The Artificial Intelligence(AI) training in Nepal class will teach you how to construct Artificial Intelligence and accelerated computing applications. The course concepts will be explained in a simple language that makes you easy to grasp. Ever since the invention of computers, their ability to perform various tasks grew exponentially. Humans have developed the capacity of computer systems to the next generation level concerning their usefulness in each and every segment of a market, along with their speed and reducing their size as time passed. AI is achieved by studying the thinking process of human brains, how humans learn, decide, and work while trying to find a solution. After learning the course we need to use the results of this study in developing intelligent computers and software. Enroll & Become Certified!

Artificial Intelligence training in Nepal is provided by TechAxis, the one of the Top AI Training Institute in Nepal. Artificial intelligence has existed since humans first began entering automation and related technologies. The Greeks had mythical stories about robots, the Chinese and the Egyptians also had achieved some advancement in this field. AI is continuously advancing at a fast rate as companies are discovering new ways to reap the benefits of this technology to automate processes, increase productivity, and reduce cost.

Over the recent years, this has triggered a substantial increase in the need for Artificial Intelligence certified professionals. However, a student often encounters difficulties while trying to find the best AI(Artificial Intelligence) training in Nepal. The Nepal education market is full of training institutes that provide AI training, but they aren't all reliable. our extended experience in this realm, we provide our students with real-time project-based training that will make you adept at AI, like professionals.

The Future of Artificial Intelligence in Kathmandu appears promising. There are lots of private and governmental organizations in Nepal especially in Kathmandu that have begun using Artificial Intelligence for automating a variety of tasks through machines and devices such as drones, robots, communication intercepting softwares, chatbots it.

Introduction to Python :

• Concepts of Python programming
• Configuration of Development Environment
• Variable and Strings
• Functions, Control Flow and Loops
• Tuple, Lists and Dictionaries
• Standard Libraries

Module 2: Data Science Fundamentals :

• Introduction to Data Science
• Real world use-cases of Data Science
• Walkthrough of data types
• Data Science project lifecycle

Module 3: Introduction to NumPy:

• Basics of NumPy Arrays
• Mathematical operations in NumPy
• NumPy Array manipulation
• NumPy Array broadcasting

Module 4: Data Manipulation with Pandas :

• Data Structures in Pandas-Series and DataFrames
• Data cleaning in Pandas
• Data manipulation in Pandas
• Handling missing values in datasets
• Hands-on: Implement NumPy arrays and Pandas DataFrames

Module 5: Data Visualization in Python :

• Plotting basic charts in Python
• Data visualization with Matplotlib
• Statistical data visualization with Seaborn
• Hands-on: Coding sessions using Matplotlib, Seaborn packages

Module 6: Exploratory Data Analysis :

• Introduction to Exploratory Data Analysis (EDA) steps
• Plots to explore relationship between two variables
• Histograms, Box plots to explore a single variable
• Heat maps, Pair plots to explore correlations
• Perform EDA to explore survival using titanic dataset

Module 7: Introduction to Machine Learning :

• What is Machine Learning?
• Use Cases of Machine Learning
• Types of Machine Learning - Supervised to Unsupervised methods
• Machine Learning workflow

Module 8: Linear Regression :

• Introduction to Linear Regression
• Use cases of Linear Regression
• How to fit a Linear Regression model?
• Evaluating and interpreting results from Linear Regression models
• Predict Bike sharing demand

Module 9: Logistic Regression :

• Introduction to Logistic Regression
• Logistic Regression use cases
• Understand use of odds & Logit function to perform logistic regression
• Predicting credit card default cases

Module 10: Decision Trees & Random Forest :

• Introduction to Decision Trees & Random Forest
• Understanding criterion(Entropy & Information Gain) used in Decision Trees
• Using Ensemble methods in Decision Trees
• Applications of Random Forest
• Predict passenger survival using Titanic Data set

Module 11: Model Evaluation Techniques :

• Introduction to evaluation metrics and model selection in Machine Learning
• Importance of Confusion matrix for predictions
• Measures of model evaluation - Sensitivity, specificity, precision, recall & f-score
• Use AUC-ROC curve to decide best model
• Applying model evaluation techniques to Titanic dataset

Module 12: Dimensionality Reduction using PCA:

• Unsupervised Learning: Introduction to Curse of Dimensionality
• What is dimensionality reduction?
• Technique used in PCA to reduce dimensions
• Applications of Principle component Analysis (PCA)
• Optimize model performance using PCA on SPECTF heart data

Module 13: KNearestNeighbours:

• Introduction to KNN
• Calculate neighbours using distance measures
• Find optimal value of K in KNN method
• Advantage & disadvantages of KNN

Module 14: Naive Bayes Classifier:

• Introduction to Naive Bayes Classification
• Refresher on Probability theory
• Applications of Naive Bayes Algorithm in Machine Learning
• Classify spam emails based on probability

Module 15: K-means Clustering:

• Introduction to K-means clustering
• Decide clusters by adjusting centroids
• Find optimal 'k value' in K-means
• Understand applications of clustering in Machine Learning
• Segment hands in Poker data and segment flower species in Iris flower data

Module 16: Support Vector Machines:

• Introduction to SVM
• Figure decision boundaries using support vectors
• Identify hyperplane in SVM
• Applications of SVM in Machine Learning
• Predicting wine quality using SVM