Intro & Motivation, AI Fundamentals, Production System, Ontology, Propositional logic, First order predicate logic, Fuzzy logic, pattern Recognition, Machine Learning, Neural Networks
Duration
10 Weeks
Session Days
Thursday & Saturday
Timings
Class1: 2:00PM - 4:OOPM
Class2: 4:30PM - 6:30PM
Seats Available
30
Course Outline
• Introduction of Artificial intelligence
o Machine Learning and Deep Learning
o Data Science
• Setting up environments
o Installation of Anacoda
o Pycharm
o Jupyter notebook
o Google colab
• Python Basics
o Print statement.
o Variables and its types
o Operators and its types
o Execution of Hello World Program.
• Conditional statement
o Develop a program based on control structures (IF,IF ELSE, ELIF and NESTED IF)
• Loops
o Working of for loop
o Working of while loop
o Nested loop
• Modular Programming
o Function creation
o Function calling
o Develop program using functions.
• String Handling
• Data types in python
o Use of list, tuple, and dictionary.
o Accessing elements from list, tuple, and dictionary.
o Adding and removing data from list, tuple, and dictionary. o Functions of list, tuple, and dictionary
• OOP Basics
• Introduction toNumPy Library
o Import and install NumPy.
o Creating Arrays
o NumPy- Data types
o Array Attributes
o Indexing and slicing
o Array creation routines
o Operations on arrays
o Sorting arrays
• Introduction to Pandas Library
o Pandas’ data structures (series & data frame)
o Input & output operations using pandas.
o Selection operations
o Dropping data
o Sort & rank
o Applying functions
o Data Alignment
o Data preprocessing using pandas
• Data visualization
o Installation and Import Matplotlib
o Preparing the data
o Creating the plot
o Plotting routines
o Customizing the plot
o Saving the plot
o Displaying the plot
o Types of plots
• Introduction to Machine Learning
o Supervised Learning
o Unsupervised learning
o Semi Supervised Learning
o Reinforcement Learning
• Introduction to Machine Learning
o Supervised Learning
o Unsupervised learning
o Semi Supervised Learning
o Reinforcement Learning
• Supervised Learning
o Unsupervised learning
o Semi Supervised Learning
o Reinforcement Learning
• Supervised learning (Part 1)
o Model Training using Regression.
o Model Training using classification.
o Predictions on unseen data
o Model Evaluation metrices
• Unsupervised Learning
o Clustering
o Classification Vs Clustering
o Types of Clustering
o Implementation of K-Means and Hierarchical clustering
• Introduction to Time Series analysis
o What is Time Series
o Application of Time Series analysis
o Seasonality and cyclicity
o Implementation using ARIMA model
• Introduction to Deep Learning-I
o Machine Learning Vs Deep Learning
o Neuron Vs Perceptron
o MLP
o Neural network
o Types of Neural network
• Introduction to Deep Learning-II
o Feed Forword neural network
o Backpropagation
o Activation Functions
o Loss Function
o Optimization
o Implementation of ANN using TensorFlow
• Introduction to Deep Learning-III
o Theory of CNN
o Image Classification using CNN
• Introduction to Deep Learning-IV
o What is Recurrent neural network.
o Theory of LSTM and GRU
o Sine wave prediction using LSTM
• Soft Skils
o Effective Communication Skills
o Leadership and Teamwork
o Time Management and Productivity
o Emotional Intelligence and Resilience
• Entrepreneurships
o Introduction to Entrepreneurship
o Developing a Business Plan
o Funding and Financing
o Launching and Scaling Your Business
• Soft Skils
o Effective Communication Skills
o Leadership and Teamwork
o Time Management and Productivity
o Emotional Intelligence and Resilience
• Entrepreneurships
o Introduction to Entrepreneurship
o Developing a Business Plan
o Funding and Financing
o Launching and Scaling Your Business