Posts in category: SYLLABUS.

WHAT EVERY MACHINE LEARNING COURSE MUST CONTAIN

THE HUGE DEMAND OF MACHINE LEARNING ENGINEERS HAS CAUSED AN UPSURGE IN STUDENTS TAKING UP ONLINE COURSES FOR THE SAME AND ALSO WANTING TO KNOW WHAT TOPICS AN IDEAL MACHINE LEARNING COURSE MUST COVER . SINCE MANY STUDENTS ARE TAKING UP SUCH COURSES WHAT WILL MAKE YOU DIFFERENT? THE ANSWER IS KNOWING THE MATH BEHIND THE MODELS . KNOWING THE ALGORITHM FROM ITS ROOT LEVEL WILL ALLOW YOU TO MAKE BETTER MODELS , USE ENSEMBLE TECHNIQUES AND SHOWCASE YOUR SKILL IN YOUR PROJECTS . OTHER WISE ITS A MERE USING OF A PREDEFINED LIBRARY WHICH ANY 8TH GRADER WITH LITTLE KNOWLEDGE OF PYTHON CAN ALSO DO . HERE WE DISCUSS AN IDEAL MACHINE LEARNING COURSE SYLLABUS STRUCTURE.

YOU CAN USE THIS TO COMPARE THE COURSES , OR IF ALREADY ENROLLED YOU CAN SEE WHETHER THEY ARE PROVIDING YOU WITH EVRYTHING YOU REQUIRE .

LETS BEGIN!!!!

INTRODUCTION TO MACHINE LEARNING

1. WHAT IS “LEARNING ” , MATHEMATICAL MEANING OF “LEARNING”
2. THE NEED OF MACHINE LEARNING
3. THE LIMITATIONS OF MACHINE LEARNING
4. THE PREREQUISITES OF GETTING INTO MACHINE LEARNING COURSE. PROGRAMMIMG LANGUAGES , LIBRARIES AND MATHEMATICS .
5. APPLICATIONS IN REAL LIFE AND RELATION WITH OTHER FIELDS .
6. TIME COMPLEXITIES OF VARIOUS ML ALGORITHMS.

STATISTICS

1. DISTRIBUTIONS
2. GAUSSIAN DIRSTRIBUTION , POWER LAW DISTRIBUTIONS , LOG NORMAL DISTRIBUTIONS ETC.
3. THE TRANSFORMATION TECINIQUES LIKE BOX COX TRANSFORMS ,
4. PDF, CDF AND THEIR PROPERTIES
5. MEAN , VARIANCE , SKEWNESS, KURTOSIS , MOMENTS AROUND MEAN
6. DIFFERENCE IN 2 DISTRIBUTIONS , KS TEST
7. QQ PLOTS , VIOLIN PLOTS, BOX PLOTS, WHISKER PLOTS, PAIR PLOTS
8. COVARIANCE , VARIANCE , CAUSE
9. PEARSON CORRELATION COEFICIENT, SPEARMAN CORRELATION COEFFICIENT.

TYPES OF MACHINE LEARNING

1. TYPES OF MACHINE LEARNING :SUPERVISED , UNSUPERVISED AND REINFORCEMENT
2. HOW TO IDENTIFY THE TYPE OF LEARNING ACCORDING TO PROBLEM STATEMENT
3. WHAT ARE MACHINE LEARNING MODELS , WHERE DO WE TRAIN THEM ,WHAT DO WE TRAIN THEM ON .
4. WHERE DO WE GET THE DATA SETS FROM.

TERMS RELATED TO MACHINE LEARNING MODELS

1. DATA SETS , NUMERIC AND STRING DATA
2. TRAINING AND TESTING DATA
3. FEATURE ENGINEERING , ONE HOT ENCODING
4. ERRORS ASSOCIATED WITH TRAINING A MODEL : TRAINING ERROR /TEST ERROR /VALIDATION ERROR /K FOLDS CROSS VALIDATION
5. TERMS LIKE ERROR SURFACES , GRADIENTS , VECTORS AMD MATRICES , EIGEN VECTORS
6. FITTING A MODEL , PREDICTION BY A MODEL , LOSS FUNCTIONS , ACCURACY ,PRECISION , RECALL
7. BIAS AND VARIANCE OF A MODEL ,ROC , AUC
8. MODEL COMPLEXITY AND ITS RELATION TO THE TEST ERROR
9. OVERFITTING AND UNDERFITTING MODELS
10. LINEAR AND NON LINEAR PROBLEMS , ARE ALL DATASETS LEARNABLE ? (TRY PROVING IT MATHEMATICALLY )
11. DIFFERENCE IN CLASSIFICATION AND REGRESSION PROBLEMS ,TERMS RELATED TO THEM .
12. ENSEMBLES , WEAK LEARNERS , BOOSTING
13. BENEFIT OF USING ENSEMBLE MODELS .

