IT & Software
AI & ML ADVANCE
- 50 Hrs.
- 38 Modules
- 37 Chapters
- 1 Student Enrolled
Artificial Intelligence (AI) has a long history, but is still properly and actively growing and changing. In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI, such as Data Science, Machine Learning, Deep Learning, Statistics, Artificial Neural Networks, Restricted Boltzmann Machine (RBM) and TensorFlow with Python. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.
This Artificial Intelligence course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is going to apply.
Course Pre-Requisites
- Foremost, a motivation of learning
- Good Knowledge of Mathematics
- A Programming language knowledge
- A Strong Analytical and Logical skills
- Good understanding of complex algorithms
- Basic fundamental knowledge of statistics and modeling
- Comprehensive knowledge of ML and Data Science fundamentals
- Insight view of applied AI and ML models
Key Features
Data Science Deep Learning & Artificial Intelligence
Learning Path
No Chapters Found!
- Limitations of Machine LearningChapter 1
- Need for Data Scientists, Foundation of Data Science, What is Business Intelligence, What is Data Analysis, What is Data MiningChapter 1
- Value Chain, Types of Analytics, Lifecycle Probability, Analytics Project Life cycle, Advantage of Deep Learning over Machine learning, Reasons for Deep Learning, Real-Life use cases of Deep Learning, Review of Machine LearningChapter 1
- Basis of Data Categorization, Types of Data, Data Collection Types, Forms of Data & Sources, Data Quality & Changes, Data Quality Issues, Data Quality Story, What is Data Architecture, Components of Data Architecture, OLTP vs OLAP, How is Data Stored?Chapter 1
- Big Data, What is Big Data?, 5Vs of Big Data, Big Data Architecture, Big Data Technologies, Big Data Challenge, Big Data Requirements, Big Data Distributed Computing & Complexity, Hadoop, Map Reduce Framework, Hadoop EcosystemChapter 1
- What Data Science is, Why Data Scientists are in demand, What is a Data Product, The growing need for Data Science, Large Scale Analysis Cost vs Storage, Data Science Skills, Data Science Project Life Cycle & Stages, Data Science Use Cases, Data AcquisitiChapter 1
- Python Overview, About Interpreted Languages, Advantages/Disadvantages of Python pydoc. Starting Python, Interpreter PATH, Using the Interpreter, Running a Python Script, Using Variables, Keywords, Built-in Functions, StringsDifferent Literals, Math OperaChapter 1
- The xrange() function, List Comprehensions, Generator Expressions, Dictionaries and Sets.Chapter 1
- Learning NumPy, Introduction to Pandas, Creating Data Frames, Grouping Sorting, Plotting Data, Creating Functions, Slicing/Dicing Operations.Chapter 1
- Functions, Function Parameters, Global Variables, Variable Scope and Returning Values. Sorting, Alternate Keys, Lambda Functions, Sorting Collections of Collections, Classes & OOPsChapter 1
- What is Statistics, Descriptive Statistics, Central Tendency Measures, The Story of Average, Dispersion Measures, Data Distributions, Central Limit Theorem, What is Sampling, Why Sampling, Sampling Methods, Inferential Statistics, What is Hypothesis testiChapter 1
- ML Fundamentals, ML Common Use Cases, Understanding Supervised and Unsupervised Learning TechniquesChapter 1
- Similarity Metrics, Distance Measure Types: Euclidean, Cosine Measures, Creating predictive models, Understanding K-Means Clustering, Understanding TF-IDF, Cosine, Similarity and their application to Vector Space Model, Case studyChapter 1
- What is Association Rules & its use cases?, What is Recommendation Engine & it’s working?, Recommendation Use-case, Case studyChapter 1
- How to build Decision trees, What is Classification and its use cases?, What is Decision Tree?, Algorithm for Decision Tree Induction, Creating a Decision Tree, Confusion Matrix, Case studyChapter 1
