Data Science-AI Course in Hyderabad
Data Science-AI Course In Hyderabad
Key Highlights
Live Sessions
Dedicated Success Coach
Placement Success Manager
Placement Drives
Data Science-AI Course In Hyderabad
Python + Data Science (Machine Learning Includes) + Google Cloud Platform
> Python + Data Science (Machine Learning Includes) + Google Cloud Platform
1. What is Artificial Intelligence & how it’s changing the world?
2. What is Data science?
3. What is Data Analysis & Business Analysis?
4. Introduction to Data.
a. Types of data
b. Categories of data.
5. Measurement & Scale
6. Scaling Techniques
7. Sampling
8. Sampling Techniques
9. Introduction and Importance of Google Cloud Platform.
Data Analytics
> Statistics
a. Central Tendency
b. Measures of Spread
c. Outliers
d. Correlation
e. Covariance
f. Quartiles
g. Inter quartile range
h. skewness
i. Standardization
j. Normalization
k. Hypothesis Testing
l. Chi-Square testing
m. ANOVA
> Probability
a. Kinds of Probability
b. General Additional rule
c. Distribution
d. normal distribution
e. Binomial distribution
f. Poisson distribution
g. Uniform Distribution
Python
> Introduction to Python
> Anaconda software introduction and installation
> Python – Fundamentals
a. Python – Syntax
b. Python – Variables and Datatypes
c. Python – Numbers
d. Strings
e. Sequences
f. List
g. Tuples
h. Ranges
i. Dictionary
j. Array
k. Sets
l. Operators
m. Statements
n. Loop
o. Date & Time
p. Functions
q. Packages and modules
r. Reading a File
s. Writing into File
t. Python – Exceptions
u. Regular Exp
> NumPy (with updated methods)
a. NumPy Introduction & Installation
b. NumPy Array creation
c. NumPy Operations
d. Mathematical functions with NumPy
e. Indexing
f. Slicing
g. Iterating
h. Shape Manipulation
i. Split function
j. Types of copy
> Pandas (with updated methods)
a. Introduction and Installation
b. Data creation
c. Data handling
d. Import & Export Data
e. Data Frame creation
f. Indexing
g. Data viewing
h. Data view with Mathematical functions
i. Resample
j. Sorting
k. Boolean Indexing
l. Merge
m. Join
n. Append
o. Reshaping
p. Grouping
q. Pivot Tables
r. Time series
s. Melt
> Matplotlib (Graphical data visualization)
a. Introduction and Installation
b. Line plot
c. Bar plot
d. Histogram
e. Scatter plot
f. Pie chart
g. Bar chart
h. 3-d plot
> Seaborn
a. Introduction and Installation
b. Data Plotting graphs
MS Excel
1. Basics of Excel
2. Data management and Formatting
3. Statistical Formulas implementation
4. Short cut keys
5. Pivot table
Exploratory Data Analysis (EDA): The Initial Step of Data Science Project
1. Data Analysis Introduction
2. Types of Analysis
a. Univariate Analysis
b. Bivariate Analysis
3. Data Preprocessing
a. Data Cleaning
b. Missing value treatment
c. Outlier treatment
d. Data transformation
Data Visualization
1. Histogram Plot
2. Bar Plot (Vertical & Horizontal)
3. Density Plot
4. Box Plot
5. Pie Plot
6. Line Plot
7. Correlation Matrix
8. Scatter Plot
9. Joint Plot
10. Heat map Plot
Machine Learning
> A brief Introduction to Machine Learning
> How Machine Learning Helping the Technology?
> Types of Machine Learning
> Supervised Learning
a. Regression
b. Classification
> Unsupervised Learning
a. Clustering
b. Recommendation
c. Principle component Analysis
> Reinforcement Learning (Self Supervised Learning)
> Data set Models
a. Underfitting model
b. Overfitting model
c. A good fit model
> Model Validation Metrics
a. Regression:
• R squared
• Adjusted R Squares
• Mean Squared Error (MSE).
• Root Mean Squared Error (RMSE).
• Mean Absolute Error (MAE)
b. Classification:
• Confusion Matrix
• Accuracy
• Precision
• Recall
• Sensitivity
• Specificity
• F1 Score
• AUC & ROC Curve
Algorithms Introduction
1. Regression Algorithms
a. Linear Regression
b. Logistic Regression
2. Linear discriminant Analysis
3. Gradient decent Algorithm
4. Tree Algorithm
a. Decision tree
b. Random forest
5. KNN Algorithm
6. Naive Bayes Algorithm
7. Support vector machines algorithm
8. XG Boost
9. Clustering Algorithms
a. K Means Clustering
b. Hierarchical Clustering
10. Principle component Analysis
11. Dimensionality Reduction
12. Time Series Forecasting (ARIMA, SARIMA, MA, Prophet, Holts)
13. SK-Learn package for Algorithms implementation.
Deep Learning
1. Introduction to Deep learning
2. How Deep Learning changing the World
3. Neural Networks
a. Introduction
b. Convolution Neural network
c. Artificial Neural network
d. Deep Neural network
2. Tensor flow
3. Open CV
Complimentary
1. Optical Character recognition (OCR)
2. Image Processing
3. Basics and Importance of Big Data
Google Cloud Platform (GCP)
1. Importance of Cloud
2. Introduction to Google Cloud Platform
3. Project Setup
4. Introduction to GCP Administration
5. GCP Cloud Storage (Data Storage tool)
6. GCP Vertex AI (Data Science Work Notebooks and API’s Platform)
7. GCP Big Query
Project
1. Assignments for every topic
2. 4 – Realtime projects
3. Mockup interview
4. Resume preparation & Interview questions
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