MATRIX , PCA ,SVD

1. WHAT IS A MATRIX
2. COVARIANCE MATRIX
3. WHAT IS SVD(SINGULAR VALUE DECOMPOSITION)
4. PCA (PRINCIPAL COMPONENT ANALYSIS) ,ITS USES ,
5. T-SNE , LIMITATIONS OF TSNE , PCA
6. RECOMMENDATION SYSTEMS, SPARSE MATRICES AND COLD START PROBLEM
7. NETFLIX PRIZE PROBLEM( FAMOUS CASE STUDY)

LINEARLY SEPARABLE PROBLEMS

1. LINEARLY SEPARABLE DATASETS
2. VISUALISING DATA IN 2 /3 DIMENSIONS . INTUITION BEHIND N DIMENSIONS.
3. PERCEPTRON LEARNING , PERCEPTRON MATHEMATICS
4. MATHEMATICALLY SHOWING CONVERGENCE OF SUCH PROBLEMS .
5. LINEAR REGRESSION ,POLYNOMIAL REGRESSION .
6. HOW SVMS, KERNEL SVMS HELP TO SOLVE NON-LINEAR PROBLEMS.

LINEAR REGRESSION /MULTIPLE REGRESSSION

1. CONCEPT OF LINEAR REGRESSION
2. CONCEPT OF LEAST SQUARES , LOSS FUNCTION ,RMS , RMSLE, MSE
3. FINDING THE BEST FIT LINE
4. WHAT ARE REGULARISATION TECHNIIQUES , AVOIDING OVERFITTING .
5. LASSO , RIDGE REGRESSION ,ELASTINET :MATHEMATICS BEHIND THEM , REGULARISATION IN MULTIPLE REGRESSION (DON’T SKIP THE MATH)
6. LIMITS OF LINEAR REGRESSION

LOGISTIC REGRESSION

1. WHAT IS LOGISTIC REGRESSION
2. DIFFERENCE FROM LINEAR REGRESSION
3. PROBABILITY DISTRIBUTION , USE OF SIGMOID FUNCTION , ADVANTAGES OF USING PROBABILITY OVER BOOLEAN OUTPUTS.
4. MATH BEHIND LOGISTIC REGRESSION .(TRY GOING AS DEEP AS YOU CAN, THERE IS ALWAYS MORE TO IT THAN YOU KNOW!!!!)
5. LOSS FUNCTION USED FOR LINEAR REGRESSION . TERMS LIKE SOFTMAX , LOG FUNCTION
6. LIMITATIONS OF LOGISTIC REGRESSION .

SUPPORT VECTOR MACHINES ,KERNELS

1. WHAT ARE SUPPORT VECTOR MACHINES
2. WHAT IS MARGIN AND HARD SVM
3. SOFT AND HARD SVMS
4. NORM REGULARISATION
5. LIMITATIONS OF SUPPORT VECTOR MACHINES
6. WHAT ARE KERNELS ,WHERE ARE KERNELS USED
7. KERNELISATION TRICK , MERCER’S THEOREM
8. PROPERTIES OF KERNEL MATRIX
9. IMPLEMENTING SOFT SVM WITH KERNELS , IMPLEMENTING SOFT SVM WITH STOCHASTIC GRADIENT DESCENT .

