MyTrainingsOnline

Online Course Learning

Data Science

DATA SCIENCE

 -> 1. What is machine Learning?
-> 2. what is data Science?
-> 3. What is Artificial Intelligence?
-> 4. What are the Programming Languages and how much Proficiancey required to be successfull in this stream?
-> 5. How Much Mathematics is required for ML, Data science, Artificial intelligence?
-> 6. What are the different tools, Techniques and libraries we are going to Use in this course curriculum?
-> 7. How much Practical Exposure you will get in this cource?
-> 8. How Much Time you need to Spend in day for this course ? 
-> 9. What is The Course Duration?months /Days
-> 10. How will be the course structure, and Learning Methodology?
-> 11. what are the Roles and Responsibilities/ Designations in the Industry for these Areas?
-> 12. What are the Prerequisites to choose , this as a carrier Oppurtunity?
-> 1. It is your requirment / For a better carrier you choose this . or Passion ? or Techonology upgrading Process.
-> 2. Commitment or desire to Learn 
-> 3. Some decent Level of commonsense
-> 4. Tenth class level Mathematical Understanding and willing to learn Mathematics.
-> 5. Willing to Adoptable Logical and Reasoning based Thinking Styel.
-> 6. Even you have zero Programming skills, willing to learn and Practice . 
-> 13. You need to clarify you are eligible for this course or not?
 ->1. Pre Algebra
->1. Reading And Interpreting the data
->2. Ratios, Rates, Praportions
->3. Equations, Expressions, Inequalities
->4. Exponents, Radicals Scienctific Notations
-> 5. Algebric Expressions
-> 6. Graphining Lines and Slope
->7. Quadratics and Polynomiyals
->8. System of Equations
->9. Equations and Geometry
 ->1. Functions
->2. Complex numbers
->3. Exponential Growth and Decay
->4. Exponentials and Logerithms
->5. Polynomiyals
->6. Vectors and Spaces
->7. Matrix transformations
->8. Alternate Cooredinate systems
 ->1. Introduction To Statistics
->2. Descriptive Statistics
->3. Probability
->4. Random Variables
->5. Normal Distribution
->6. Sampling and Sampling Distributions
->7. Confidence Intervals
->8. Hypothesis Testing
->9. The Comparision Of Two populations
->10. Analysis Of Variance (ANOVA)
->11. Simple Linear regression and Correlation
->12. Multiple Linear regression
->13. Forecating, Time Series
->14. Bayesian Statistics and Decission Analysis.
->15. Sampling Methoeds
->16. Multivariate Analysis
1. Basic Python programming

1. Using Python As a calculator
2. Variables
3. Value Assignment
4. Data Types in Python

1) Simple Arthimetic Operations
2) Comparision Operators
3) Type Functionality
4) Data type Conversions

5. Lists

->1. Creating list 
->2. Accessing elements from a list with index (Positive/Negetive)
->3. Slicing a list using index (Positive/Negetive)
->4. How to update an existing element in List
->5. How to add New element in list?
->6. How to delete/remove an element from list and removing entire list
->7. delete elements using remove(),pop()
->8. By assigning an empty list to a slice of list also we can remove elements
6. Tuples

->1. Creating Tuple
->2. Accessing elements from a list with index (Positive/Negetive)
->3. Slicing a list using index (Positive/Negetive)
->4. How to update an existing element in List
->5. How to add New element in list?
->6. How to delete/remove an element from list and removing entire list
->7. delete elements using remove(),pop()
->8. By assigning an empty list to a slice of list also we can remove elements
 ->1. Introduction about numpy
->2. Creating Numpy Arrays
->3. Different Numpy Operations
->4. Matrix
->5. Vectors
->6. Broadcasting With Numpy
->7. Solving Equations With Numpy
 ->1. Introduction about Pandas
->2. DataStructures in Python
->3. Reading or Loading data into Dataframe.
->4. Data Loading /Reading in different formates 
-> CSV
-> Excel
-> Json
-> HTML
->5. Pandas DataFrame Manipulations
Explorative Data Analysys.
Explorative Data Analysys.
Data Cleaning And Preprocessing/Wrangling
 ->1. What is Data Visualization?
->2. Theoritical Principles Behind Data Visualizations
->3. Histograms-Visualize the Distribution of Continuous Numerical Variables
->4. Boxplots-Visualize the Distribution of Continuous Numerical Variables
->5. Boxplots-Visualize the Distribution of Continuous Numerical Variables
->6. BarPlots
->7. Pie Chart
->8. Line Chart
Statisticas and Probability concepts to understandin Machine learning

->1. Unsupervised Learning in Python

->1. Ideology about Unsupervised Learning
->2. K- Means Theory/ Implementation
->3. Quantifying KMeans Clustering Performance
->4. Selection criteria for number of clusters choosing.
->5. Hierarchical Clustering Theory / Implementation
->6. Principal Component Analysis(PCA) theory / IMplementation
->2. Supervised Learininig

->1. Ideology about Unsupervised Learning
->2. Classification Problems
->3. Regression Problems
->4. Classification and Regression Accuaracy metrics
->5. Data Preparation Steps for Supervised Learning.
->6. Using Logistic Regression As a Classification Model
->7. RF – Classification
->8. RF – Regression
->9. NaiveBayes Classification
->10. Support vector Machines(SVM) -Linear Classification
->11. Support vector Machines(SVM) – Non Linear Classification
->12. Support Vector Regression(SVR)
->13. Knn classification
->14. KNN – Regression
->15. Gradient Boosting Regression
->16. Gradient Boosting Classification
->17. Linear Discriminant Analysis (LDA)
->1. Regular Expressions & Word tokenization
->2. Simple Topic Identification
->3. Named Entity Recognization
->4. Building fake News Classifier
 ->1. Basics of deep learning and neural networks
->2. Perceptron For Binary Classification
->3. ANN_ Binary Classification
->4. Multilabel Classification With MLP
->5. Regression With MLP
->6. MLP with PCA on Large DataSets
->7. DNN Introduction
->8. Specify the Activation Function
->9. Default H20 DeepLearning Algorithm
->9. H20 DeepLearning For Predection
->10. Optimizing a neural network with backward propagation
->11. Building deep learning models with keras
->12. Fine-tuning keras models
->13. Linear Programming
->14. Introduction about ANN Theory and Implementation
->15. RNN
->16. RNN_LSTM
->17. Basic Image Processing Using CNN
Foundations of Text mining
Foundations of ChatBoat making
Foundations of bigdata and GCP(Google Cloud Platform)
Visualizations Using The Tableau introduction.
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