- What is Random Forests, Features of Random Forest, Out of Box Error Estimate and Variable Importance, Case studyChapter 1
- Case studyChapter 1
- Various approaches to solve a Data Science Problem, Pros and Cons of different approaches and algorithms.Chapter 1
- Case study, Introduction to Predictive Modeling, Linear Regression Overview, Simple Linear Regression, Multiple Linear RegressionChapter 1
- Case study, Logistic Regression Overview, Data Partitioning, Univariate Analysis, Bivariate Analysis, Multicollinearity Analysis, Model Building, Model Validation, Model Performance Assessment AUC & ROC curves, ScorecardChapter 1
- Case Study, Introduction to SVMs, SVM History, Vectors Overview, Decision Surfaces, Linear SVMs, The Kernel Trick, Non-Linear SVMs, The Kernel SVMChapter 1
- Describe Time Series data, Format your Time Series data, List the different components of Time Series data, Discuss different kind of Time Series scenarios, Choose the model according to the Time series scenario, Implement the model for forecasting, ExplaChapter 1
- Various machine learning algorithms in Python, Apply machine learning algorithms in PythonChapter 1
- How to select the right data, Which are the best features to use, Additional feature selection techniques, A feature selection case study, Preprocessing, Preprocessing Scaling Techniques, How to preprocess your data, How to scale your data, Feature ScalinChapter 1
- Highly efficient machine learning algorithms, Bagging Decision Trees, The power of ensembles, Random Forest Ensemble technique, Boosting–Adaboost, Boosting ensemble stochastic gradient boosting, A final ensemble techniqueChapter 1
- Introduction Model Tuning, Parameter Tuning GridSearchCV, A second method to tune your algorithm, How to automate machine learning, Which ML algo should you choose, How to compare machine learning algorithms in practiceChapter 1
- Sentimental Analysis, Case studyChapter 1
- Introduction to Spark Core, Spark Architecture, Working with RDDs, Introduction to PySpark, Machine learning with PySpark–MllibChapter 1
- Case Study, Deep Learning Overview, The Brain vs Neuron, Introduction to Deep LearningChapter 1
- The Detailed ANN, The Activation Functions, How do ANNs work & learn, Which Algorithms perform best, Model selection cross validation score, TextMining& NLP, PySpark and MLLib, Deep Learning & AI using Python, Deep Learning & AI, Introduction to ArtificiaChapter 1
- Convolutional Operation, Relu Layers, What is Pooling vs Flattening, Full Connection, Softmax vs Cross Entropy, ” Building a real world convolution neural network for image classification”Chapter 1
- Recurrent neural networks RNN, LSTMs understanding LSTMs, long short term memory neural networks LSTM in pythonChapter 1
- Restricted Boltzmann Machine, Applications of RBM, Introduction to Autoencoders, Autoencoders applications, Understanding Auto encoders, Building an Auto encoder modelChapter 1
- Introducing Tensorflow, Introducing Tensorflow, Why TensorFlow?, What is TensorFlow?, TensorFlow as an Interface, TensorFlow as an environment, Tensors, Computation Graph, Installing Tensorflow, TensorFlow training, Prepare Data, Tensor typesChapter 1
- Tensors, TensorFlow data types, CPU vs GPU vs TPU, TensorFlow methods, Introduction to Neural Networks. Neural Network Architecture, Linear Regression example revisited, The Neuron, Neural Network Layers, The MNIST Dataset, Coding MNIST NNChapter 1
- Deepening the network, Images and Pixels, How humans recognize images, Convolutional Neural Networks, ConvNet Architecture, Overfitting and Regularization, Max Pooling and ReLU activations, Dropout, Strides and Zero Padding, Coding Deep ConvNets demo, DebChapter 1
- Transfer Learning Introduction, Google Inception Model, Retraining Google Inception with our own data demo, Predicting new images, Transfer Learning Summary, Extending Tensorflow, Keras, TFLearn, Keras vs TFLearn ComparisonChapter 1
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