Note: We have both Online & Offline options for this course.

Join the AI Revolution with this easy AI ML DL Course



If you want to explore cutting-edge data science and acquire skills needed to enter the amazing world of ML, this is the right opportunity for you. With average salaries more than 11 Lakhs in India, and the promise of millions of job being created, it makes sense to enter this field ASAP.

The trainer, Nikhil specialises in making difficult concepts look simple, so that even if you are inexperienced, or tired having worked all day, or simply lazy, you will find it easy to learn. Unlike other trainers who skip complicated concepts and are too busy to explain the concepts in depth, Nikhil is loved by his students for taking care that even the weakest student understands well.

By the end of the course, you will

  •             » Develop a deep understanding of AI & ML
  •             » Become confident enough to develop AI Apps from scratch on your own
  •             » Have knowledge experience of a dozed practical AI applications
  •             » Gain real experience which counts when applying for jobs
  •             » Get certified as an AI/ML Developer
  •             » Get placements support with thousands of AI/ML jobs across multiple disciplines

Join our Machine Learning Expert AI Course at a discounted price only till November, 2022 !

Discounted price: ₹ 36000, Original price: ₹ 72000. Join by paying ₹ 2000 only and the rest in instalments.




SYLLABUS

Please note our syllabus below. Please note that while all the topics given here will be covered in full details, there are some part of our syllabus which is kept hidden and will only be revealed to those who join the course. Just like the recipe of KFC is a well protected secret, so are topics in our syllabus.


How to Learn
The Right Mindset
Developing a deep Understanding
Memorizing new concepts fast
Improving Focus & Time Management


What Is Machine Learning?
Why Use Machine Learning?
Types of Machine Learning Systems
Supervised/Unsupervised Learning
Batch and Online Learning
Instance-Based Versus Model-Based Learning
Main Challenges of Machine Learning
Insufficient Quantity of Training Data
Nonrepresentative Training Data
Poor-Quality Data
Irrelevant Features
Overfitting the Training Data
Underfitting the Training Data
Stepping Back
Testing and Validating
Hyperparameter Tuning and Model Selection
Data Mismatch


Working with Real Data
Look at the Big Picture
Frame the Problem
Select a Performance Measure
Check the Assumptions
Get the Data
Create the Workspace
Download the Data
Take a Quick Look at the Data Structure
Create a Test Set
Discover and Visualize the Data to Gain Insights
Visualizing Geographical Data
Looking for Correlations
Experimenting with Attribute Combinations
Prepare the Data for Machine Learning Algorithms
Data Cleaning
Handling Text and Categorical Attributes
Custom Transformers
Feature Scaling
Transformation Pipelines
Select and Train a Model
Training and Evaluating on the Training Set
Better Evaluation Using Cross-Validation
Fine-Tune Your Model
Grid Search
Randomized Search
Ensemble Methods
Analyze the Best Models and Their Errors
Evaluate Your System on the Test Set
Launch, Monitor, and Maintain Your System


MNIST
Training a Binary Classifier
Performance Measures
Measuring Accuracy Using Cross-Validation
Confusion Matrix
Precision and Recall
Precision/Recall Tradeoff
The ROC Curve
Multiclass Classification
Error Analysis
Multilabel Classification
Multioutput Classification


Linear Regression
The Normal Equation
Computational Complexity
Gradient Descent
Batch Gradient Descent
Stochastic Gradient Descent
Mini-batch Gradient Descent
Polynomial Regression
Learning Curves
Regularized Linear Models
Ridge Regression
Lasso Regression
Elastic Net
Early Stopping
Logistic Regression
Estimating Probabilities
Training and Cost Function
Decision Boundaries
Softmax Regression


Linear SVM Classification
Soft Margin Classification
Nonlinear SVM Classification
Polynomial Kernel
Adding Similarity Features
Gaussian RBF Kernel
Computational Complexity
SVM Regression
Under the Hood
Decision Function and Predictions
Training Objective
Quadratic Programming
The Dual Problem
Kernelized SVM
Online SVMs


Training and Visualizing a Decision Tree
Making Predictions
Estimating Class Probabilities
The CART Training Algorithm
Computational Complexity
Gini Impurity or Entropy?
Regularization Hyperparameters
Regression
Instability


Voting Classifiers
Bagging and Pasting
Bagging and Pasting in Scikit-Learn
Out-of-Bag Evaluation
Random Patches and Random Subspaces
Random Forests
Extra-Trees
Feature Importance
Boosting
AdaBoost
Gradient Boosting
Stacking


The Curse of Dimensionality
Main Approaches for Dimensionality Reduction
Projection
Manifold Learning
PCA
Preserving the Variance
Principal Components
Projecting Down to d Dimensions
Using Scikit-Learn
Explained Variance Ratio
Choosing the Right Number of Dimensions
PCA for Compression
Randomized PCA
Incremental PCA
Kernel PCA
Selecting a Kernel and Tuning Hyperparameters
LLE
Other Dimensionality Reduction Techniques


Clustering
K-Means
Limits of K-Means
Using clustering for image segmentation
Using Clustering for Preprocessing
Using Clustering for Semi-Supervised Learning
DBSCAN
Other Clustering Algorithms
Gaussian Mixtures
Anomaly Detection using Gaussian Mixtures
Selecting the Number of Clusters
Bayesian Gaussian Mixture Models
Other Anomaly Detection and Novelty Detection Algorithms


