Artificial Intelligence

Ever wonder how machines learn, make decisions, and even recognize your preferences? This is the captivating realm of Artificial Intelligence (AI), a rapidly evolving field shaping our world in profound ways.

Saksham's Artificial Intelligence course offers a beginner-friendly yet comprehensive approach, equipping you with the foundational knowledge to:

• Understand the core concepts: Demystify fundamental AI concepts like machine learning, deep learning, and neural networks, and explore their diverse applications.
• Navigate the AI landscape: Gain insights into various AI techniques, algorithms, and tools used to develop intelligent systems.
• Unleash your creativity: Explore how AI is transforming various industries, from healthcare and finance to transportation and entertainment, and spark your imagination for future possibilities.

Course Overview:

• Session 1: Introduction to Python
• Session 2: Strings
• Session 3: Dictionaries
• Session 4: List
• Session 5: Functions
• Session 6: Tuples
• Session 7: OOPs
• Session 8: Exceptional handling
• Session 9: Python Libraries
• Session 10: Pandas Library
• Session 11: Libraries
• Session 12: Statistics
• Session 13: Data Science
• Session 14: Numpy, matplotlib, Scipy
• Session 15: EDA PART 1
• Session 16: EDA PART 2
• Session 17: Predictive analysis
• Session 18: Regression analysis
• Session 19: Forecasting techniques
• Session 20: Simulation and Risk Analysis
• Session 21: Decision Analytics and Sensitivity Analysis
• Session 22: Visualizations
• Session 23: Strategy and analytics
• Session 24: Factor Analysis
• Session 25: DDA AND FDA
• Session 26: TVM and Bond
• Session 27: Multi-factor model
• Session 28: Portfolio analytics
• Session 29: Web scraping
• Session 30: Regularization
• Session 31: What is machine Learning?
• Session 32: Types of Algorithm:
• Session 33: CART
• Session 34: KNN
• Session 35: Model Stacking
• Session 36: Association
• Session 37: SVM
• Session 38: ARMA
• Session 39: Neural Network
• Session 40: Introduction to Deep Learning
• Session 41: Deep Neural Network
• Session 42: Refresher
• Session 43: Understanding the dataset
• Session 44: Learning Algorithm
• Session 45: Forward and Backward propagation
• Session 46: Parameters vs Hyper Parameters
• Session 47: L2 normalization
• Session 48: Gradient checking
• Session 49: data normalization
• Session 50: Mini-batch gradient descent
• Session 51: Gradient descent algorithms
• Session 52: TensorFlow
• Session 53: Recurrent Neural Networks (RNNs)
• Session 54: Gates
• Session 55: LSTM and GRU
• Session 56: Sequence modeling
• Session 57: Reinforcement Learning
• Session 58: Success of Reinforcement learning
• Session 59: MDPs
• Session 60: Returns and Value functions
• Session 61: Dynamic programming
• Session 62: Temporal difference learning