DECISION TREES

1. WHAT ARE DECISION TREES
2. CLASSIFICATION PROBLEMS , CONFUSION MATRIX OF A CLASSIFICATION PROBLEM
3. EXAMPLES IN DAILY LIFE
4. MATHEMATICAL TERMS LIKE INFORMATION , INFORMATION GAIN , ENTROPY , LOG FUNCTION
5. DECISION TREE ALGORITHMS
6. HOW NODES ARE SPLIT IN DECISION TREES
7. RANDOM FOREST ALGORITHM
8. ISOLATION FOREST ALGORITHM

CLUSTERING ,K NEAREST NEIGHBOUR , K MEANS CLUSTERING

1. CONCEPT OF DISTANCE
2. TYPES OF DISTANCES ” EUCLIDEAN , MINKOWSKI , MANHATTAN DISTANCE , HAMMIMG DISTANCE .(MATHEMATICAL CONCEPT/FORMULA ).
3. TERMS LIKE “NEIGHBOURS” ,”CENTROID” , “OUTLIERS”
4. DIFFERENCE BETWEEN K MEANS AND K NEAREST NEIGHBOURS
5. WHAT IS CLUSTERING ?
6. K MEANS CLUSTERING ALGORITHM
7. HOW TO DECIDE VALUE OF K : ELBOW METHOD
8. HOW K MEANS ALGORITHM IS IMPLEMENTED , MATHS BEHIND IT .
9. K NEAREST NEIGHBOUR
10. MATH BEHIND IT.

NLP

1. NEED OF NLP
2. STEMMING, TOKENISATION, STOP WORD REMOVAL AND OTHER PREPROCCESING OF WORDS
3. ENCODING WORDS AS NUMBER
4. NLP TECHNIQUES LIKE NAIVE BAYES, DEEP LEARNING

NEURAL NETWORKS /DEEP LEARNING

1. BIOLOGICAL NEURON
2. PERCEPTRON MODEL ,MATHEMATICS INVOLVED ,
3. SIGMOID NEURON , ACTIVATION FUNCTIONS , PROBABILITY DISTRIBUTIONS , CONCEPT OF PATIAL DERIVATIVES
4. WHEN DO NEURAL NETWORKS OVERSHADOW TRADITIONAL MACHINE LEARNING MODELS .
5. TERMS LIKE NEURONS ,ANN , NEURAL NETWORKS ,DEEP LEARNING , HIDDEN LAYERS , INPUT LAYER , OUTPUT LAYER.
6. WEIGHTS , BIASES ,LOSS FUNCTIONS
7. WEIGHT INITIALIZATION TECHNIQUES. (LIKE XAVIER INITIALISATION)
8. FEEDFORWARD NETWORKS , BACKPROPAGATION , STOCHASTIC GRADIENT DESCENT
9. INTRODUCTION TO TYPES OF NEURAL NETWORKS : ANN ,CNN , RNN ,LSTMS ,GRUs
10. DEEP LEARNING AND AI , REAL LIFE APPLICATIONS ,CURRENT TECHNOLOGY
11. TRANSFER LEARNING
12. IMAGE SEGMENTATION
13. NLP (W2V , SKIPGRAM , CBOW, WORD EMBEDDINGS, GLOVE VECTORS)
14. ENCODER DECODERS
15. AUTO ENCODERS
16. ATTENTION MODELS
17. TRANSFORMERS
18. BERT
19. GPTS

NAIVE BAYES CLASSIFIERS

1. CONDITIONAL PROBABILITY
2. PROBABILITY DISTRIBUTION
3. NAIVE BAYES ALGORITHM
4. NAIVE BAYES ON CONTINUOUS FEATURES , (GAUSSIAN NAIVE BAYES)
5. LIMITATIONS

REINFORCEMENT LEARNING

1. WHAT IS REINFORCEMENT LEARNING?
2. CONCEPT OF REWARDS AND PENALTIES , GREEDY ALGORITHMS
3. ALGORITHMS USED IN REINFORCEMENT LEARNING , EG: MONTE CARLO AND MATHS BEHIND THEM .
4. REINFORCEMENT LEARNING FOR GAMES .
5. GENETIC ALGORITHMS

ABOVE YOU SAW A DETAILED DESCRIPTION OF WHAT A GOOD MACHINE LEARNING COURSE MUST CONTAIN . TRY TO UNDERSTAND THEE MATH BEHIND EVERY MODEL . WE WILL BE UPLOADING ARTICLES ON ALL THE TOPICS , THE NEURAL NETWORK SECTION WILL BE DEALT IN THE DEEP LEARNING /NEURAL -NETWORKS SECTION OF 7-HIDDEN LAYERS . YOU WILL FIND ARTICLES ON DEEP LEARNING /REINFORCEMENT LEARNING USING NEURAL NETWORKS IN AI UPDATES SECTION TOO .

HAPPY LEARNING!!!!!