I have Completed Full Stack Python in Appin Technology Lab. Teaching is very good and in understandable way. Staffs are very good and they easily interpret with us.
Course Details
Machine learning is captivating over the globe, and with that, there is a rising need among companies for specialized to know the ins and outs of machine learning. This Machine Learning classes will provide you with insights into the crucial task played by machine learning engineers and data scientists. After completing the course you will be able to reveal the unseen value in data using Python programming for innovative conjecture. You will learn how to develop machine learning algorithms using idea of regression, categorization, time sequence modeling and much more.
Appin’s Machine Learning foundation training will make you an specialist in machine learning, a form of artificial intelligence that computerize data analysis to enable computers to learn and get used to experience to do specific tasks without precise programming. In this course you will get real time knowledge with machine learning from a series of practical case-studies.
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ML enables you to develop , deploy and use exciting applications.
Course Features
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Why Appin?
Appin machine learning training in Coimbatore is most suited for job seekers, college students and working professionals.
With industry experts frame the syllabus. Appin provides real time projects in Machine learning.
The Training is a Flagship workshop of Appin Technology Lab. Students interested to make their career in Robotics or Embedded Technologies always join the type of training
This helps student to get placed easily in Interview. Our machine learning trainers have indepth knowledge and can taught you in much better way with real time experience.
Machine Learning Syllabus
Introduction to ML, AI
- Why we require AI and ML?
- Problem with traditional Software systems
- Opportunities with AI,ML
- What you need to excel – only a logical mind!!
- Tools and Software to efficiently build ML models
- Why R and Python(with Tensorflow) is very popular ?
Your first ML model
- Understand what actually is a ML model
- How to handle data
- Preprocessing data
- Types of ML models – Supervised and Unsupervised
- A peek into Reinforcement Learning
- How to break your data into Training and Test
- Cross validation techniques
Linear Regression
- Understand Linear Regression
- Gradient Descent
- Do actual hands on and understand the calculations behind Gradient Descent
- Brush up on Differentiation to understand the maths behind the hands-on
- Code both in R and Python
- Learn how to improve your model
Overfitting
- Overfitting is one of the most difficult aspect to learn while building a ML model
- Use the above Linear Regression model to understand Overfitting
- Learn with Hands on – how to avoid Overfitting
- Bias Variance Tradeoff
- Regularization – Ridge, LASSO
- ANOVA, F tests
- Logistic Regression
- Understand CLassification with Logistic Regression
- Maximum Likelihood Estimation
- Build an end-end model with Logistic Regression using scikit Learn
- Hands on – how actually you will build a model in the Industry
- How to code for Interviews, Data Science Competitions
Decision Trees
- What is a Probability based model and why Decision Tree is such a model? Understand the concepts of Entropy, Gini Impurity, Information Gain Do a detailed hands on project to predict the possible Loan Defaulters for a large multi-national bank
- Apply the concepts of Overfitting
- How to improve the Decision Tree model without Overfitting
- Bagging, Boosting
- Random Forest
- AdaBoost, Gradient Boost
k-NN
- Understand a Distance based model with kNN
- how to choose the value of k
- Project work on Predicting Breast Cancer
Support Vector Machines(SVM)
- The power of SVM and what it can do which other models cannot
- Why SVM is so popular in the industry
- Learn all about Kernel Functions
- Different Kernel functions
- Build an OCR(Optical Character Reader) with the help of SVM and Kernel functions
- Neural Networks
- Why Neural Networks can actually solve any Complex pattern?
- How to build the Neural Network Architecture
- How Neural Network mimics the cognitive capabilities of humans
- Build your own AND,OR,NOT,XOR,XNOR Logic Gates with Neural Network
- Understand Forward & Backward Propagation
- Plot a Neural Network with code and map your understanding between theory and practical
- Change the architecture with code and see how the Neural Network behaves
- Different Activation Functions
- Vanishing Gradient problem
- Loss functions
Deep Neural Networks
- Optimization methods
- Gradient Descent with Momentum, RMSProp, ADAM
- Learning Rate Decay
- Xavier Initialization
- Introduction to Keras and Tensorflow(TF)
- Deep Learning in Keras with TensorFlow as the backend
- Project Work
Unsupervised Learning
- Basic concepts of Clustering
- k-means Clustering
- Hierarchical clustering
- Build a hands on project to do Social Media analysis with Clustering
PCA
- Principal Component Analysis(PCA)
- Learn the maths behind PCA
- Learn how to code and plot a PCA
- Recommendation Engine
- Understand how Netflix or any other Tech giant uses Recommendation Engine
- Content and Collaborative Filtering
- Pros and Cons of different approaches of Recommendation Engine
- Market Basket Analysis
- Apriori Rules
- Build your own Recommender System
Computer Vision
- Image Detection, Image Classification, Localization
- Introduction to Convolutional Neural Networks(CNN)
- Build a Handwritten Digit recognizer with CNN
- Strides, Padding concepts
- Convolutional, Padding and Fully Connected layers
- Sliding Window
- Edge Detection
Advanced Computer Vision
- YOLO Algorithm – You Only Look Once
- Introduction to classical networks like LeNet5
- IoU
- Build an Image Classifier with CNN
- Data Augmentation Techniques
- Natural Language Processing(NLP)
- Introduction to Natural Language Processing(NLP)
- Text Preprocessing
- Lemmatization, Stemming
- Syntactical Parsing, Entity Parsing
- CTopic Modelling with Latent Dirichlet Allocation(LDA)
- Collapsed Gibbs Sampling
- Word Embedding with Word2Vec – CBOW and SkipGram models
- Restricted Boltzman Machines
- Recurrent Neural Network(RNN) and Long Short Term memory(LSTM)
- Build a chatbot with the above concepts of NLP and Neural Networks
Introduction to AI
- History of AI
- State of the Art AI
- Types of Agents
- Types of Environments
- Asymptotic Notations
Search
- Uninformed Search-Breadth first search
- Uniform Cost Search
- Depth First Search
- Depth-Limited Search
- Iterative Deepening Depth-First Search
- Bidirectional Search
- Informed Search-Greedy Best-First Search
- A* Search
- Beyond Local Search-Hill Climbing
- Simulated Annealing
- Beam Search
- Genetic Algorithms
- Online Search
- Informed Search
Adversarial Search
- Min-max Tree
- Alpha-Beta Pruning
- Move Ordering
- Stochastic games
Constraint Programming
- Constraint Satisfaction Problems
- Map coloring
- Sudoku
- Job scheduling Constraint Propagation
- Backtracking
Reinforcement learning
- Passive Reinforcement learning
- Active Reinforcement learning
- Policy Search