Machine Learning with Data Science Online Course | Learn and Build

Machine Learning with Data Science Online Course | Learn and Build

• 1.
  Machine Learning With DataScience Booster Program Deep Learning Neural Networks NLP Tensorflow Computer Vision Pytorch Numpy
• 2.
  Who we are BoosterProgram | ML & Data Science learnandbuild.in Learn and Build is a leading online ed-tech provider dedicated to core learning and building the career of aspiring learners. It has been envisioned to provide learners a starting point to begin their career in the field of related technologies. Our team of experts, having over 45+ years of industry experience are intensively committed in providing high-quality education and research in all aspects of technology .
• 3.
  What are BoosterPrograms? The course will take you from the basics to the advanced level and will assist you in developing a better knowledge. This booster program will assist you in maximizing the outcome and benefit of your knowledge through practical application. Outcomes to Expect Work on Real World Projects Practical Assignments Weekly Assessments Hybrid Learning(Pre-recorded + Live classes) Doubt Sessions 24 hrs of Mentor Assistance Letter of Recommendation Industry Recognised LnB Certificate 01 02 03 04 05 06 07 08 Booster Program | ML & Data Scienceii learnandbuild.in #Bss thoda h, thode ki jarurat or h!!
• 4.
  Booster Course |Artificial Intelligence learnandbuild.in Automate the Learning ML WITH DATA SCIENCE Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. The algorithms that give computers the ability to learn from data, and then make predictions and decisions. The primary aim is to allow the computers to learn automatically without human assistance and perform actions accordingly. Why should you learn this? Booster Program | ML & Data Science learnandbuild.in ML has applications in multiple industries. This technology can be applied to real-life problems like image recognition, social media analysis, smart assistants, news classification, video surveillance, optimization of search engine results, email analysis, cyber security, customer service etc. Making machines able to Learn like humans With this course, learner will get through with Machine Learning starting from scratch to pro. • • • •
• 5.
  Booster Program |ML & Data Science learnandbuild.in Curriculum Introduction with AI & Machine Learning Data Science vs Data Engineering vs Data Analysis vs AI Use of Data in the world of AI Basic Linux/Windows Commands Why Python? Installing & Setting up Python on System Understanding its Command Line & Scripts Simple Python Program Python Revisit: Keywords, Data Types, Operators Conditional/Looping/Error Handling in Python 01. Introduction & Python Recap 2 hrs Comprehensions Python User Defined Functions Python Generators Lambda Expressions Python Modules: Usage and Installation Understanding the OOP of Python 02. Python Recap 2 2 hrs Types of DATA? The key steps of Data Analysis The file handling in python Dealing with Excel/Json/CSV/txt files What are SQL based Database Basic SQL Operations 03. Data formats & Python 2 hrs Duration – 4/6 Weeks (70 Hours)
• 6.
  05. Pandas 2hrs The Series and DataFrame Creating, Accessing, Manipulating Pandas Data Series and DataFrame Attributes & Basic Functions Iteration on Data Statistical Functions; String Functions Logical Indexing; Sorting & Reindexing Merging, Joining & Concatenation of Data Exercise – 2 Understanding Kaggle/Hackerearth Platforms Pandas File Handling Grouping Data Function Application Missing Data & Treatment Date & Time Functionality Exercise-3 06. Pandas - 2 2 hrs Booster Program | ML & Data Science learnandbuild.in Python Numpy Arrays Creating, Accessing, Manipulating Numpy Array Numpy Data Types Array Attributes Data Operations Arithmetic and Statistical Methods Sort, Search, Count File Handling with Numpy Exercise – 1 04. Numpy 2 hrs
• 7.
  Booster Program |ML & Data Science learnandbuild.in Understanding the concept of Machine Learning The Flow of Machine Learning The Mathematics Required for ML Types of Learning and their sub-categories The Scikit-learn Library REGRESSION: Linear Regression The Line Equation; Fitting Data in Model Performance Evaluation of Model Project 3: “Real-estate House Price Predictor Model” Exercise – 6 10. Machine Learning - Regression 2 hrs How Data is Beautiful? Visualization Libraries in Python MATPLOTLIB PYPLOT: line, scatter, pie, box, area etc Decorating the plots using Matplotlib (labels, colors, markers, legend, grids, figure sizes etc) The Subplots and axes in matplotlib Showing Images Exercise – 4 08. Data Visualization 2 hrs Pandas Visualization: Basic Plots Bar, barh, hist, box, kde, density, area, scatter, hexbin, pie plots Plotting with Missing Data Exercise – 5 Project 2: “Descriptive Analytic Model of Restaurant Billing & Tips Data” 09. Data Visualization 2 hrs Project 1: “Movie Recommendation System” 07. Project 1 2 hrs
• 8.
