AI & Machine Learning Course using Python
Course Overview
Artificial intelligence and Machine Learning is Gain expertise in one of the most fascinating and fastest growing areas of computer science through an innovative online program that covers fascinating and compelling topics in the field of Artificial Intelligence and its applications
Machine learning is a connected insight or arithmetic. It is a sub-field of software engineering. This part gives a short presentation about the Machine learning, history of machine learning, sorts of issues and errands in machine learning and its calculations.
Course Objective
Toward the finish of this course, you will have the capacity to
- Identify potential zones of uses of Machine Learning & AI
- This course covers foundational concepts and hands-on learning of leading machine learning tools, such as Python and TensorFlow.
- Providing Handon experience on a real-time case study.
Course Outcome
In Self Learning Artificial intelligence and Machine Learning, the results are truly intriguing, after this course, you ought to have a solid comprehension of machine learning and Artificial Intelligence with the goal that you can seek after any further and further developed learning.
Here we provide training through artificial intelligence and machine learning video tutorial so you are going to learn the course by your self and we provide all time support with your queries and the major course part is run by python programming for machine learning python program is well suited and we prefer machine learning python course because it is open source and easily understood by candidates than other programming languages.
Software & Tools
A learner will be using given software and tool during this course.
and other …
Prerequisites
Candidate must have basic knowledge of any programing language & Mathematics
Software and Machine –
Software:
- Anaconda – https://www.anaconda.com/download/ (According to bit version 32 bit for 32 Machine and 64 bit for 64-bit machine)
- Python Libraries used (Most of them are already available in Anaconda, others we will install during the training)
Machine Requirement:
- Recommended – Machine with 4GB RAM, i3 or above quad-core processor
- Requirement: Working Internet Connection throughout the training for participants.
FAQ’s
Q. How long does it take to finish the Course?
A. Time to finish can change in view of your calendar, yet most students can finish the Specialization in 3 months.
Q. How can we do practice?
A. We are providing hand on training, we use to provide all coding and connection guideline for your practice.
Q. How can we resolve our queries?
A. We are providing 24*7 Technical support till 3 Months, for any query you may reach on support@techtrunk.in
Q. Do you provide any Certificate for this course?
A. Yes, Course IoT Certification from TechTrunk Ventures.
Course Curriculum
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Module 1 (Demo)
In this Module we will cover, Introduction to AI and Machine Learning. Application of Machine Learning- Supervised, Unsupervised and Reinforcement Learning, Classification, Regression, Clustering, Anomaly Detection Recommendation, System Algorithms in Machine Learning and Introduction to Deep Learning
- Session 1. Introduction to AI Machine Learning and Application
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Session 2. Machine Learning Machine Learning techniques and Introduction to Deep Learning
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Module 2
In this module you will be able to popup with Python Programming, Object of this Module to make a fearless Python Coder, You will get to learn, practices and implement use cases, Build expertise in python programming, after learning python you can quick start with AI & ML
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Session 3. Introduction to Python Programming and Controls
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Session 4. Controls, function, Class Import and Export
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Module 3
In this module you will experience different tools for Statistics-
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Session 5. numpy- Arrays, Statistical, manipulation and Linear Algebra
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Session 6. Pandas
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Session 7. Statistical Analysis with Pandas
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Session 8. Statistical and Visualization
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Module 4
In this module you will learn about Statistics of Data- Data Variable, Mean, Mode, Median, Standard Deviation Variance, Correlation and Probability
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Session 9. Descriptive Statistics, Inferential Statistics, Variable, Median, Percentile, RFM
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Session 10. Variance, Standard Deviation and IQR, Chebyshev’s Theorem
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Session 11. Covariance, Correlation, Kurtosis, Skewness, Analyzing the Continuous and Categorical Data, Probability
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Session 12. Chi-Square Test & Prediction
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Module 5
Starting with Machine Learning Algorithms
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Session 13. Linear Regression
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Session 14. Linear Regression Model
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Session 15. Linear Regression use cases, Lasso, Ridge
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Session 16. Logistics Regression and Model
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Session 17. Matrix, Precision, Recall, RFM and model
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Session 18. Breast cancer Model using Logistic Regression, Decision Tree
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Module 6
In this module we will starting with Artificial Neural NetWorks
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Session 20. Possibility the survival of passenger in titanic- Model, Random Forest and Introduction to Artificial Neural Networks
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Session 21. Artificial Neural Networks, Architecture and Mathematical Modelling of Neural Network
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Session 22. Backpropagation algorithm
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Session 23. Backpropagation algorithm-Model
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Module 7
Here, are continue with ANN Example and Introduction with Image Processing
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Session 24. ANN Model and Image Processing
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Session 25. Image Processing and Image Manipulation.mp4
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Session 26. Face Recognition Model
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Session 27. Nearest Neighbour Predictions and Emotion Detection Model
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Module 8
Here you are going to learn forecasting, time series
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Session 28. Forecasting and Time Series
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Session 29. Time Series Components and Model
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Session 30. Exponential Smoothing Techniques
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Session 31. Support Vector Machine
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Module 9
Here you will learn Unsupervised learning
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Session 32. Clustering and K Means
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Session 33. Principal Component Analysis
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Session 34. Anomaly Detection
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Module 10
In this module you will learn Natural Language Processing and It's Model
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Session 35. Introduction to NLP, Application of NLP, Components of NLP and Simple Text Preprocession Model Regular Expression
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Session 36. Count Vector, Word Embedding
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Session 37. Text Classification, Chatboat Model
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Session 38. Tensorflow
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Instructors
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