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Course curriculum / Syllabus
Introduction to Computer Science
- What is computer science?
- What is machine learning?
- What is deep learning?
- What is artificial intelligence?
- Analysis of data and data types.
Introduction to Python
- What is Python?
- Why Python?
- How to install Python
- Python IDE
- Overview of the Jupyter notebook
Python Basics
- Basic Python data types
- What are lists in Python?
- What is splitting?
- Use of IF operators
- What are cycles in Python?
- What is a dictionary and how do we use it?
- What is the double method?
- What are functions in Python?
- What is an array?
- Selecting by tags and location
Python packages
- What is pandas and where is it used?
- What is Numpy in Python?
- What is scikit learn in python?
- What is the mat plot library?
Importing Data
- Reading CSV files in Python
- Writing data in Python
- Loading objects in Python
- Writing data to a CSV file
Data Manipulation
- Selecting rows/observations
- Rounding numbers
- Selecting columns/fields
- How to combine data
- Combining data in Python
- Techniques for aggregating data in Python
Fundamentals of Statistics
- Central tendency
- What is the mean?
- What is the median?
- What is a country?
- What is the slope?
- What is the normal distribution in Pythons?
- Probability calculation in Python
- What does the term probability mean?
- Types of probabilities
- ODDS ratios ?
- Standard deviation
- Variation and distribution of data
- What are Outliers?
- Agreement on the change
- What is underperformance?
- What is outperformance?
- Distance measures
- What is Euclidean distance and how is it calculated?
- How is the Manhattan distance calculated?
- What is a gap analysis?
- What is an analysis of variance?
- Interquartile range
- Case Diagram
- What is the upper bound and how is it determined?
- What is the lower limit?
- What is a scatter plot and how is it drawn?
- What is the distance from Cook?
- Treatment of missing values
- What is NA?
- What is a centralized function in Python?
- What is the KNN imputation method?
- How are dummy variables created?
- Correlation
- What is the Pearson correlation coefficient?
- What are positive and negative correlations?
Error Measurement
- What measures are used in classification problems?
- What is a mixture matrix?
- What is a precision measure?
- What is recall and how is it calculated?
- What is specificity?
- What is the F1 measure and how is it calculated?
- What is a regression measure and how is it measured?
- What is MSE?
- What is RMSE?
- What is MAPE?
Machine Learning Guided learning
- How does linear regression work?
- How do you solve linear equations?
- What is a gradient?
- What is an interrupt in Python?
- Calculating the value of an R-square
- Logistic Regression
- Explain what the ODDS ratio is.
- Probability of success
- Probability of failure exchange
- How ROC curves are used.
- Understand the interaction of variables and biases.
- Unlimited learning
- How are K-Means calculated?
- What is K-Means++?
- What is hierarchical clustering?
- Concepts with Support Vectors
- What are support vectors?
- What are hypergraphs in SVMs?
- What is a two-dimensional case?
- Use of linear hyperplanes
- SVM
- How does the linear SVM work?
- What is a radial kernel?
- What is a polynomial kernel?
- Other machine learning algorithms
- K - nearest neighbor
- Naive Bayes classifier
- Decision Tree - CART
- Decision Tree - C50
- Random forest
SCIENTIFIC INTEGRATION Introduction to Artificial Intelligence
- What is the perceptron learning algorithm?
- What is a multilayer perceptron?
- What is a Markov decision process?
- What is a logical agent and first order logic?
- Application of artificial intelligence
Deep Learning Algorithms
- CNN - convolutional neural network
- RNN - Recurrent Neural Network
- ANN - Res
Artificial Intelligence certification FAQ’s:
1.What is Artificial Intelligence?
AI is branch of computer science that deals with simulation of human intelligence in machines that are programmed to think like humans. Some of the examples of AI applications are natural language processing, Speech recognition, and machine vision.
2.What is the AI technology used for?
AI is the ability of machine to display human capabilities such as learning, creativity, reasoning, and planning. AI allows the technical systems to perceive their environment and solve problems to achieve specific goal.
3.How do I get AI certification?
We would provide you with AI certificate upon the completion of the course successfully. Our certificate is recognized with many leading organizations. It will be a value add to your resume. And will help you gain credibility while hiring.
4.Can I attend the AI demo session?
Yes, you can attend our AI demo session to give you confidence upon taking our course.
5.What if I miss the class?
We would provide you with recording of the session, and also provide you with eLearning material for self-study.
6.Do you provide AI job assistance?
Yes, we provide you the job assistance required and also help you to prepare for interview.
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