From Biological to Artificial Neurons
Biological Neurons
Logical Computations with Neurons
The Perceptron
Multi-Layer Perceptron and Backpropagation
Regression MLPs
Classification MLPs
Implementing MLPs with Keras
Installing TensorFlow 2
Building an Image Classifier Using the Sequential API
Building a Regression MLP Using the Sequential API
Building Complex Models Using the Functional API
Building Dynamic Models Using the Subclassing API
Saving and Restoring a Model
Using Callbacks
Visualization Using TensorBoard
Fine-Tuning Neural Network Hyperparameters
Number of Hidden Layers
Number of Neurons per Hidden Layer
Learning Rate, Batch Size and Other Hyperparameters


Vanishing/Exploding Gradients Problems
Glorot and He Initialization
Nonsaturating Activation Functions
Batch Normalization
Gradient Clipping
Reusing Pretrained Layers
Transfer Learning With Keras
Unsupervised Pretraining
Pretraining on an Auxiliary Task
Faster Optimizers
Momentum Optimization
Nesterov Accelerated Gradient
AdaGrad
RMSProp
Adam and Nadam Optimization
Learning Rate Scheduling
Avoiding Overfitting Through Regularization
ℓ1 and ℓ2 Regularization
Dropout
Monte-Carlo (MC) Dropout
Max-Norm Regularization
Summary and Practical Guidelines


A Quick Tour of TensorFlow
Using TensorFlow like NumPy
Tensors and Operations
Tensors and NumPy
Type Conversions
Variables
Other Data Structures
Customizing Models and Training Algorithms
Custom Loss Functions
Saving and Loading Models That Contain Custom Components
Custom Activation Functions, Initializers, Regularizers, and Constraints
Custom Metrics
Custom Layers
Custom Models
Losses and Metrics Based on Model Internals
Computing Gradients Using Autodiff
Custom Training Loops
TensorFlow Functions and Graphs
Autograph and Tracing
TF Function Rules


The Data API
Chaining Transformations
Shuffling the Data
Preprocessing the Data
Putting Everything Together
Prefetching
Using the Dataset With tf.keras
The TFRecord Format
Compressed TFRecord Files
A Brief Introduction to Protocol Buffers
TensorFlow Protobufs
Loading and Parsing Examples
Handling Lists of Lists Using the SequenceExample Protobuf
The Features API
Categorical Features
Crossed Categorical Features
Encoding Categorical Features Using One-Hot Vectors
Encoding Categorical Features Using Embeddings
Using Feature Columns for Parsing
Using Feature Columns in Your Models
TF Transform
The TensorFlow Datasets (TFDS) Project


The Architecture of the Visual Cortex
Convolutional Layer
Filters
Stacking Multiple Feature Maps
TensorFlow Implementation
Memory Requirements
Pooling Layer
TensorFlow Implementation
CNN Architectures
LeNet-5
AlexNet
GoogLeNet
VGGNet
ResNet
Xception
SENet
Implementing a ResNet-34 CNN Using Keras
Using Pretrained Models From Keras
Pretrained Models for Transfer Learning
Classification and Localization
Object Detection
Fully Convolutional Networks (FCNs)
You Only Look Once (YOLO)
Semantic Segmentation


Predict Housing Price
Stock Price Prediction
Online Assignment Plagiarism Checker
Personality Prediction System via CV Analysis
Breast Cancer Detection
Undersea Sonar Bomb Detection
Handwriting Recognition
Face recognition
Chatbot
YouTube Comment Spam Detection
11. Face Filter


Secrets of Success
SUpercharged CVs
Technical & HR Interview Preperation
Speaking & giving Presentations on AI
Freelancing / Entrepreneurship



Fees

Our actual course price is ₹ 72000.
For this month, the discount price is
₹ 36000

Duration

The total duration is about 3 Months, With classes taken: 5 Days a week

Certification

You will receive our Certificate, which is internationally accepted & honored by MNCs. Live projects add to your experience as well.

Real Experiences

Watch a feedback from just one of our batches. There are hundreds of videos like these.

Ready to change your life?

Our Machine Learning Expert AI Course is the best course!

Join Now by just paying Rs. 2000 and Change your life forever.

Not just a Course

How is FreshersIndia able to provide written Job guarantee in Court paper, notarized and completely legal when no other institute in India can do this? What is the secret? Carefully watch the video below and you will understand.

How we are different

Beware! For many organizations, Education is purly a profit optimization business and they make money by selling dreams which never come true.

We ensure that our courses cover all the basics in depth, teach advanced concepts well that YOU are able to perform, teach you real life projects and also prepare you not only for the interview but also to become a successful professional.

We give 100% Effort for Every individual Student. Everyone is treated as an equal.
100%
Easy! We teach advanced topics in simplified way for average and even slow minds.
100%
Our courses cover 100% of the important and basic fundamental concepts. No Skimming!
100%
Our Courses are 100% results oriented. We believe that If YOU don't learn, then WE fail.
100%
Our courses are 100% Updated - Latest Versions balanced with evergreen basics.
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Video Testimonials

Did you know, many institutes pick up stock images from Google search, and show completely fabricated feedback. We therefore use videos, the truth.


Got any career related problems? Any queries? Call us on phone: 8620007775


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 Our trainers have trained in several IITs and  MNCs different technologies.