Certificate Program in Artificial Intelligence and Machine Learning

Blended Learning | 9 Months (Inclusive of Project - 1 Month) | INR 1,77,000/-

Certificate Program in Artificial Intelligence and Machine Learning | Category : IT

Why Join Certificate Program in Artificial Intelligence and Machine Learning

  • Apply the knowledge of Embeded Systems and wireless sensor networks to develop a basic understanding of AI building blocks presented in Intelligent Agents
  • Ability to design the solutions and analyse the strengths and weaknesses of AI approaches towards knowledge intensive problem-solving
  • Classify Machine Learning into supervised and unsupervised learning and perform Linear and Logistic regression
  • Ability to select the best algorithms for Machine Learning problems


Advanced Analytics Tools :- NumPy / SciPy / Pandaas. Artificial Intelligence Tools :- Keras / TensorFlow Programming Tool :- R

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  • Curriculum & Learning Outcomes
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This Program is designed to train you in the most promising world of Artificial Intelligence and Machine learning which gives the ability to choose the best algorithm for machine learning along with computer vision, NLP and deep learning as a principle components of artificial intelligence. Use of Python Libraries like NumPy, SciPy and Pandas again justifies Python as a most suitable language for hands-on experience of learning Artificial Intelligence and Machine Learning. Inclusion of R as a programming language interconnects the field of Data Science with AI & ML. Training of AI tools like Keras and Tensor Flow and the 1 month capstone project makes the program more industry- relevant.

Program Duration

9 Months (Inclusive of Project - 1 Month)

Mode Of Delivery

Blended Learning



Artificial Intelligence assists with ways of making a computer, a computer-controlled robot or software to think intelligently in a manner similar to the human mind.
Introduction to Artificial Intelligence
- Statistical Learning
- Python For AI and ML
Intelligent Agents
- Uninformed and heuristic-based search techniques
- Adversarial search and its uses
- Planning and constraint satisfaction techniques
Deep Learning
- Neural Network Basics
- Deep Neural Networks
- Recurrent Neural Networks (RNN)
- Deep Learning applied to images using CNN
- Tensor Flow for Neural Networks & Deep Learning
Computer Vision
- Convolutional Neural Networks
- Keras library for deep learning in Python
- Pre-processing image Data
- Object & face recognition
- Visualizing features & kernels
- TensorBoard – Visualizing Learning, Graph Visualization
- Synthesis and style transfer
- Case Study: Visualizing a convoluted neural network
Natural Language Processing
- Statistical NLP and text similarity
- Syntax and parsing techniques
- Text summarization techniques
- Semantics and Generation
Machine learning basics
- Introduction to machine learning
- Key terminologies
- Key tasks of machine learning
- Choosing a right algorithm
- Application development steps
Classifying with k-nearest Neighbour
- Classifying with distance measurement
- Improving matches from a dating site using KNN
- Handwriting recognition system
Decision Tree
- Tree construction
- Plotting trees in Python with matplottlib annotation
- Testing and storing a classifier
- Using decision tree to predict contact lenses type.
Classifying with probability theory: Naive Bayes
- Classifying with Bayesian decision theory
- Conditional Probability
- Classifying with conditional Probabilities
- Documents classification with naïve bayes
- Classifying text with Python
- Classifying Naïve Bayes with Python
Logistic regression
- Classification with logistic regression and the sigmoid function: a tractable step function
- Using optimization to find the best regression coefficient
- Estimating horse fatalities from colic database
Support Vector Machines
- Separating data with a maximum margin
- Finding a maximum margin
- Efficient optimization with SMO algorithm
- Speeding up optimization with full Platt SMO
- Using kernels for more complex data
- Revisiting handwriting classification
Improving classification with the AdaBoost meta –algorithm
- Classifiers using multiple samples of dataset
- Train: Improving the classifier by focussing on errors
- Creating a weak learner with descision stump
- Implementing the full Adaboost algorithm
- Test: Classifying with AdaBoost
- Adaboost on difficult dataset
Predicting numeric values: regression
- Finding best-fir lines with linear regression
- Locally weighted linear regression
- Predicting the age of abalone
- Shrinking coefficient to understand data
- The bias/variance trade off
- Forecasting the price of LEGO sets
Grouping unlabelled items using K-means clustering
- The k-Means clustering algorithm
- Improving clustering performance with post processing
- Bisecting K-means
- Clustering points on a map
Association analysis with Apriori algorithm
- Association analysis
- The Apriory principle
- Finding frequent itemsets with the Apriori algorithm
- Mining association rules from frequent itemsets
- Uncovering patterns in congressional votings
- Finding similar features in poisonous mushrooms
Efficiently finding frequent item sets with FP-Growth
- FP-trees: an efficient way to encode a dataset
- Build an FP tree
- Mining frequent items from an FP tree
- Finding co-occurring words in twitter field
- Mining a clickstream from a news site



  • Engineering, Graduate and Post Graduates (Computer Science, Information Technology)(Electronics and telecommunication, Electronics (With pre-requisite test)
  • Science Graduate (BSC IT, BCA) Diploma Engineer of above branches (With pre-requisite test)
  • Phd_Persuing in the above mentioned domains


INR 1,77,000/-

Please consult your Admission Counselor for flexi-EMI options


Please reach out to the admission office if you have any queries
100% Placement Assistance with Leading Corporates

Our placement assistance program offers students one-on-one career counselling, and the chance to work with our corporate partners.

What if I am unable to complete the course?

In case you drop out of the course due to a genuine reason, you will have 6 months’ time to return to it. If you fail to return to your course within this period, you will have to start afresh.

How will I pay for this course?

You can pay for the course of your interest on the website by clicking on the Fees tab via e-wallets, net banking, credit cards, debit cards as well as NEFT/Bank Transfer

What is the refund policy?

Refund must be claimed before the commencement of your batch. The Application form fees are non-refundable. Skillville will deduct 20% of the program fees paid till date of application for refund towards administrative charges and 80% will be refundable within 1 month from the approval of refund by a Skillville Authorized representative

What if you miss a class?

You will have an opportunity to catch up with a simultaneous batch in session or you can reach the respective Faculty to cover the missed class.

Whom should I contact in case of any purchase related query?

Please contact your Admission Counselor or drop an email regarding your querry to admissions@skillville.in.

Do I get a certificate of participation at the end of the training program?

Yes, you will get a certificate after completing your program after you meet the attendance and evaluation criteria set for your respective program.