You learn from industry experts not only through content but also through mentor-driven personalized learning sessions. Each module has recorded content that is followed by a session with an industry mentor who helps students master Machine Learning & Data Science tools and techniques and clears any doubts in a small group of 5 participants.
EXPERIENTIAL LEARNING PROJECTS
An experiential learning project at the end of every module helps candidates internalize their understanding of the content consumed. Our coursework and practical assignments are designed to enable candidates to apply what they have learned during self-study and industry sessions.
Each week, participants get access to industry videos and webinars other than personalized learning sessions. Delivered by industry leaders, these resources are business-relevant, provide insights on current industry knowledge and solving real-life business problems.
GREAT LAKES ADVANTAGE
World-class award-winning faculty from Great Lakes ensure that the candidates learn through an exhaustive curriculum with hands-on experience. All participants upon successful competion are awarded a certificate by Great Lakes.
Candidates learn from 100+ distinguished Machine Learning experts who mentor them throughout the course of the program. Our Machine Learning mentors are thought-leaders in different domains with several years of industry experience and impart practical knowledge and industry insights in participants’ learning jouney.
CAREER ENHANCEMENT SESSIONS
The Program includes career development sessions which help participants identify their strengths, a customised career path and empowers them to clear interviews. Interacting with industry practitioners also helps participants gain exposure and experience in transitioning their careers.
WEEKEND INSTRUCTOR LED CLASSES
48 hours of personalised mentorship from Machine Learning & Data Science professionals working in leading companies.
Best-in-class recorded content from expert faculty and industry mentors.
7 HANDS-ON PROJECTS
Practical assignments, case studies and instructor-led practice sessions on data sets.
Statistical analysis conceptsDescriptive statisticsIntroduction to probability and Bayes theoremProbability distributionsHypothesis testing & scoresExperiential learning project
PYTHON FOR MACHINE LEARNING
Python OverviewPandas for Pre-Processing and Exploratory Data AnalysisNumpy for Statistical AnalysisSeaborn for Data VisualizationCase Studies and careersExperiential Learning projectMACHINE LEARNING MODULE
Introduction to Machine LearningSupervised Learning conceptsLinear RegressionLogistic RegressionK-NN ClassificationNaive Bayesian classifiersSVM – Support Vector MachinesExperiential Learning project
Unsupervised Learning conceptsClustering approachesK Means clusteringHierarchical clustering
Decision TreesIntroduction to Ensemble LearningDifferent Ensemble Learning TechniquesBaggingBoostingRandom ForestsStackingExperiential Learning projectPCA (Principal Component Analysis) and Its Applications
FEATURIZATION, MODEL SELECTION & TUNING
Text AnalyticsFeature extractionModel Defects & Evaluation MetricsModel selection and tuningComparison of Machine Learning modelsExperiential Learning project
Introduction to Recommendation SystemsTypes of Recommendation TechniquesCollaborative FilteringContent based FilteringHybrid RSCase StudyPerformance measurementExperiential Learning project
Applicants should have a bachelor’s degree with a minimum of 50% aggregate marks or equivalent.
Preference will be given to candidates with Engineering, Mathematics, Statistics, and Economics background.
Interested candidates need to apply by filling up an Online Application Form.
The Admissions committee and faculty panel will review all the applications and shortlist candidates based on their profiles.
Offer will be made to selected applicants.