Course Details
Machine Learning & AI
Course Overview
ML & AI course offers a comprehensive journey into Machine Learning (ML) and Artificial Intelligence (AI), equipping learners with the knowledge, tools, and hands-on skills to design, build, and deploy intelligent systems.
Beginning with the fundamentals of AI, ML, mathematics, and data preprocessing, learners progress through key concepts such as supervised and unsupervised learning, deep learning, neural networks, and reinforcement learning. The course also introduces specialized domains of AI, including Natural Language Processing (NLP), Computer Vision, Time Series Analysis, and Generative AI.
Course lessons
- What is Machine Learning ?
- Advantages & Disadvantages
- Why Machine Learning ?
- History of ML
- What is Data Mining ?
- Difference between AI & ML & DL
- Types Of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Baiscs of Python
- What is IDE?
- Download Anaconda Navigator & Installing
- Variables In Python
- Operators
- Functions
- Control Flow Statements
- String Functions
- Lists
- Tuples
- Dictionary
- Sets
- NumPy
- Pandas
- Matplotlib
- Seaborn
- TensorFlow
- Scikit-Learn
- Data Preprocessing
- Data Cleaning
- Data Integration
- Data Reduction
- Data Transformation
- Data Discretization
- Feature Engineering
- Steps for Data Cleaning
- Categorial Variable Encoding
- Feature Scaling
- MinMax Scale
- Train Test Split
- StandardScaler
- Linear Regression
- Multiple Linear Regression
- Ridge Regression
- Lasso Regression
- Mean Squared Error
- Root Mean Squared Error
- R-squared
- Polynomial Regression
- SVM
- NON -Linear SVM
- SVC
- Decision Tree Classifier
- Random Forest
- Function Transformer
- Power Transformer
- Binning
- Binarization
- Handling Date & Time
- ZScore
- Gradient Descent
- Batch Gradient
- Stochastic Gradient Descent
- Elastic Net Regression
- Save Model
- Pickle
- Joblib
- K Nearest Neighbour
- Euclidean Distancs
- Manhattan Distance
- Naive Byes
- Gaussian Method
- MultinomialNB
- BernoulliNB
- K-Means Clustering
- Elbow method
- Hierachical Clustering
- Dendrogram
- DBSCAN Clustering
- Silhouette Score
- Knee Locator
- PCA
- Anomaly Detection
- Isolation Tree
- Isolation Forest
- Dimension Reduction
- Factor Analysis
- Scree Plot
- Linear Discriminant
- Association Rule Mining
- A-priori Algorithm
- Market Basket Analysis
- AIS Algorithm
- SETM Algorithm
- Fp Growth
- Reinforcement Learning
- Markov Decision Process
- Cartpole In OpenAI Gym
- Recommedation System
- Collaborative Filtering
- Time Series Analysis & Forecasting
- Seasonality
- Introduction
- Neuron
- Neural Networks & Types
- Single layer Perceptron
- Multilayer perceptron(ANN)
- Forward propogation
- Backward Propogation
- Activation Functions
- Sigmoid
- Tanh
- Softmax
- ReLu
- Loss Functions
- Normalization
- Regularization
- Dropout Layer
- CNN
- ANN
- NLP
- POS
- Introduction
- Types of AI
- AI Models
- Narrow AI
- General AI
- Super AI
- Agents In AI
- Problem Solving
- N-Queens Problem
- Graph Coloring
- Search Algorithm
- Depth First Search
- Breadth First Search
- Informed Search Algorithm
- A* Tree Search
- Local Search Algorithm
- Adversarial Search Algorithm
- Constraint Satisfaction Problems
- Knowledge Representation
- Structured Representation
- In Robotics
- OpenCV
- Generative AI
- GAN
- CGAN
- DCGAN
- CNN
- LAPGAN
- SRGAN
- CycleGAN
- StyleGAN
- GPT
- BERT Model
- Multilingual language Models
- Next Sentence Prediction
- Hate Speech Detection
- Generative AI Applications
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