  Booster Program |ML & Data Science learnandbuild.in What is NLP? Linguistic to Natural Language Text and Speech Processing Text to Speech and Speech to Text Modules in Python Morphological Analysis Syntactic Analysis Word Clouds Libraries in Python Exercise – 9 Project 8: “Fake News Detection System” Multiple Linear Regression: Case-study Polynomial Regression: The Non-linearity in Data Project 4: “Startup Profit Prediction System” Project 5: “Companies Valuation Prediction” Exercise – 7 11. Machine Learning - Regression 2 hrs Logistic Regression: Concept Dealing with missing data in real-time information Exercise – 8 Project 6: “Flower Species Classification Model” 12. Machine Learning - Classification 2 hrs The Information Theory Decision Trees Classifier Random Forest Classifier Project 7: “Heart Disease Detection App” 13. Machine Learning - Classification 2 hrs 14. Natural Language Processing - Intro 2 hrs
• 9.
  15. Machine Learning– Naïve Bayes 2 hrs 16. Deployment of ML Models 2 hrs Introduction with Flask Deployment of Machine Learning Models over GITHUB and Cloud Services (AWS/ Google/ and similar platforms) QUERY SESSION The Probability Bayes Theorem Naïve Bayes Algorithm for Machine Learning Naive Bayes Algo implementation Project 9: “Email SPAM Detection Application” 17. Data Visualization - Seaborn 2 hrs Easy and advanced Data Visualization from Seaborn Categorical, Distributive, Regression, Matrix, Grid Plots Customizing Color Themes Some Datastore Exercise – 10 18. Data Visualization - Plotly 2 hrs Booster Program | ML & Data Science learnandbuild.in The Real-time challenges of Data Visualization The Front End of ML - PLOTLY Plotly Express for Quick and Fiery Interactive Graphs Scatter, Pie, Line, Bubble, Bar, Error, Box, Histograms Heatmaps, Funnel, Waterfall, Candlestick, Distribution Plots Map Plotting Exercise – 11
• 10.
  20. Machine Learning-Classification 2 hrs Kernel Nearest Neighbors (KNN) Working with sound data MFCC for speech processing Project 11: “Music Genre Prediction System” 19. Data Visualization - Plotly 2 hrs Graph Transformation, Filtering, Aggregation, Grouping User Instructiveness with User Controller Creating Dashboards (Introduction) Project 10: “Weather Prediction System” 21. Machine Learning -Classification 2 hrs Support Vector Machines (SVMs) The Hyperplane Concept Trade-off between biases-variances Project 12: “Finding the organic users on social media” Exercise – 12 Booster Program | ML & Data Science learnandbuild.in 22. Unsupervised Machine Learning 2 hrs Clustering Theory K-Means Clustering Principal Component Analysis Project 13: “Detecting Real versus Fraud Credit Card Applications”
• 11.
  23. Deep Learning–Speech Recognition 2 hrs What is Deep Learning? Understanding Neural Network Multi-layer perceptron Problem Libraries for Deep Neural Network – sklearn, pytorch, TensorFlow, Keras The MLP Classifier Project 14: “Speech Emotion Recognition” 24. Artificial Neural Network 2 hrs What is ANN? The basic terminology – Layers, weights, biases, activation functions, losses, optimizers, learning rate The Concept of Forward Propagation Backward Propagation Gradient Descent & SGD ANN Using TensorFlow TensorFlow 1.x vs TensorFlow 2.x Project 15: “MNIST Image Classification Model” Booster Program | ML & Data Science learnandbuild.in
• 12.
  Data Analyst Human Centered MLEngineer Cloud Architect ML Engineer Data Architect Software Developer NLP Scientist Business Intelligence Developer Data Scientist Career Opportunites Booster Program | ML & Data Science learnandbuild.in
• 13.
  • Automotive Industry •Production • Robotics • Logistics • Manufacturing • Quantum Computing • IT Industry • Security • Customer Service Industries using AI the most Booster Program | ML & Data Science learnandbuild.in Key Takeaways Coding Proficiency Neural Network Architecture Natural Language Processing Software Testing & Debugging Data Architecture Audio & Video Processing ML Concepts ML Algorithms & Libraries
• 14.
  Booster Program |ML & Data Science learnandbuild.in Courses Offered for these Tech Skills Artificial Intelligence 33 Modules Embedded Systems & IoT 36 Modules 16+ Projects Full Stack Development 22 Modules 10 Projects & Case Studies Data Engineering with DevOps 14 Modules 3+ Projects Flutter App Development 11 Modules 10 Projects & Case Studies Ethical Hacking & Cyber Security 12 Modules 10 Projects & Case Studies 28+ Projects
• 15.
  We believe incore learning and want every learner to be a part of this. Booster Program | ML & Data Science learnandbuild.in Learning Pedagogy Pre-Learning Attend Technical Sessions Submit Assignments Weekly Submit Projects Test your skills through Assessments Submit Project Reports Get Certified Nucleus Course Aspire Course Choose to proceed for specialisation programs Start Learning
• 16.
  50+ Hiring Partners 200K+ Learners 45+ Technologies 300+ Institution Tie-ups 25+ TechnologyExperts 10K+ Sessions Delivered Your Success is Our Success Booster Program | ML & Data Science learnandbuild.in
• 17.
  info@learnandbuild.in +91 94626 81393| +91 9251094002 Call us Email us learnandbuild.in Website Artificial Intelligence Computer Vision Azure Kubernetes Python Linux Internet of Things Docker AWS Cloud DevOps Cloud Computing Cyber Security Big Data Spark Machine Learning Data Science