making from data 
  • Statistics: Making sense of data 

3)    Statistics and Probability

  • Uni Variate analysis 
  • Measure of central tendency 
  • Mean – Median – Mode 
  • Range 
  • IQR 
  • Variance 
  • Standard deviation 
  • Correlation
  • ANOVA 

4)    Applied Maths
5)    Probability Distributions 

  • Skewness 
  • Kurtosis 
  • Gaussian distribution 
  • Multivariate Gaussian distributions 
  • Binomial distributions 
  • Poisson distributions 

6)    Machine learning Overview 

  • Supervised learning – regression, classification 
  • Unsupervised learning - clustering 
  • Reinforcement learning 

7)    Basics of python for data analysis 

  • Basics of python for data analysis
  • Datatypes in Python
  • Working with different datatypes
  • Important Packages in Python
  • Anaconda
  • Exploratory analysis in python using Pandas
  • Data Munging in Python using Pandas

8)    Dictionary 

  • Creating a Dictionary 
  • Accessing Values in Dictionary 
  • Updating Dictionary 
  • Delete Dictionary Elements 
  • Properties of Dictionary Keys 

9)    Tuples 

  • Creating a Tuples 
  • Accessing Values in Tuples: 
  • Updating Tuples 
  • Delete Tuple Elements 
  • Basic Tuples Operations 

10)    List 

  • Creating a list 
  • Accessing Values in Lists 
  • Updating Lists 
  • Delete List Elements 

11)     Numpy
12)    Pandas
13)    Matplotlib
14)    Seaborn
15)    The Math and Stats Packages
16)    Scikit-learn
17)    Regression 

  • Linear regression 
  • Hypothesis 
  • Gradient Descent 
  • Prediction 
  • Normalization 
  • Hands on 
  • Logistic regression 
  • Sigmoid function 
  • Decision Boundary 

18)    PCA
19)    AUC – ROC Curve
20)    Bias – Variance trade-off
21)    Overfitting and underfitting
22)    Classification evaluation matrics 
23)    Model evaluations 

  • Mean Squared Error 
  • K fold cross validation 
  • Accuracy, Precision, Recall 

24)    Tree based models 

  • What is a decision tree? 
  • Decision tree algorithms 
  • How does it work? 
  •  Implementation 

25)    Ensemble methods of trees based models 

  • Random forest 
  • What is random forest? 
  • Advantages of random forest 
  • Disadvantages of random forest 
  • Random forest implementation 

26)    K – Nearest Neighbor 

  • What is KNN algorithm? 
  • How to select appropriate k value? 
  • Calculating distance 
  • KNN algorithm – pros and cons 

27)    Cluster analysis 

  • Why clustering? 
  • K means clustering 
  • Number of clusters k=? 
  • Pros and cons 

28)    Support Vector Machines 

  • Overview 
  • Classification Using a Separating Hyperplane 
  • The Maximal Margin Classifier _Non-separable Case 
  • Support Vector Classifiers - Details 
  • Support Vector Machines - Classification with non-linear boundaries 

29)    Time Series Analysis –ARIMA
30)    Pickling
31)    Data visualization 

  • How to create a scatter plot? 
  • How to create a histogram? 
  • How to create a bar chart? 
  • How to create a stacked bar chart? 
  • How to create a box plot? 
  • How to create an area chart? 
  • How to create a heat map? 
  • How to plot a geographical map? 

      32) Neural Network
      33)  Tensorflow
      34) Keras
      35) NLP

  • Practice questions/case studies will be shared.
  • Practice Project work will be shared towards the end of the session.
  • Will share interview questions for preparation.  
My course on Datascience with Python Online Training and Artificial Intelligence has successfully completed under Rishika madam.Teaching is unique and understandable
My course on Datascience with Python Online Training and Artificial Intelligence has successfully completed under Rishika madam.Teaching is unique and understandable.
My course on Datascience with Python Online Training and Artificial Intelligence has successfully completed under Rishika madam.Teaching is unique and understandable